NON-LINEAR ANALYSIS OF VISUAL EVOKED

POTENTIALS:

APPLICATIONS FOR UNDERSTANDING THE ROLE OF THE

AFFERENT STREAMS IN VISUAL PROCESSING

Laila Elaine Hugrass

2018

A thesis submitted in total fulfilment of the

requirements for the degree of Doctor of Philosophy

i

Abstract The receptive field properties of neurons in the magnocellular (M), parvocellular

(P) and koniocellular (K) afferent pathways have been well studied in primates. Due to

interactions between these pathways at the cortical level, it is difficult to study them

with non-invasive methods in humans. M and P signatures have been identified in the

non-linear temporal structure of visual evoked potentials (VEPs). This has opened the

door to investigating contributions from these afferent pathways to various aspects of

visual processing.

This thesis uses non-linear VEPs, in combination with conventional VEPs and

psychophysics to identify putative M and P responses. The principle aim is to extend

the use of non-linear VEP analysis by applying these techniques to investigate how the

afferent streams contribute to different aspects of visual processing. Specifically, this

thesis reviews the use of non-linear VEPs, conventional VEP techniques and

psychophysics to study M and P processing (Chapters 2 and 3). It applies EEG and

MEG measures of non-linear VEPs to investigate variations in M and P responses to red

and green background (Chapter 4) and to blue chromatic saturation (Chapters 5).

Conventional VEP (Chapter 6) and non-linear VEP (Chapter 7) were applied to

investigate the ways in which the neuropeptide, oxytocin, influences early visual

processing of affective and non-affective input.

By combining non-linear VEP and psychophysical evidence, I challenged the

long held belief that red surrounds specifically suppress M contributions to cortical

processing and perception (Chapter 4). Furthermore, by studying non-linear MEG

responses, I identified an early cortical signature of chromatic saturation that appears to

originate from a population of neurons that recover rapidly from stimulation (Chapter

5). Finally, I found that oxytocin administration influences very fast latency,

presumably M-driven, responses to emotional faces (Chapter 6), yet it does not

influence M or P driven VEP responses to non-affective multifocal flash stimulation

(Chapter 7).

These findings highlight the importance of using a variety of complementary

techniques to investigate the earliest stages of visual processing. By studying the non-

linear temporal structure of VEPs, I was able to resolve some ambiguities in the

ii

literature. This work is discussed in terms of its implications, and future directions for

studying M and P afferent contributions to vision.

iii

Acknowledgements I would like to express my sincere gratitude to my supervisor, Professor David

Crewther. I appreciate the support and encouragement you have provided along the

way. Your enthusiasm and knowledge have been so important in my development as a

researcher. I would also like to thank Professor Sheila Crewther for her guidance and

support. I am very grateful to Doctor Izelle Labuschagne for her input regarding the

design of the oxytocin studies and for her advice in preparing the related manuscripts.

It would not have been possible to complete this thesis without the people who

participated in my research. Thank you all for being so generous with your time. I am

very fortunate to have been a member of David Crewther’s Visneuro laboratory group.

Over the years, it has been an absolute pleasure to welcome undergraduate student

members to the Visneuro team. I am very grateful for the assistance that each of you

have provided. Our weekly Visneuro meetings have been very valuable in providing a

platform for us to discuss our work. In particular, I would like to thank my colleagues,

Dr Talitha Ford, Dr. Nicola Jastrebski, Dr. James Collett, Alyse Brown, Brook

Shiferaw, Eveline Mu, Adelaide Burt and Katie Wykes for all of their wisdom and

encouragement.

I would like to thank my incredible friends and family for being my support

network throughout this journey. I am so grateful for my Armidale friends, my

Tasmanian ladies, and my Melbourne champions. Lastly, I would like to give a special

thanks to my partner Chris Ormston for believing in me and for helping me to see the

funny side of absolutely everything.

iv

General Declaration

I, the candidate, declare that the contents of this thesis:

1. Contains no material which has been accepted by me for the award of any

other degree at any other university or equivalent institution.

2. To the best of my knowledge, contains no material previously published or

written by another person except where appropriate reference is made in the

thesis.

3. Discloses the relative contributions of the authors on work that is based on

joint research or publications (see Appendix I).

4. I warrant that I have obtained, where necessary, permission from the

copyright owners to use any third party copyright material reproduced in the

thesis (such as artwork, images, unpublished documents), or to use any of

my own published work (such as journal articles) in which the copyright is

held by another party (such as publisher, co-author).

v

Table of Contents

Abstract ............................................................................................................................. i

Acknowledgements ........................................................................................................ iii

General Declaration ....................................................................................................... iv

Table of Contents ............................................................................................................ v

List of Figures ................................................................................................................. xi

List of Tables ............................................................................................................... xvii

List of Papers as Part of this Thesis ........................................................................ xviii

List of Additional Papers that do not Form a Part of this Thesis ............................ xix

List of Presentations ...................................................................................................... xx

List of Abbreviations .................................................................................................... xxi

Chapter 1: Introduction and Thesis Overview .......................................................... 1

1.1 Overviewafferentpathways.............................................................................11.2 Thenon-linearVEPapproachtostudyingtheafferentstreams...........41.3 Outlineofthesischaptersandaims................................................................61.4 References...............................................................................................................9

Chapter 2: The Afferent Pathway Origins of Scalp Recorded Visual Evoked

Potentials - A Review .................................................................................................... 14

2.1 Chapterguide......................................................................................................142.2 Abstract.................................................................................................................152.3 Introduction.........................................................................................................162.4 Primatestudiesoftheafferentvisualpathways.....................................162.5 HumanVEPanalysesoftheafferentvisualpathways...........................172.6 ThetransientVEPapproach...........................................................................192.6.1 IntroductiontotransientVEPs............................................................................192.6.2 EffectsofluminancecontrastandspatialfrequencyonVEPs...............192.6.3 EffectsofcolourontransientVEPs....................................................................212.6.4 EffectsofmotionontransientVEPs..................................................................222.6.5 SummaryofafferentcontributionstotransientVEPs..............................23

2.7 ThessVEPapproach..........................................................................................232.7.1 IntroductiontossVEP..............................................................................................23

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2.7.2 EffectsofluminancecontrastandspatialfrequencyonssVEP.............242.7.3 EffectsofcolouronssVEP......................................................................................252.7.4 EffectsofmotiononssVEP....................................................................................262.7.5 SummaryofafferentcontributionstossVEP................................................26

2.8 Them-sequenceapproach..............................................................................272.8.1 Introductiontom-sequencesandWienerkernelanalysis......................272.8.2 Effectsofluminancecontrastonnon-linearVEPs......................................282.8.3 Effectsofspatialfrequencyonnon-linearVEPs...........................................292.8.4 Effectsofcolouronnon-linearVEPs.................................................................302.8.5 Corticalsourcesofnon-linearVEPsignal.......................................................312.8.6 Summaryofafferentcontributionstonon-linearVEPsignals..............31

2.9 TheVESPAapproach.........................................................................................322.9.1 IntroductiontoVESPA.............................................................................................322.9.2 TheeffectsofluminanceandspatialfrequencyonVESPAsignals......322.9.3 SummaryofafferentcontributionstoVESPAsignal..................................33

2.10 DiscussionandConclusions.........................................................................332.11 References..........................................................................................................35

Chapter 3: A review of Non-Linear Visual Evoked Potential Research into

Contributions from the Human M and P pathways to Cortical Vision ................... 443.1 Chapterguide.......................................................................................................443.2 Abstract..................................................................................................................453.3 Introduction.........................................................................................................463.4 Primatephysiology:CharacteristicsoftheMandPpathways...........463.5 Thenon-linearVEPapproach........................................................................483.5.1 Introductiontonon-lineartemporalanalysisofVEPs..............................483.5.2 ThemultifocalVEPapproachtoidentifyingMandPinputstocortical

vision 493.6 Applications.........................................................................................................533.6.1 DevelopmentalchangesinMandPfunction.................................................533.6.2 MandPfunctionindevelopmentaldisorders..............................................543.6.2.1 TheAutismSpectrum.....................................................................................................543.6.2.2 Dyslexiaandmathematicalimpairment................................................................56

3.6.3 PsychopharmacologyandnonlinearVEP:EffectsofOmega-3PUFA

diets 573.6.4 Colourprocessing......................................................................................................58

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3.7 Summaryandfuturedirections....................................................................593.8 Conclusions..........................................................................................................613.9 References............................................................................................................61

Chapter 4: The Effects of Red Surrounds on Visual Magnocellular and

Parvocellular Cortical Processing and Perception ..................................................... 71

4.1 Chapterguide......................................................................................................714.1.1 Highlights......................................................................................................................71

4.2 Abstract.................................................................................................................724.3 Introduction.........................................................................................................734.4 Experiment1:Method......................................................................................764.4.1 Participants..................................................................................................................764.4.2 Stimuli.............................................................................................................................764.4.3 EEGrecordingandanalysis...................................................................................77

4.5 Experiment1:ResultsandDiscussion........................................................784.5.1 K1Amplitude...............................................................................................................784.5.2 K2.1Amplitude...........................................................................................................804.5.3 K2.2Amplitude...........................................................................................................804.5.4 Summary.......................................................................................................................81

4.6 Experiment2:Method......................................................................................814.6.1 Participants..................................................................................................................814.6.2 Stimuli.............................................................................................................................82

4.7 Experiment2:ResultsandDiscussion........................................................834.7.1 Greysteadypedestaltasks....................................................................................864.7.2 Greypulsedpedestaltasks....................................................................................864.7.3 Colouredsteadypedestaltasks...........................................................................874.7.4 Colouredpulsedpedestaltasks...........................................................................874.7.5 Summary.......................................................................................................................88

4.8 Generaldiscussion.............................................................................................894.9 Acknowledgements...........................................................................................924.10 References..........................................................................................................92

Chapter 5: The Temporal Structure of Evoked MEG Responses: Effects of

Chromatic Saturation ................................................................................................... 985.1 Chapterguide......................................................................................................985.1.1 Highlights......................................................................................................................98

5.2 Abstract...............................................................................................................100

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5.3 Introduction.......................................................................................................1015.4 Methods...............................................................................................................1035.4.1 Participants...............................................................................................................1035.4.2 Stimuli..........................................................................................................................1035.4.3 MEGrecording..........................................................................................................1045.4.4 StructuralT1.............................................................................................................1055.4.5 MEGanalyses............................................................................................................1055.4.6 MEGstatisticalanalyses.......................................................................................106

5.5 Results..................................................................................................................1075.5.1 Sensorspaceanalyses...........................................................................................1075.5.2 Sourcelocalisation.................................................................................................1095.5.3 PLSanalyses..............................................................................................................111

5.6 Discussion...........................................................................................................1135.7 Conclusions.........................................................................................................1155.8 Acknowledgements.........................................................................................1155.9 References..........................................................................................................115

Chapter 6: Intranasal Oxytocin Modulates Very Early Visual Processing of

Emotional Faces .......................................................................................................... 1206.1 Chapterguide.....................................................................................................1206.1.1 Highlights...................................................................................................................120

6.2 Abstract................................................................................................................1216.3 Introduction.......................................................................................................1226.4 Methods...............................................................................................................1246.4.1 Participants...............................................................................................................1246.4.2 Questionnaires.........................................................................................................1256.4.3 FacialemotionVEPtask.......................................................................................1256.4.4 Procedure...................................................................................................................1266.4.5 EEGrecordingandpre-processing.................................................................1266.4.6 StatisticalAnalyses.................................................................................................127

6.5 Results..................................................................................................................1296.5.1 Preliminaryanalysesofbehaviouraldata....................................................1296.5.2 Mainanalysesofbehaviouraldata..................................................................1296.5.3 PreliminaryanalysesofVEPs............................................................................1306.5.4 Earlyeffects(40-60ms)........................................................................................1326.5.5 P100..............................................................................................................................133

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6.5.6 N170andVPP...........................................................................................................1356.5.7 LPPresults.................................................................................................................137

6.6 Discussion...........................................................................................................1386.7 Conclusions........................................................................................................1416.8 Acknowledgements.........................................................................................1416.9 References..........................................................................................................141

Chapter 7: Acute Intranasal Oxytocin does not Influence the Non-Linear

Temporal Structure of Cortical Visual Evoked Potentials ..................................... 1487.1 Chapterguide....................................................................................................1487.1.1 Highlights...................................................................................................................148

7.2 Abstract...............................................................................................................1497.3 Introduction.......................................................................................................1507.4 Method.................................................................................................................1527.4.1 Participants...............................................................................................................1527.4.2 Procedure...................................................................................................................1527.4.3 Questionnaires.........................................................................................................1537.4.4 mfVEPStimuli...........................................................................................................1537.4.5 EEGrecordingandpre-processing.................................................................1547.4.6 StatisticalAnalyses.................................................................................................155

7.5 Results..................................................................................................................1567.5.1 StateanxietypreliminaryLMM........................................................................1567.5.2 StateanxietyLMM..................................................................................................1567.5.3 mfVEPpreliminaryLMMs...................................................................................1577.5.4 K1N65P105LMM...........................................................................................................1577.5.5 K2.1N70P105...................................................................................................................1597.5.6 EarlyandlateK2.2waveforms.........................................................................161

7.6 Discussion...........................................................................................................1627.7 Acknowledgements.........................................................................................1647.8 References..........................................................................................................165

Chapter 8: General Discussion ............................................................................... 1708.1 Understandingtheeffectsofredsurroundsonvisualprocessing..1708.1.1Overviewoforiginalcontributions.....................................................................1718.1.2Implications...................................................................................................................172

8.2 Understandingtheeffectsofdiffusechromaticsaturation...............1738.2.1Overviewoforiginalcontributions.....................................................................174

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8.2.2Implications...................................................................................................................1758.3 Understandingoftheeffectsofoxytocinonearlyvisualprocessing

1768.3.1Overviewoforiginalcontributions.....................................................................1768.3.2Implications...................................................................................................................177

8.4 Limitationsandfuturedirections...............................................................1788.4.1Relatingprimatephysiologytohumanbehaviour.......................................1798.4.2SeparationofKsignalsfromMandPsignals.................................................1808.4.3Updatingthethree-pathwayhypothesis..........................................................1808.4.4OtherM-drivensubcorticalvisualpathways..................................................1808.4.5Individualdifferences...............................................................................................181

8.5 Conclusions.........................................................................................................1818.6 References..........................................................................................................182

Appendix A: Certificates of ethics approval ............................................................. 189A.1SUHRECProject2015/064:TransformationsinVisualCortex:From

neuralinputtorecognition....................................................................................................189A.2SUHRECProject2017/027:TransformationsinVisualCortex:From

neuralinputtorecognition....................................................................................................191

Appendix B: Authorship Indication Forms .............................................................. 193B.1AuthorshipindicationforthepaperpresentedinChapter2...............193B.2AuthorshipindicationforthepaperpresentedinChapter3...............194B.3AuthorshipindicationforthepaperpresentedinChapter4...............195B.4AuthorshipindicationforthepaperpresentedinChapter5...............197B.5AuthorshipindicationforthepaperpresentedinChapter6...............198B.6AuthorshipindicationforthepaperpresentedinChapter7...............199

Appendix C: Summary of Journals in which paper are/are to be published ........ 200PapersInPress.............................................................................................................200PapersInSubmission................................................................................................201PapersInPreparation...............................................................................................201

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List of Figures Figure 2.1 Illustration of different VEP stimulation techniques. The top panels illustrate

transient VEP at 1Hz, for diffuse (a) and pattern onset (b) and (c) pattern reversal

stimulation modes. The bottom panels provide examples of contrast modulation

sequences during d) steady state VEP at 4 Hz, (e) fast m-sequence VEP, where the

contrast alternates pseudorandomly between binary levels, at a base interval of

16.7ms, and (f) VESPA, where the contrast is modulated randomly over a wide

range of narrowly spaced levels, at a base interval of 16.7ms. ............................... 19

Figure 3.1 Illustration of typical (a) K1, (b) K2.1 and (c) K2.2 waveforms from a single

observer, as measured from the central patch of a multifocal VEP stimulus, at 70%

luminance contrast, and a display update rate of 60Hz. .......................................... 49

Figure 4.1 Dartboard stimulus configuration for the green low contrast (a), green high

contrast (b), red low contrast (c) and red high contrast (d) conditions. We

compared VEP kernel responses to the central patch for the conditions with red and

green surrounds. ...................................................................................................... 77

Figure 4.2 K1, K2.1 and K2.2 responses to the central patch at 10% (a, b, c) and at 70%

(d, e, f) temporal contrast. The bold red and green lines correspond to the averaged

waveforms for the conditions with red and green backgrounds respectively.

Responses from each participant are illustrated in the faint red and green traces.

VEP amplitudes for the red and green surrounds were compared using running

paired samples t-tests (df = 14). The absolute t-values are shown in the black

traces at the bottom of each panel, with the dashed and dotted horizontal lines

signifying the p < .05 and p < .01 two-tailed significance thresholds respectively.

Times when the VEP traces differed significantly are flagged with * (p < .05) and

** (p< .01). .............................................................................................................. 79

Figure 4.3 Illustration of the steady (a and c) and pulsed (b and d) pedestal paradigms

on green and red backgrounds. An additional experiment was performed with

pedestals and targets that were all red (e) or all green (f). In the steady paradigms,

observers adapted to the pedestals for 3 seconds prior to a 30ms test stimulus. They

were required to identify the location of the luminance increment (the top square in

this case). The pulsed paradigms were the same except observers adapted to the

background, rather than to the pedestals. ................................................................ 83

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Figure 4.4 Plots of mean log luminance increment thresholds versus log pedestal

luminance for the centrally (a) and peripherally (b) presented grey pedestal tasks

(N = 9) and for the centrally (c) and peripherally (d) presented coloured pedestal

tasks (N=8). Results for the red and green background conditions are shown in the

red and green traces respectively. Results for the steady pedestal task are shown in

the filled markers (solid lines), whereas results for the pulsed pedestal task are

shown in the unfilled markers (dashed lines). For the coloured pedestal tasks, the

yellow markers are the average of thresholds obtained in the red and green steady

pedestal conditions, and the yellow solid line illustrates the linear fit. The

backgrounds have been shaded in dark and light greys to show when the pedestals

were decrements and increments relative to the background luminance. The error

bars denote ± 1 SEM. .............................................................................................. 85

Figure 5.1 Illustration of the chromatic saturation levels for the blue multifocal stimuli.

The central quadrants of the multifocal stimuli are illustrated in the panels on the

right, at levels of 0, 25, 50, 75 and 95% blue saturation. For each chromatic

saturation condition, temporal luminance contrast = 30% .................................... 104

Figure 5.2 Sensor space analyses. Mean magnetic evoked K1 (a), K2.1 (b) and K2.2 (c)

waveforms are shown for an occipital sensor cluster (principle component

derived). The shading represents the blue chromatic desaturation series from 95%

to 0%. Mean amplitudes and latencies for the first major troughs and peaks are

presented for the K1 (d-e), K2.1 (f-g) and K2.2 (h-i) waveforms, with chromatic

saturation plotted on the x-axes. Responses to the central multifocal quadrants are

presented in different coloured traces (red: top right, orange: top left, green: bottom

left, purple: bottom right). The error bars denote ± SEM. .................................... 108

Figure 5.3 Group averaged (n = 8) maps of z-score normalised MNI sources for the

95% blue saturation conditions. The separate rows illustrate the K2.1 response at

70ms (a) and 95ms (b), and the K2.2 response at 70ms (c) and 95ms (d). For each

column of cortical maps, the corresponding stimulus quadrants are illustrated in the

blue segments at the top of the panel (top right, top left, bottom right and bottom

left). The same colour bar range was applied for all maps, displaying z-scores

ranging from 0 (dark red) to >10 (white). Due to much stronger K2.1 activations,

the thresholds were set to z > 6 and z > 3 for the K2.1 and K2.2 maps respectively.

............................................................................................................................... 110

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Figure 5.4 Group averaged (n = 8) time courses of K1 (a), K2.1 (b) and K2.2 (c)

responses to the top-right stimulus quadrant, as measured from a corresponding V1

scout (region of interest). The shading represents the blue chromatic desaturation

series from 95% to 0% .......................................................................................... 111

Figure 5.5 Partial least squares analyses. Maps of the bootstrap ratios for the K1 (a)

K2.1 (b) and K2.2 (c) responses, at latencies of 95ms, 70ms and 70ms respectively.

The same colour bar range was applied for all maps, to illustrate bootstrap ratios

from -10 (red = strong negative correlation with the latent variable) to 10 (blue =

strong positive correlation with the latent variable). The thresholds were set so that

only the reliable sources were mapped (Bs. ratio > 2.58). For panels a-c, the top

left, top right, bottom left and bottom right cortical maps reflect responses to the

corresponding stimulus quadrants. Panels d (K1), f (K2.1) and h (K2.2) are plots of

the latent variables vs. saturation, with different coloured traces for each quadrant

(red = top right, orange = top left, green = bottom left, blue = bottom right). Panels

e (K1), g (K2.1) and i (K2.2) illustrate the bootstrap ratios for each scout time

series. The horizontal dotted lines on the bootstrap ratio plots indicate the threshold

over which values are considered reliable (Bs. ratio = 2.58). ............................... 112

Figure 6.1 Behavioural results. Estimated marginal means for response accuracy (a)

(corrected for individual differences in response latency), and (b) response latency

(corrected for individual differences in response accuracy and AQ scores).

Separate means are presented for the neutral (N), fearful (F) and happy (H) faces,

error bars denote ±SEM. Scatter plots of (a) response accuracies versus latencies

and (d) response latencies versus AQ scores. The results from the placebo (PBO)

and oxytocin (OXT) sessions are presented in blue and red, respectively. Latencies

prior to the response cue (i.e.; disappearance of the face stimulus) are shaded in

grey. ....................................................................................................................... 130

Figure 6.2 Grand mean visual evoked potentials. Panels (a), (b) and (c) present results

from the placebo (PBO) session for the left (PO7, P7), right (PO8, P8), and central

(C1-FC2) electrode clusters, respectively. Results from the oxytocin (OXT)

session are presented for the left (d), right (e) and central (f) electrode clusters.

Responses to the neutral (N), fearful (F) and happy (H) faces are presented in the

dotted, solid and dashed traces, respectively. The yellow shading illustrates the

time windows for the VEP analyses. Early responses from the clusters were

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averaged over the 40-60ms time window, P100 and N170 responses from the left

and right clusters were detected within the 80-120ms and 140-190ms time

windows respectively, whereas VPP and LPP responses from the central cluster

were detected within the 140-190 and 400-600ms time windows respectively. .. 131

Figure 6.3 Very early VEP effects. (a) VEP topographies at 40ms for the neutral (N),

fearful (F) and happy (H) faces, after placebo (PBO) and oxytocin (OXT)

administration. The left, right and central electrode clusters are marked in orange.

Panels (b) and (c) present the estimated marginal means amplitudes (averaged over

the 40-60ms time-window) from the right and central electrode clusters,

respectively. The y-axis for panel c is reversed, so that higher bars correspond with

stronger negativities. Results from the PBO and OXT sessions are presented in the

blue and red bars. The error bars denote ±SEM, * p < 0.05, ** p < 0.01. ............ 133

Figure 6.4 P100 results. (a) VEP topographies at 103ms for the neutral (N), fearful (F)

and happy (H) faces, after placebo (PBO) and oxytocin (OXT) administration. The

left, right and central electrode clusters are marked in orange. Estimated marginal

means for (b) left P100 amplitude, (c) right P100 amplitude, (d) left P100 latency

and (e) right P100 latency in response to neutral (N), fearful (F), and happy (H)

faces. The estimated marginal means are corrected for individual differences in

response latency. The results from the placebo (PBO) and oxytocin (OXT) sessions

are presented in the blue and red bars. The error bars denote ±SEM, *** p < 0.001.

............................................................................................................................... 135

Figure 6.5 N170 and VPP results (a) VEP topographies at 163ms for the neutral (N),

fearful (F) and happy (H) faces, after placebo (PBO) and oxytocin (OXT)

administration. The left, right and central electrode clusters are marked in orange.

Estimated marginal means (EMMs) are presented for (b) left N170 amplitude (c)

right N170 amplitude, (d) left N170 latency, (e) right N170 latency and (f) central

VPP amplitude (g) central VPP latency. The N170 EMMs (b-e) are corrected for

individual differences in response latency. The N170 amplitude EMMs (b-c) are

also corrected for individual differences in AQ scores. The y-axes for panels (b)

and (c) are reversed, so that higher bars correspond with stronger negativities. The

results from the placebo (PBO) and oxytocin (OXT) sessions are presented in the

blue and red bars, respectively. The error bars denote ±SEM. * p < 0.05, ** p <

0.01 ........................................................................................................................ 136

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Figure 6.6 LPP results. (a) LPP topographies at 540ms for the neutral (N), fearful (F)

and happy (H) faces, after placebo (PBO) and oxytocin (OXT) administration. (b)

Estimated marginal means for LPP amplitude (corrected for behavioural response

latency). The results from the placebo (PBO) and oxytocin (OXT) sessions are

presented in the blue and red bars, respectively. The error bars denote ±SEM, ** p

< 0.01. ................................................................................................................... 138

Figure 7.1 Illustration of the central two rings of the multifocal stimulus. a) The patches

alternate between light and dark grey in pseudorandom binary sequences, that are

updated every video frame (60Hz). (b) The first-order kernel (K1) is the difference

in response when the central patch was light or dark, K1=0.5*(SL-SD). (c) The first

slice of the second order kernel (K2.1) takes the previous frame into consideration,

comparing responses when a transition did or did not occur: K2.1=0.25*(SLL + SDD

- SLD - SDL). The second slice of the second order kernel (K2.2) is similar to K2.1,

but there is another intervening frame of either polarity: K2.2=0.25*(SL_L + SD_D -

SL_D - SD_L). ........................................................................................................... 154

Figure 7.2 STAI results (a) Estimated marginal means for the change in state anxiety

(STAIchange) after nasal spray administration, corrected for individual differences in

baseline state anxiety (STAIpre). Results for the OXT and PBO sessions are

presented in the red and blue bars respectively. The error bars represent ±1SE. (b)

Scatter plots and linear fits of the relationships between STAIchange and STAIpre for

the OXT (red markers and fit line) and PBO sessions (blue markers and fit line).

............................................................................................................................... 157

Figure 7.3 The effects of treatment, contrast and SIAS on K1 responses. Mean

waveforms for the low (a) and high (b) contrast stimuli are presented for OXT (red

traces) and PBO (blue traces). The shading denotes ±1SE. Estimated marginal

means of K1N65P105 amplitudes (c) (corrected for individual differences in SIAS)

are presented for OXT (red bars) and PBO (blue bars) at low and high contrast.

Linear fits of the correlations between K1 amplitudes and social anxiety scores are

presented for the low (d) and high (e) contrast stimuli, under the OXT (red fit lines

and markers) and PBO (blue fit lines and markers) treatment conditions. ........... 158

Figure 7.4 The effects of treatment, contrast and SIAS on K2.1 responses. Mean

waveforms for the low (a) and high (b) contrast stimuli are presented for OXT (red

traces) and PBO (blue traces). The shading denotes ±1SE. Estimated marginal

xvi

means of K2.1N70P105 amplitudes (c) (corrected for individual differences in SIAS)

are presented for OXT (red bars) and PBO (blue bars) at low and high contrast.

Linear fits of the correlations between K1 amplitudes and social anxiety scores are

presented for the low (d) and high (e) contrast stimuli, under the OXT (red fit lines

and markers) and PBO (blue fit lines and markers) treatment conditions. ........... 160

Figure 7.5 The effects of treatment and contrast on K2.2 responses. Mean waveforms

for the low (a) and high (b) contrast stimuli are presented for OXT (red traces) and

PBO (blue traces). The shading denotes ±1SE. The estimated marginal mean

amplitudes for the K2.2N70P90 (c) and K2.2N130P160 (d) waveforms are presented for

low and high contrast stimuli (OXT: red bars, PBO: blue bars). .......................... 161

xvii

List of Tables Table 1.1 Characteristics of the afferent pathways ........................................................... 2

Table 7.1 Descriptive statistics for AQ, SIAS and state anxiety .................................. 156

xviii

List of Papers as Part of this Thesis Papers In Press

Hugrass, L., Verhellen, T., Morrall-Earney, E, Mallon, C & Crewther, D.P. (In Press).

The effects of red surrounds on visual magnocellular and parvocellular cortical

processing and perception. Journal of Vision

Papers In Submission

Hugrass, L., & Crewther, D. (In Submission). The afferent pathway origins of scalp

recorded visual evoked potentials - A review

Hugrass, L., & Crewther, D. (In Submission). A review of non-linear visual evoked

potential research into contributions from the human M and P pathways to

cortical vision.

Hugrass, L., Labuschagne, I., Price, A., & Crewther, D. (In Submission). Part 1:

Intranasal oxytocin modulates very early visual processing of emotional faces.

Hugrass, L., & Crewther, D. (In Submission). Part 2: Acute intranasal oxytocin does not

influence the non-linear temporal structure of cortical visual evoked potentials.

Papers In Preparation

Hugrass, L., & Crewther, D. (In Preparation). The temporal structure of evoked MEG

responses: Effects of chromatic saturation

xix

List of Additional Papers that do not Form a Part of this Thesis

Published Papers

Burt, A., Hugrass, L., Frith-Belvedere, T., & Crewther, D. (2017). Insensitivity to

Fearful Emotion for Early ERP Components in High Autistic Tendency Is

Associated with Lower Magnocellular Efficiency. Frontiers in human

neuroscience, 11, 495.

Jastrzebski, N. R., Hugrass, L. E., Crewther, S. G., & Crewther, D. P. (2017). Surround-

Masking Affects Visual Estimation Ability. Frontiers in integrative

neuroscience, 11, 7.

Hugrass, L., Slavikova, J., Horvat, M., Al Musawi, A., & Crewther, D. (2017).

Temporal brightness illusion changes color perception of “the dress”. Journal of

vision, 17(5), 6-6.

Riddell, N., Hugrass, L., Jayasuriya, J., Crewther, S. G., & Crewther, D. P. (2016). An

asymmetric outer retinal response to drifting sawtooth gratings. Journal of

neurophysiology, 115(5), 2349-2358.

Crewther, D. P., Brown, A., & Hugrass, L. (2016). Temporal structure of human

magnetic evoked fields. Experimental brain research, 234(7), 1987-1995.

Hugrass, L., & Crewther, D. (2012). Willpower and conscious percept: volitional

switching in binocular rivalry. PloS one, 7(4), e35963.

Papers In Submission

Eveline Mu, Laila Hugrass, David Crewther (2018) Red background facilitates high

spatial frequency fearful face processing in groups with high autistic tendency.

Frontiers in Neuroscience/Perception Science

Katie Wykes, Laila Hugrass, David Crewther (2018) Autistic personality is not a strong

predictor of binocular rivalry dynamics. Frontiers in Neuroscience/Perception

Science

xx

List of Presentations 2017 Australasian Cognitive Neurosciences Conference “The Effects of Chromatic

Saturation on Non-linear Evoked MEG Responses”

2017 Vision Science Society “The effects of visual surround on multifocal visual

evoked potentials”

2016 Australasian Cognitive Neurosciences Conference “Non-linear VEP analysis of

orientation selective surround suppression”

2016 Vision Science Society “Does early processing of low spatial frequency facial

emotion vary as a function of autistic tendency?”

2016 Experimental Psychology Conference “Pupil responses reflect perceived

brightness shifts in moving ramped gratings”

xxi

List of Abbreviations

ANOVA – Analysis of variance

AQ – Autism Spectrum Quotient

CRF – Contrast response function

EEG – Electroencephalography

K – Koniocellular

K1 – First-order Wiener kernel

K2.1 – First slice of the second-order

Wiener kernel

K2.2 – Second slice of the second-order

Wiener kernel

LGN – Lateral genicular nucleus

LMM – Linear mixed effects model

M – Magnocellular

MT/V5 – Middle temporal area V5,

M – Mean

m-sequence – Maximum length

sequence

MEG -Magnetoencephalography

mfVEP – Multifocal Visual Evoked

Potential

OXT – Oxytocin

P – Parvocellular

PBO – Placebo

RMS – Root mean square

SD – Standard deviation

SIAS – Social Interaction Anxiety Scale

STAI – State-trait anxiety inventory

ssVEP – steady state Visual Evoked

Potential

V1 – Primary visual cortex

V2 – Visual area 2

VEP – Visual Evoked Potential

VESPA - Visual Evoked Spread

Spectrum Analysis

1

Chapter 1: Introduction and Thesis Overview The magnocellular (M), parvocellular (P) and koniocellular (K) visual pathways

have been studied extensively in primates (e.g.; Casagrande, 1994; Hendry & Reid,

2000; Kaplan & Shapley, 1986; Livingstone & Hubel, 1988; Wiesel & Hubel, 1966).

The afferent pathways remain fairly well segregated from the retina, to the lateral

geniculate nucleus (LGN) and the respective input layers in the visual cortex (Nassi &

Callaway, 2009). However, due to interactions at the level of the cortex (Nealey &

Maunsell, 1994; Vidyasagar & Pammer, 2010), as well as overlapping sensitivities

(Derrington & Lennie, 1984; Xu et al., 2001), there is a healthy degree of scepticism as

to whether behavioural data or scalp-recorded signals can be meaningfully traced back

to specific subcortical visual pathways (Kaplan, 2014; Skottun, 2014; Skottun &

Skoyles, 2007, 2010).

Despite these difficulties, interest in the major afferent pathways has remained

strong owing to their different contributions to form, colour and motion processing

(Livingstone & Hubel, 1988), and a proposed role for the M system in the rapid

processing of behaviourally relevant input (Kveraga, Boshyan, & Bar, 2007; Vlamings,

Goffaux, & Kemner, 2009; Vuilleumier, Armony, Driver, & Dolan, 2003).

Furthermore, M pathway abnormalities are associated with a range of developmental of

disorders, including autism (McCleery, Akshoomoff, Dobkins, & Carver, 2009),

schizophrenia (Butler et al., 2006) and dyslexia (Lovegrove, 1996; Stein & Walsh,

1997). In order to infer contributions from the subcortical visual pathways to these brain

mechanisms and disorders, neuroscientists are faced with the challenge of developing

valid techniques for studying each pathway in isolation.

The remaining sections of this chapter present an introduction to the M, P and K

afferent pathways (§1.1), a description of the non-linear VEP technique for inferring M

and P contributions to cortical evoked responses (§ 1.2), and a chapter-by-chapter

overview of the thesis questions and aims (§ 1.4).

1.1 Overview afferent pathways At any point of the visual field, there are multiple types of retinal ganglion cells

(RGCs) that transmit different signals in parallel to the brain (Nassi & Callaway, 2009).

The most widely studied visual pathways have been the M, P and K projections from

2

the retina to V1 via the LGN; however, there are other visual pathways, including a

pathway to the cortex via the superior colliculus and pulvinar, which receives

predominantly M input (Bridge, Leopold, & Bourne, 2016; Warner, Kwan, & Bourne,

2012). Primate physiological studies of the major afferent pathways have been reviewed

in much greater detail elsewhere (Kaplan, 2014; Nassi & Callaway, 2009). To explain

the background and rationale for this thesis, I have summarised the characteristics of the

M, P and K pathways in Table 1, with citations to relevant review and empirical papers.

Table 1.1 Characteristics of the afferent pathways

Characteristic Magnocellular (M) Parvocellular (P) Koniocellular (K) Retinal geniculate cells (RGCs)

Parasol (analogous to cat Y cells), although the M pathway also receives input from smooth monostratified cells1

Midget (analogous to cat X cells) 1

Various, including small and large bistratified cells (analogous to cat W cells)1

LGN Layer Ventral layers 1 and

21 Dorsal layers 3,4,5,61 Mostly within (but

not restricted to) thin inter-laminar LGN layers. The dorsal, middle and ventral pairs of K layers appear to form separate sub-pathways2

V1 Input Layer 4Cα1 4Cβ1 Cytochrome oxidase rich regions of Layers 2/3 (also direct projections to MT/V5)2

Projections from LGN to cortex

~10% 1,2 ~ 80% 1,2 ~ 10% 1,2

Axons Thick, fast conduction1

Thin, slow conduction1

Thin,2 slow conduction3

Receptive field size Large4 Small4 Some small and some large4

Increase cell density and decrease in receptive field size with eccentricity

Yes5 Yes. There is a very high P: M ratio near the fovea 4,5

Yes, but more scattered than M and P, presumably due to heterogeneity of K cell types 4,5

Receptive field centres

Both ON and OFF subpopulations

Both ON and OFF subpopulations

Small bistratified cells: blue ON/ yellow OFF

Chromatic input Combination of L- and M- cone input to centre and surround.

Antagonistic L-cone and M-cone input (i.e.; red/green)

See above

3

Characteristic Magnocellular (M) Parvocellular (P) Koniocellular (K) Spatial opponency

Type III M cells (centre-surround opponent but not spectrally opponent) Type IV M cells (centre-surround opponent, but are suppressed by red light in the surround)6

Receptive fields tend to be both centre-surround and red/green opponent) 6

Receptive fields tend to be both centre-surround and blue/yellow opponent 7

Achromatic contrast response function (LGN)

High gain at low contrast and a degree of response saturation at mid-high contrast8

Low contrast gain, most P cells show no saturation at mid-high contrast8

Heterogeneous. The mean K response is intermediate to P and M responses, but there is some variation in response from K cells within different LGN layers4,5

Spatial frequency preference (LGN)

Prefers low spatial frequency stimuli than P cells, low-pass tuning curve4

Prefers higher spatial frequencies that M cells, band-pass tuning4

Heterogeneous. Some K cells do not respond at all to gratings4

Temporal frequency preference (LGN)

Prefers 10-20 Hz stimulation, with a gradual drop off in sensitivity outside of this range8

Prefers ~10Hz stimulation, the drop off in sensitivity for low temporal frequencies is more shallow than it is for M cells 8

Heterogeneous4

Citations: 1 (Nassi & Callaway, 2009), 2 (Hendry & Reid, 2000), 3 (Irvin, Casagrande, & Norton, 1993), 4 (White, Solomon, & Martin, 2001), 4 (Xu et al., 2001), 5 6 (Livingstone & Hubel, 1984), 7 (Field et al., 2007), 8 (A. M Derrington & Lennie, 1984)

As illustrated in Table 1.1, although there are some differences in sensitivities

between the pathways, there is a great deal of overlap between them. Of the three major

afferent pathways, the P pathway appears to be the most homogenous in terms of

receptive field properties and cortical projections. However, in far peripheral vision, P

(i.e. midget) RGCs are no longer chromatically opponent (Dacey, 1999), because as

their density drops off, they receive input from more than one cone type. The notion that

the M pathway reflects a homogenous population of visual neurons is complicated by a

few factors. Firstly, the subdivision of M receptive fields into Types III (spatially

opponent, but not chromatically opponent) and IV (suppressed by red light in the

receptive field surround) suggests there might be functionally distinct M sub-streams

(de Monasterio, 1978; Hubel & Livingstone, 1990). Secondly, parasol RGCs and

4

smooth monostriate RGCs, have similar receptive field properties and both appear to

project to the superior colliculus and ventral layers of the LGN, which suggests the

presences of parallel ‘M’ like channels (Crook et al., 2008).

It was once assumed that visual processing could be cleanly separated into the M

and P streams, however this assumption was complicated by the discovery of K cells

(Casagrande, 1994). The K layers of the LGN are populated by a heterogeneous group

of cells with different receptive field properties (Hendry & Reid, 2000), so it would not

make sense to design VEP stimuli aimed at targeting all K cells. On the other hand, the

subpopulation of K cells that transmit blue chromatic input to the cortex can be studied

based on responses to s-cone isolating stimuli (Chatterjee & Callaway, 2003). K

thalamocortical neurons are only slightly more numerous than those of the M pathway,

but they lack the power that M cells have to dominate cortical circuits, owing to their

high divergence factor and large axons (Hendry & Reid, 2000). This limits the degree to

which K signals are likely to contribute to scalp-recorded VEPs, so for the purpose of

this thesis, I focus mostly on putative M and P VEP signatures, while acknowledging

the possibility of some contribution from K cells.

After arriving at the input layers of V1, M P and K signals project to different

cortical regions, with the dorsal ‘vision for action’ stream receiving predominantly M

input and the ventral, ‘vision for perception’ stream receiving predominantly P input

(Goodale & Milner, 1992; Ungerleider & Mishkin, 1982). K cells mostly project to the

cytochrome oxidase blobs in V1 layers 2/3, as well as to the V1/V2 border, which

suggests they play an important role in colour processing; however there is also direct K

input from the LGN to MT/V5 (Hendry & Reid, 2000). Due to complex

interconnections between regions within the dorsal and ventral visual streams, the sub-

cortical afferent pathways are no longer segregated once their signals reach the cortex

(Kaplan, 2014). This means that it is difficult to design experiments to selectively target

contributions from the M, P or K pathways.

1.2 The non-linear VEP approach to studying the afferent streams There are several approaches to identifying M and P contributions to scalp-

recorded VEPs, including transient VEPs, steady state VEP, Visual Evoked Spread

Spectrum Analysis (VESPA) and non-linear temporal analysis of multifocal VEPs (e.g.;

Ellemberg, Hammarrenger, Lepore, Roy, & Guillemot, 2001; Klistorner, Crewther, &

Crewther, 1997; Lalor, Kelly, & Foxe, 2012; Zemon & Gordon, 2006). Of the

5

aforementioned techniques, non-linear analysis of diffuse mfVEPs provides the cleanest

separation of putative M and P VEP signals (reviewed, Chapter 2), so this technique

forms the basis for much of the work presented in the following chapters. Applications

of this technique are reviewed in Chapter 3, and non-linear VEP methodology was

applied for the experimental papers presented in Chapters 4, 5, and 7. This section

provides a brief introduction to the multifocal VEP technique.

For a linear system, it is possible to fully characterise the response to a train of

stimuli based on the sum of responses to each individual stimulus. Yet, due to temporal

non-linearities in the visual system, it is necessary to account for responses to the

preceding stimuli (Victor, 1992). There are many different methods for characterising

temporal non-linearities in the visual system (reviewed, Klein, 1992; Marmarelis &

Marmarelis, 1978; Sutter, 1992; Victor, 1992). For the purpose of this thesis, we focus

specifically on the use of multifocal binary m-sequence stimulation, and Weiner kernel

decomposition of the non-linear temporal VEP kernels (Sutter, 1992).

An m-sequence (maximum length sequence) is a type of pseudorandom binary

sequence of length 2m -1 that contains all of the possible configurations of m

consecutive elements exactly once, except for the all zero configuration (Golomb,

1967). In a multifocal VEP experiment, an m-sequence modulates the diffuse luminance

(e.g., Klistorner et al., 1997) or pattern contrast (e.g., Baseler & Sutter, 1997) between

binary levels. The m-sequences for separate patches are temporally de-correlated from

each other, so it is possible to record independent responses from across the visual field.

The m-sequence for each patch is usually updated at the frame rate of the display screen

(i.e., 60 – 100 Hz).

Sutter developed a method to cross-correlate visual responses with the m-

sequence, based on Fast Walsh transform (Sutter, 1992). This allows for rapid Wiener

kernel decomposition with a single cross-correlation, so that one can easily investigate

the response recovery of the visual system to a full range of binary stimulation. In a

linear system, the first-order kernel (K1) is the impulse response function. The first

(K2.1) and second (K2.2) slices of the second order kernel measure non-linear temporal

interactions over one and two video frames respectively. The nature of the kernel

responses depends on the timing and form of the stimuli. For diffuse luminance

stimulation, there are a robust K1, K2.1 and K2.2 responses, yet for pattern reversal

stimulation, there is not much power in the K1 response (Baseler & Sutter, 1997). As

6

the frame rate slows, the resulting VEP kernels tend to approximate waveforms

recorded using conventional (i.e.; transient) VEP paradigms (Fortune & Hood, 2003).

Klistorner et al. (1997) presented diffuse (i.e. unpatterned) multifocal, m-

sequence stimuli, and varied the degree of temporal contrast between the binary

luminance levels. Peak-to-peak amplitudes of the major waveforms were plotted against

temporal luminance contrast. Modelling of the K1 waveform suggests that it reflects the

sum of two components, one with high contrast gain and rapid saturation (putative M)

and the other with lower contrast gain but no saturation (putative P). Consistent with an

M generator, the K2.1 response had high contrast gain and saturated at mid contrast

levels. Consistent with a P generator, the main K2.2 waveform gradually increased in

amplitude with contrast, and the contrast response function showed no sign of

saturation. More recently, it has been shown that the contrast response function of the

early K2.2 waveform (which is lower in amplitude than the main K2.2 waveform) is

consistent with an M pathway generator (Jackson et al., 2013).

There are several advantages to using non-linear VEP analyses to identify

putative M and P signals. Firstly, it is advantageous to stimulate with small multifocal

patches, because it minimises temporal blurring. When VEP stimuli activate large

regions of V1, temporal blurring can occur due to changes in the ratio of M and P cells

with eccentricity, and individual differences in the gross anatomy of visual field maps

can alter the orientation of cortical sources of VEP signals (Baseler & Sutter, 1997;

Baseler, Sutter, Klein, & Carney, 1994). Secondly, fast m-sequence stimulation evokes

responses that are well localised to the primary visual cortex, compared with the

conventional, transient VEP signal, where there is considerable input from extrastriate

regions to the waveforms (Fortune & Hood, 2003). Thirdly, due to relatively clean

separation of M and P signals into the main K2.1 and K2.2 waveforms, it is possible to

compare putative M and P responses to the same stimuli; whereas other VEP techniques

often rely on different stimuli to target the M and P systems (i.e.; achromatic low

contrast versus isoluminant red/green stimulation).

1.3 Outline of thesis chapters and aims The overall aim of this body of work is to use non-invasive neurophysiological

and psychophysical techniques to infer cortical processing of input from the M and P

visual pathways. This thesis presents two review chapters and four experimental

chapters that address this general aim. As a consequence of the format there is some

7

unavoidable overlap in the background information and methodological explanations. In

order to communicate how each chapter contributes to the overall body of work, I have

included a guide at the start of each chapter, as well as short dot-pointed highlights of

the key and novel findings. All of the research conducted for this thesis was carried out

in accordance with the Declaration of Helsinki and the Swinburne Human Research

Ethics committee.

Chapters 2 and 3 are review papers that have been submitted for publication.

These chapters review evidence for different electrophysiological techniques that have

been used to investigate M, P and K processing, and identify future directions for non-

linear VEP research into the human M and P streams. Chapter 2 evaluates different

scalp-recorded VEP techniques for investigating the afferent visual pathways. The

techniques reviewed include transient VEP, steady state VEP, non-linear analysis of

multifocal VEP, and Visual Evoked Spread Spectrum Analysis (VESPA).

The review paper presented in Chapter 3 focuses specifically on the fast m-

sequence, non-linear VEP approach for measuring putative M and P signals. This

chapter begins by describing the afferent pathways, and evaluating evidence that the

main K2.1 and K2.2 waveforms reflect M and P input to the cortex respectively. The

paper goes on to review experiments from over the past 20 years that have applied this

technique to investigate the M and P pathways in humans. Specifically, it reviews

studies that used non-linear VEP signatures of M and P processing to draw conclusions

about: typical development of the visual system, individual differences in visual

processing associated with autism, dyslexia and dyscalculia, and the nature of chromatic

surface and form representations in V1.

Chapter 4 presents an original research paper that evaluates the claim that

presenting stimuli on a red background selectively suppresses M contributions to visual

processing. This work was based on primate single cell studies that identified a sub-

population of M cells (Type IV) that exhibit tonic suppression when red light is

presented in their receptive field surround (de Monasterio, 1978; Derrington,

Krauskopf, & Lennie, 1984; Hubel & Livingstone, 1990; Livingstone & Hubel, 1988).

Many human behavioural studies have been conducted based on the assumption that

presenting visual stimuli on a red backgrounds blocks M contributions to visual

processing (West, Anderson, Bedwell, & Pratt, 2010). There has been a surprising lack

of investigation into the validity of this assumption. The experiments presented in this

8

chapter used both non-linear VEP and steady and pulsed pedestal psychophysics to

investigate these claims.

Chapter 5 presents an original research paper (in preparation) that investigated

the effects of chromatic saturation on the non-linear temporal structure of cortical

signals, as measured with MEG. In an EEG study, Crewther and Crewther (2010)

analysed non-linear temporal responses to a diffuse patch that pseudo-randomly

reversed between binary grey levels. K2.1 amplitudes greatly increased when blue or

red colour was added to the darker grey, even though the luminance contrast was held

constant (Crewther & Crewther, 2010). As described in Section 1.2, under conditions of

achromatic stimulation the K2.1 waveform appears to reflect input from the M pathway

(Klistorner et al., 1997). Given that the M pathway does not contribute to colour

perception (Livingstone & Hubel, 1988), it was surprising that K2.1 amplitudes

increased with the level of chromatic saturation. Chapter 5 investigates the cortical

sources of non-linear VEP signal that are sensitive to chromatic saturation.

Chapters 6 and 7 present the results of a two-part original research paper that

was designed to investigate the effects of oxytocin on visual processing of affective and

non-affective stimuli. Oxytocin is a neuropeptide that is well known for its effects on

social cognition (Bartz & Hollander, 2008). Evidence that oxytocin influences

functional coupling between the amygdala and superior colliculi (Gamer, Zurowski, &

Büchel, 2010) suggests that it might have profound effects on the earliest stages of

visual processing (Ebitz, Watson, & Platt, 2013). Rapid affective processing involves

the projection of coarse input from the M pathway to the superior colliculus, LGN,

amygdala and frontal cortices (Bar et al., 2006; Kveraga, Boshyan, & Bar, 2007; Pessoa

& Adolphs, 2010).

The data presented in Chapters 6 and 7 were collected from the same sample of

participants, in a double-blind, placebo controlled crossover study. Participants

completed two EEG sessions, one after receiving intranasal oxytocin and the other after

receiving a placebo. The aim of the experiment presented in Chapter 6 was to

investigate the timing of the effects of oxytocin on visual evoked responses to fearful,

happy and neutral faces. The aim of the experiment presented in Chapter 7 was to

investigate whether oxytocin specifially modulates visual processing of non-affective

input from the M or P pathways.

9

Chapter 8 provides a general discussion of the body of work presented within

this thesis. It summarises the novel findings, outlines key contributions to knowledge in

the area, and discusses implications for understanding how input from the afferent

visual pathways contributes to cortical signals and perception. This leads to a discussion

of the limitations associated with studying the human M and P pathways, and some

suggestions for future research.

1.4 References Bartz, J. A., & Hollander, E. (2008). Oxytocin and experimental therapeutics in autism

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Chapter 2: The Afferent Pathway Origins of Scalp Recorded Visual

Evoked Potentials - A Review

2.1 Chapter guide

Hugrass, L., & Crewther, D. (In Submission). The afferent pathway origins of scalp

recorded visual evoked potentials - A review

This review chapter presents a re-formatted version of the article cited above,

which has been submitted to the European Journal of Neuroscience as a review paper.

In this chapter, I reviewed a range of non-invasive VEP techniques that are used to

investigate the human M and P pathways, including transient VEP, ssVEP, nonlinear

VEP and Visual Evoked Spread Spectrum Analysis (VESPA). The aim of this chapter

was to review evidence as to whether it is possible to meaningfully separate

contributions from the human M, P and K pathways to scalp-recorded VEPs, and to

identify the advantages and disadvantages of the different VEP techniques.

15

2.2 Abstract It is not an easy task to attribute scalp-recorded visual evoked potential (VEP)

signals to their origins in the LGN afferent pathways. This is largely due to the non-

invasive restriction on recording techniques in humans. Primate studies have shown that

the magnocellular (M), parvocellular (P) and koniocellular (K) pathways have different

sensitivities to luminance contrast, spatial frequency, colour, and motion. On the basis

of these results, researchers have designed stimuli to preference different afferent

pathways, and then inferred their contributions to components of scalp-recorded VEPs

in humans. Here we review studies that have used transient, steady state, multifocal

(i.e., fast m-sequence) and VESPA approaches to study afferent inputs to VEP signals.

We evaluate evidence as to whether input from the human M, P and K pathways can be

separated using VEPs, and discuss advantages and disadvantages of the different VEP

techniques.

16

2.3 Introduction Visual pathways from the retina to the striate cortex form partly anatomically

and physiologically distinct magnocellular (M), parvocellular (P) and koniocellular (K)

pathways. The major afferent visual pathways have been well studied in primate single

cell, viral transfection and optical imaging studies (reviewed, Kaplan, 2014; Nassi &

Callaway, 2009). Beyond the input layers of V1, there are complex interactions between

inputs from each afferent pathway (Nealey & Maunsell, 1994; Vidyasagar, Kulikowski,

Lipnicki, & Dreher, 2002). This makes it difficult to study the human M, P and K

systems non-invasively. Due to differences in the temporal and spatial receptive field

properties of M, P and K cells, as well as differences in their cortical projections, it has

been proposed that the afferent pathways contribute differently to higher-level

perceptual and cognitive tasks (Bullier, 2001; Kveraga, Boshyan, & Bar, 2007;

Laycock, Crewther, & Crewther, 2007). Of particular interest, M pathway abnormalities

have been implicated in a range of neurodevelopmental disorders (Laycock et al., 2007;

Lovegrove, 1996; Stein, 2014). Consequently, many researchers have worked on

developing scalp recorded visual evoked potential (VEP) paradigms to isolate the

afferent pathways.

2.4 Primate studies of the afferent visual pathways Primate physiological studies of the afferent visual pathways have been

reviewed in detail elsewhere (Kaplan, 2014; Nassi & Callaway, 2009). For the reader’s

convenience, this section provides a brief summary of the M, P and K pathways. M

retinal ganglion cells (i.e.; parasol cells, which are analogous to Y cells in cats) have

large cell bodies and thick axons that project to the ventral two LGN layers, and then to

input layer 4Cα of V1. M receptive fields are large, with spatially opponent ON- or

OFF-centres. M cells do not exhibit chromatically opponent responses; however there is

a subpopulation of ‘Type IV’ M cells, that are suppressed when red light is presented in

their receptive field surrounds (de Monasterio, 1978; Derrington, Krauskopf, & Lennie,

1984; Livingstone & Hubel, 1984; Wiesel & Hubel, 1966). M cells have a preference

for low spatial frequency, high temporal frequency stimulation. M contrast response

functions (CRFs) exhibit high gain at low contrast levels, and saturate at medium to

high contrasts (Kaplan & Shapley, 1986).

P retinal ganglion cells (i.e. midget cells, which are analogous to X cells in cats)

have much smaller somata and dendritic field spread than M cells, and have relatively

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thin axons that project to the dorsal four LGN layers and the trans-synaptically project

to input layer 4Cβ in V1. The density of P cells is very high around the fovea, but there

is a steep drop off in the ratio of P to M cells at greater eccentricities (Dacey, 1993). P

receptive fields have ON- or OFF-centres, with red-green chromatically opponent and

spatially opponent responses. In comparison to M cells, P cells have lower luminance

contrast sensitivity, with non-saturating CRFs. Some evidence suggests that P cells do

not respond to stimuli with Michelson contrasts lower than approximately 8% (Tootell,

Hamilton, & Switkes, 1988).

K cells (konio: Greek, dust, which are analogous to W cells in cats) are a group

of small cells, which were initially ignored by physiologists who focused on the larger

M and P cells (Casagrande, 1994). K cells have thin axons that project to regions in

between layers of the LGN, and then to the upper layers of V1, mostly close to, or

within the cytochrome oxidase blobs (Hendry & Reid, 2000). The receptive field

properties of K cells are heterogeneous. Some K cells have CRFs similar to those of M

cells or P cells, while cells in the middle K layers of the LGN receive short-wave length

cone input from small bistratified and large sparse retinal ganglion cells, and transmit

input to the cytochrome oxidase blob centres in V1 (Chatterjee & Callaway, 2003; ,

Martin & Lee, 2014; Szmajda et al., 2008; White, Solomon, & Martin, 2001). While it

was once thought that the afferent pathways could be cleanly separated into the M and P

streams, the study of K cells has shown there are at least five parallel afferent streams

(Hendry & Reid, 2000).

2.5 Human VEP analyses of the afferent visual pathways

Several researchers have attempted to identify VEP components originating

from the M, P and K pathways, based on what is known from primate studies of

achromatic contrast response functions, spatial frequency preferences, chromatic

sensitivity, temporal response characteristics and changes in response to stimulation

across the visual field. For instance, it has been proposed that VEPs to low luminance

contrast stimulation rely on the M pathway, that red-green colour vision is highly

dependent on the P pathway, and that the K subpopulation that transmits s-cone input

can be inferred based on responses to tritan (blue-yellow) stimuli (e.g.; Barboni et al.,

2013; Foxe et al., 2008).

When attempting to separate afferent contributions to VEP signals, it is

worthwhile considering their cortical sources. Although the afferent streams target

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different layers of V1 (Hendry & Reid, 2000; Livingstone & Hubel, 1988), the spatial

resolution of VEP is nowhere near sufficient to resolve these inputs into separate

sources. The ventral ‘vision for perception’ stream receives predominantly P input, and

the dorsal ‘vision for action’ stream receives predominantly M input; yet a substantial

number of cells receive converging input from the different afferent pathways (Nassi &

Callaway, 2009; Nealey & Maunsell, 1994; Vidyasagar et al., 2002). So, while it is

possible to localise VEP waveforms that originate from extrastriate regions of the

ventral and dorsal streams, it is not easy to attribute these signals to inputs from the M,

P or K pathways (Foxe et al., 2008).

Studies using different stimulation techniques have reported different signatures

of the M, P and K streams in scalp-recorded VEP signals. Therefore, it is advantageous

to understand how these techniques relate to each other, and to evaluate evidence as to

whether they identify separable measures of activity in the afferent pathways. For the

purpose of this review, we broadly categorized VEP methodologies into groups based

on the nature of temporal stimulation: transient VEPs (Section 2.6), steady state VEPs

(Section 2.7), m-sequence nonlinear VEPs (Section 2.8) and VESPA (Visually Evoked

Spread Spectrum Response Potential) (Section 2.9). These stimulation techniques are

illustrated in Figure 2.1 (with the exception of VESPA, which would be difficult to

represent pictorially). Within these sections, we review studies that have used responses

to luminance contrast, spatial frequency and colour in order to identify M, P and K

dominated VEP signals.

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Figure 2.1 Illustration of different VEP stimulation techniques. The top panels illustrate transient VEP at 1Hz, for diffuse (a) and pattern onset (b) and (c) pattern reversal stimulation modes. The bottom panels provide examples of contrast modulation sequences during d) steady state VEP at 4 Hz, (e) fast m-sequence VEP, where the contrast alternates pseudorandomly between binary levels, at a base interval of 16.7ms, and (f) VESPA, where the contrast is modulated randomly over a wide range of narrowly spaced levels, at a base interval of 16.7ms.

2.6 The transient VEP approach

2.6.1 Introduction to transient VEPs

A transient VEP is one that is recorded when the visual system is in resting state

before stimulus presentation, and is allowed to return to resting state before the next

presentation. Thus, it is presumed that a transient VEP recorded on any given trial does

not depend on the previous trials (Regan, 1989). The method of stimulus delivery

influences VEP waveforms. Onset VEPs are responses to an increase in pattern contrast

from a zero contrast baseline. Offset VEPs are recorded when the contrast returns to

zero after pattern stimulation. They tend to be lower in amplitude and more variable

than onset VEPs (Parker et al., 1982; Murray & Kulikowski, 1983; Regan, 1989). An

alternative method is to record responses to contrast reversing patterned stimuli (Regan,

1989; Odom et al., 2016). As discussed below, there are some interesting differences

between putative M and P VEP signatures for pattern onset and pattern reversal

stimulation. For consistency, we will use the following nomenclature to describe the

main waveforms of pattern reversal VEPs: N75, P100 and N135, and pattern onset

VEPs C1 (positive, ~75ms), C2 (negative, ~125ms), C3 (positive, ~ 150ms). It should

be noted that the timing of these waveforms varies, depending on the display monitor

(e.g.; LCD or CRT) and stimulus (e.g.; size, luminance, contrast) characteristics. C1

reverses polarity as a function of visual field position, due to the geometry of V1

(Jeffreys & Axford, 1972; Di Russo et al., 2002; Kelly et al., 2013).

2.6.2 Effects of luminance contrast and spatial frequency on VEPs

For achromatic pattern reversal stimuli, the N75 (50-90ms) and P100 (100-

130ms) VEP waveforms appear to receive differential contributions from the M and P

afferent pathways. The N75 waveform dominates responses to gratings with high

contrasts and high spatial frequencies, and is absent in response to gratings with low

contrasts and low spatial frequencies (Ellemberg, Hammarrenger, Lepore, Roy, &

20

Guillemot, 2001). P100 latencies tend to be shorter for checkerboards with larger check

sizes (Shawkat & Kriss, 1998). For low spatial frequency patterns, P100 amplitudes are

measurable even at low contrast and saturate at mid to high contrast (Souza, Gomes,

Saito, da Silva Filho, & Silveira, 2007). For high spatial frequency patterns, P100

amplitudes increase more steeply with contrast and do not appear to saturate from

medium to high contrast (Rudvin, Valberg, & Kilavik, 2000). Taken together, these

results suggest that the N75 waveform is dominated by input from the P pathway, that

the P100 response to high contrast, high spatial frequency patterns is dominated by

input from the P pathway, and that the P100 response to low contrast, low spatial

frequency patterns is dominated by input from the M pathway.

The major C1 and P1 peaks of onset-offset VEP responses also appear to reflect

differential contributions from the M and P pathways (Hall et al., 2005; Jones & Keck,

1978; Murphy, Kelly, Foxe, & Lalor, 2012; Parker, Salzen, & Lishman, 1982). Some

early studies reported that for low spatial frequency pattern onset VEPs, a fast-latency

N0-P0 (60-80ms) waveform emerges prior to the C1-P1 waveform (90-130ms) (Jones

& Keck, 1978). Consistent with an M generator; N0-P0 amplitudes saturate at low

contrast, and as the spatial frequency of the pattern decreases, N0 amplitudes increase

(Jones & Keck, 1978) and latencies decrease (Parker et al., 1982). However, for most

other studies, C1 is the earliest reported waveform in the pattern-onset VEP (Foxe et al.,

2008; Hall et al., 2005; Marcar & Jäncke, 2016).

Foxe et al. (2008) used high-density EEG to record pattern onset VEPs and to

investigate the spatiotemporal dynamics of C1 generation. Consistent with a P

generator, C1 amplitudes were maximal in response to an achromatic, high contrast

check pattern (which should stimulate M, P and K cells), and for an isoluminant red-

green pattern (which should stimulate P, and possibly K cells). The C1 waveform

disappeared in response to a low contrast achromatic pattern (which should only

stimulate M cells), despite the fact that a strong P1 waveform was observed. These

results indicate that the initial volley of M input to the striate cortex does not contribute

to scalp recorded C1 waveforms.

Hall et al. (2005) used MEG to investigate the cortical sources of pattern onset

VEP waveforms. The analyses revealed striate and extrastriate sources, with CRFs that

were consistent with those of P and M cells respectively (Kaplan & Shapley, 1986).

This may account for some disparities in previous EEG studies that recorded from

21

occipital or lateral sites. Hall et al. averaged these source responses over a broad time

window, so it is unclear whether these sources specifically reflect the C1 or P1

responses. However, the lack of correlation between C1 and P1 amplitudes (Murphy et

al., 2012) supports the argument that they originate from separate sources. Thus, it is

likely that the C1 waveform is generated in V1, and that the P1 waveform reflects

extrastriate contributions.

Marcar and Jäncke (2016) attempted to reconcile differences in the

manifestation of M and P characteristics to reversal and onset VEPs. Source localization

of pattern-reversal VEPs showed that N75 responses are maximal at the occipital pole,

and P100 amplitudes are higher in the surrounding areas. Pattern-reversal VEP

amplitudes increased with the area of the dartboard stimulus that underwent contrast

modulation, which suggests these signals are sensitive to temporal luminance contrast.

For onset VEPs, C1 responses were maximal over the occipital pole and P1 responses

were maximal in parietal regions. C1 responses were present regardless of the contrast

modulation area, whereas P1 amplitudes were much higher when at least 50% of the

dartboard was modulated. This suggests that C1 and P1 are modulated by spatial and

temporal contrast mechanisms respectively. Hence it could be the choice of stimulation

technique that determines the degree to which M and P signatures are visible in pattern

onset and pattern reversal VEPs.

2.6.3 Effects of colour on transient VEPs

This section reviews studies that have isolated P and K contributions to transient

VEPs, by stimulating with isoluminant chromatic gratings. Consistent with evidence

from chromatically sensitive V1 cells (Jankov, 1978), isoluminant pattern offset stimuli

do not elicit VEPs (Suttle & Harding, 1999). The onset of isoluminant red/green or

blue/yellow gratings evokes a prominent N1 (~120ms) response (Murray, Parry,

Carden, & Kulikowski, 1987; Suttle & Harding, 1999; Tobimatsu, Tomoda, & Kato,

1995). Both N1 (pattern onset) and N145 (pattern reversal) amplitudes increase with the

level of chromatic contrast (Gomes et al., 2006; Souza et al., 2008). When luminance

contrast is added to red/green gratings, N1 amplitudes decrease and VEP responses are

dominated by the P1 waveform, which presumably reflects luminance sensitive M and

P mechanisms (Mottron & Burack, 2001). A similar pattern of results is found when

luminance is added to blue/yellow gratings (Suttle & Harding, 1999).

22

N1 responses to achromatic stimuli increase monotonically with spatial

frequency, whereas N1 responses to chromatic stimuli are maximal at roughly two

cycles per degree (Murray et al., 1987; Tobimatsu et al., 1995). This suggests that P

dominated chromatic and luminance mechanisms have different spatial frequency

sensitivities. It is curious that N1 chromatic VEP amplitudes decrease for low spatial

frequency gratings (Murray et al., 1987), while psychophysical measurements do not

(Mullen, 1985). A possible explanation could be that V1 cells within and between

cytochrome oxidase blobs have different spatial frequency sensitivities (Edwards,

Purpura, & Kaplan, 1995), and appear to contribute differently to chromatic VEPs

(Crewther & Crewther, 2010). Overall, the evidence reviewed in this section suggests

that N1 responses to isoluminant chromatic gratings are likely to reflect P and K input

to colour sensitive regions in the striate and extrastriate cortex (Murray et al., 1987;

Suttle & Harding, 1999; Tobimatsu et al., 1995).

2.6.4 Effects of motion on transient VEPs

The M pathway targets motion sensitive cortical regions, such as MT/V5

(Maunsell, Nealey, & DePriest, 1990). Furthermore, psychophysically measured

contrast response functions for motion perception are similar to those of M cells

(Tootell et al., 1995). Motion VEPs were first studied by Clarke (1973), who identified

VEP components associated with motion onset, offset and reversal stimuli. Interest in

motion VEPs was later renewed, with the goal of identifying M and P driven VEP

components. This literature has been reviewed in detail elsewhere (Kuba, Kubová,

Kremláček, & Langrová, 2007); for the purpose of the current discussion, we provide an

overview of evidence as to whether motion VEPs can isolate the M pathway.

Motion onset VEPs produce larger and more reliable responses than motion

offset or motion reversal VEPs, hence they have been the most widely studied form of

motion VEP (Kuba et al., 2007). The main components of the motion onset VEP are the

P1 (~130ms) and N2 (~180ms) waveforms, which are maximal for occipital and

parietal/occipitotemporal recording sites respectively (Bach & Ullrich, 1997; Nakamura

& Ohtsuka, 1999). Early papers suggested that the P1 waveform was the main motion

related potential (Clarke, 1973), however more recent evidence suggests that this

component may reflect responses to patterned stimulation, as opposed to motion per se

(Bach & Ullrich, 1997; Kuba et al., 2007).

23

Bach and Ullrich (1997) found that N2 responses can be recorded even for very

low contrast motion stimulation, and that N2 amplitudes saturate at mid to low contrast.

These results suggest the N2 is dominated by M input. The largest N2 responses are

recorded for radially expanding grating stimuli, particularly when the stimuli are

sinusoidally modulated (Kremláček, Kuba, Kubova, & Chlubnova, 2004). This is

presumably because the high spatial frequency component of square-wave modulation

is not ideal for stimulating the M pathway. In summary, evidence reviewed in this

section suggests that N2, but not P1, motion onset VEP responses reflect M dominated

input to motion processing regions in the dorsal stream.

2.6.5 Summary of afferent contributions to transient VEPs

The studies reviewed in this section have identified transient VEP components

that appear to be dominated by input from the M, P and K pathways. For achromatic

pattern reversal and pattern onset stimulation, N75/C1 responses appear to be

dominated by P input (Ellemberg et al., 2001; Foxe et al., 2008), and P100/P1 responses

appear to be dominated by either M or P input, depending on the contrast and spatial

frequency of the stimulus (Parker et al., 1982; Souza et al., 2007). For isoluminant

chromatic stimulation, the N1 waveform appears to reflect P and K contributions to

chromatic processing mechanisms in the early visual cortex (Gomes et al., 2006; Suttle

& Harding, 1999); whereas for motion stimulation, the N2 waveform appears to reflect

M inputs to motion processing regions in the dorsal stream (Bach & Ullrich, 1997;

Kremláček et al., 2004). However, the reliance on different stimulation parameters to

target M, P and K dominated signals mean that transient VEP techniques are not well

suited for making direct comparisons regarding the initial volleys of afferent input to

the cortex.

2.7 The ssVEP approach

2.7.1 Introduction to ssVEP

When visual stimuli are presented at frequencies higher than 3Hz, a continuous

oscillatory potential can be recorded from the visual cortex, which has been termed the

steady state VEP, or ssVEP (Regan, 1973; Vialatte, Maurice, Dauwels, & Cichocki,

2010). Nonlinearities in the visual system change the shape of responses to a relatively

pure sine wave stimulus, which results in additional frequencies not originally present

in the input signal. The human SSVEP is typically characterized in terms of the phase

24

and amplitude of responses at the fundamental frequency (F) and at two times the

fundamental frequency (2F). For pattern reversal stimuli, most of the response power is

at 2F, whereas for onset-offset stimulation, there are robust F and 2F responses (Regan,

1973).

2.7.2 Effects of luminance contrast and spatial frequency on ssVEP

When ssVEP amplitudes are plotted against log luminance contrast, the function

consists of distinct limbs at low and high contrasts. This has been observed for F and 2F

responses to onset-offset stimulation (Bobak, Bodis-Wollner, Harnois, & Thornton,

1984; Nelson & Seiple, 1992), and for 2F responses to contrast reversing gratings

(Murray et al., 1987; Nakayama & Mackeben, 1982), and is indicative of contributions

from two neural mechanisms with different contrast sensitivities. This double slope

function is evident even at very early stages of development (Norcia, Tyler, & Hamer,

1990). Nakayama and Mackeben (1982) proposed that the low and high contrast limbs

could be reflections of M and P processing in the striate cortex.

For diffuse (i.e. unpatterned) stimulation, both slopes are present for frequencies

from 1 to 8Hz, yet the high contrast slope is compromised at frequencies above 16.67Hz

(Rudvin et al., 2000). This low-pass temporal filtering is consistent with a P generator.

Nelson and Seiple (1992) showed that the effects of spatial and temporal frequency on

ssVEP amplitudes are consistent with generators in separate pathways. For low

temporal frequency, high spatial frequency gratings, amplitudes increased

monotonically with log stimulus contrast. With low spatial frequency, high temporal

frequency gratings, the contrast response function had high gain up to 20% contrast, and

then showed logarithmic compression. This is consistent with the saturating CRFs

recorded from primate M cells (Kaplan & Shapley, 1986).

Some researchers have claimed that responses at 2F isolate the M system. This

was based on the idea that M cells have phasic response properties, and 2F responses

are similar in amplitude for the onset and offset of achromatic gratings (McKeefry,

Russell, Murray, & Kulikowski, 1996). This claim has been criticized based on the

presence of phasic responses in P cells (Skottun & Skoyles, 2007). Although it is

probably an overstatement to claim that 2F responses isolate the M pathway, evidence

from a recent chromatic ssVEP study (Barboni et al., 2013) is consistent with the

argument that responses at 2F are dominated by the M pathway (see Section 2.7.3).

25

Previous ssVEP (Zemon, Gordon, & Welch, 1988) and physiological (Schiller,

Sandell, & Maunsell, 1986) results suggest that bright or dark grey patches elicit

responses from the subcortical ON or OFF systems respectively. Zemon and Gordon

(2006) targeted the M ON and OFF systems by measuring ssVEPs to the appearance of

low contrast bright or dim patches on a mid-grey background. To target P ON and OFF

responses (and presumably K responses), a high-contrast pedestal was added to saturate

the M system, such that it would not respond strongly to temporal modulation in patch

luminance. Spatial tuning curves and CRFs for the pedestal and onset-offset conditions

were similar to those of P and M cells respectively (Derrington & Lennie, 1984; Kaplan

& Shapley, 1986). Across conditions, CRFs were steeper for the bright than dark

patches, which indicates that contrast gains are steeper for OFF than ON cells in both

the M and P pathways (Zemon & Gordon, 2006).

Current evidence suggests that ssVEP signal is not adequately explained by a

single dipole source; rather it appears to originate in the primary visual cortex, and

propagate to extrastriate regions (Vialatte et al., 2010). Di Russo et al. (2001) used

dipole modelling and fMRI to analyse the neural sources of 2F ssVEP responses to a

contrast reversing grating stimulus (6Hz alternation rate; 12 Hz contrast reversal rate).

The results revealed V1 and MT/V5 dipole sources account for approximately 97% of

variance in the signal. These results suggest it is possible that M and P dominated

ssVEP signals originate from different cortical sources.

2.7.3 Effects of colour on ssVEP

Under conditions of isoluminant chromatic stimulation, McKeefry et al. (1996)

found that the 2F component of the onset-offset ssVEP signal is degraded. In order to

further investigate potential M, P and K contributions to ssVEP components, Barboni et

al. (2013) varied the degree of chromatic contrast in isoluminant red/green (i.e. L/M

cone) and blue/yellow (S/L+M cone) gratings, under onset-offset and pattern reversal

stimulation conditions. Colour discrimination ellipses were plotted based on ssVEP

amplitudes at F (10 Hz for onset offset, and 10 rev/s for pattern reversal). These results

were well matched with the psychophysically measured ellipses. Therefore, it is likely

that input from the P and K streams dominates the F component of ssVEP responses to

isoluminant chromatic stimuli.

For both the onset/offset and reversal modes of stimulation, 2F ellipses were

larger and more elongated along the tritan confusion line than the F or psychophysically

26

measured ellipses (Barboni et al., 2013). The M pathway receives only minimal input

from short wavelength cones (Chatterjee & Callaway, 2002), and responds to red/green

chromatic flicker at 2F, presumably due to nonlinear summation of medium and long

wavelength cone mechanisms (Lee, Martin, & Valberg, 1989). Hence, under conditions

of isoluminant chromatic stimulation, it appears that input from the M pathway

dominates the 2F ssVEP component.

2.7.4 Effects of motion on ssVEP

Only a small number of ssVEP investigations have used visual motion to probe

M processing, presumably due to difficulties in isolating signals associated with motion

and pattern sensitive mechanisms. Snowden, Ullrich, and Bach (1995) compared

responses to random dot stimuli that either alternated between incoherent and coherent

motion, or reversed in motion direction. There were different effects of motion speed

and luminance contrast for the two stimulation conditions. For coherent motion onset,

there was a low-pass relationship between motion speed and ssVEP amplitude at F, with

no clear response at 2F. Consistent with a P generator, the CRF increased

monotonically with no sign of saturation. For motion reversal, there was a band-pass

relationship between motion speed and ssVEP amplitudes at F and 2F. Consistent with

an M generator, the CRF saturated at approximately 20% contrast.

Heinrich and Bach (2003) showed that motion reversal ssVEPs (at the reversal

rate, i.e.; twice the stimulus frequency) are susceptible to directionally specific pre-

adaptation, which further supports the argument that they are a true reflection of motion

processing, as opposed to pattern processing. In summary, current evidence suggests

that motion reversal ssVEPs are much better suited to targeting M-driven motion

processing mechanisms than motion-onset ssVEPs.

2.7.5 Summary of afferent contributions to ssVEP

The studies reviewed in this section suggest that for achromatic stimulation, the

M pathway dominates ssVEPs to diffuse, rapidly flickering stimuli, whereas the P

pathway dominates ssVEPs to high spatial frequency, low temporal frequency stimuli

(Nelson & Seiple, 1992; Rudvin & Valberg, 2006). For isoluminant chromatic

stimulation, responses at the fundamental frequency appear to be dominated by P and K

input, and responses at 2F appear to reflect non-linear summation of L- and M-cone

inputs to the M pathway (Barboni et al., 2013). For motion reversal stimulation, 2F

27

ssVEP signals appear to reflect M input to motion sensitive cortical regions (Heinrich &

Bach, 2003; Snowden et al., 1995).

A major advantage of ssVEP over transient VEP is that high signal to noise

amplitude responses can be achieved in much shorter recording sessions (Regan, 1973).

However, a disadvantage is that rapid, periodic visual stimulation does not allow brain

activity to recover from the previous stimulus before the next one appears, so the

contributions of multiple visual areas overlap in the averaged waveform (Vialatte et al.,

2010). This suggests that ssVEP is not well suited for comparing the initial volleys of

M, P and K afferent input to V1.

2.8 The m-sequence approach

2.8.1 Introduction to m-sequences and Wiener kernel analysis

Nonlinear temporal analyses offer an alternative approach to separating afferent

inputs to VEPs. The impulse response function characterises temporal responses of a

linear system, by decomposing the stimulus into discrete time bins, such that each

impulse response can be considered independently. In a nonlinear system, successive

impulse responses cannot be considered independently, so one needs to take nonlinear

temporal interactions into account. There are several different mathematical approaches

to studying nonlinear systems (reviewed, Klein, 1992; Marmarelis & Marmarelis, 1978;

Sutter, 1992; Victor, 1992). In this section, we review studies that used an m-

sequence/Wiener kernel approach to non-linear VEP analysis. The VESPA approach is

reviewed in Section 2.9.

An m-sequence (maximum length sequence) is a type of pseudorandom binary

sequence of length 2m -1 that contains all of the possible configurations of m

consecutive elements exactly once, except for the all zero configuration (Golomb,

1967). Sutter developed a method to cross-correlate visual responses with the m-

sequence based on Fast Walsh transform (Sutter, 1992). This allows for rapid extraction

of first and higher order Wiener kernel responses from a single cross-correlation, so that

one can investigate the memory (i.e. response recovery) of the visual system to a full

range of binary stimulation.

The first-order kernel (K1) is analogous, but not identical to the conventional

onset-offset VEP response, or the impulse response function in a linear system. The first

slice of the second order kernel (K2.1) measures non-linear temporal responses over one

28

video frame, and is analogous, but not identical to the conventional contrast reversal

VEP. The second slice of the second order response (K2.2) measures non-linearity over

two video frames (Sutter, 1992). For pattern reversal non-linear VEPs, there is not much

power in the K1 VEP response, but there are robust second order responses (Baseler &

Sutter, 1997).

Transient VEP and ssVEP experiments have often used stimuli that activate

large areas of the visual field. Yet the M and P pathways are likely to respond

differently to stimuli presented at fixation or in the periphery (Dacey, 1993). An

advantage of pseudo-random stimulation is that independent multifocal responses can

be measured by stimulating each patch with temporally decorrelated versions of the m-

sequence (Sutter, 1992). By scaling the patch size with linear approximations of the

cortical magnification factor (Horton & Hoyt, 1991), it is possible to match the signal to

noise ratio across the visual field (Baseler, Sutter, Klein, & Carney, 1994).

2.8.2 Effects of luminance contrast on non-linear VEPs

In 1997, two separate research groups published papers that inferred M and P

contributions to VEP signals based on the CRFs of second-order non-linear kernel

responses (Baseler & Sutter, 1997; Klistorner, Crewther, & Crewther, 1997). Baseler

and Sutter (1997) presented a contrast-reversing multifocal checkerboard stimulus,

where each checkerboard reversed in de-correlated pseudorandom m-sequences

(reversal rate 66.7 reversals/s, i.e., 33.3Hz). The K2.1 response could be decomposed

into C1 and C2 components, based on the effects of eccentricity on the K2.1P60 (60-

80ms) and K2.1P90 (75-115ms) peak latencies. The effects of spatial frequency (i.e.

check size), luminance and colour on C1 and C2 were consistent with M and P

generators respectively. The authors suggested that C1 may reflect M input to V1 input

layer 4Cα and that C2 may reflect a combination of P input arriving at layer 4Cβ, as

well as processing in V1 layers 2 and 3.

When the visual system is stimulated at higher temporal frequencies, M and P

signatures are more cleanly separated into different slices of the second order non-linear

VEP. Klistorner, Crewther and Crewther (1997) presented a diffuse onset-offset

multifocal stimulus, where the binary luminance of each patch alternated along de-

correlated m-sequences (updated at 66.67 Hz). CRFs were plotted for the peak-to-peak

amplitudes of the first and second order kernels. Modelling of the K1 waveform

suggested it reflects the sum of M and P inputs. Consistent with a M generator, the

29

K2.1N70P100 waveform had high contrast gain and saturated at around 43% contrast

(semi saturation coefficient ~ 20%). Consistent with a P generator, the main K2.2N90P130

waveform gradually increased in amplitude with contrast, and the CRF showed no sign

of saturation. The K2.3 waveform had a similar CRF to the K2.2 response, but the

signal to noise ratio was lower. Differences in the latencies of the major K2.1 and K2.2

waveforms are consistent with the latency advantage of M input to V1 (Nowak, Munk,

Girard, & Bullier, 1995).

In order to establish the saturation properties of the K2.1 and K2.2 waveforms,

Jackson et al. (2013) carried out Naka-Rushton fits of the CRFs. Consistent with

Klistorner et al. (1997), the K2.1 N60P90 waveform had high contrast gain and rapid

saturation. The K2.2N95P130 waveform had lower contrast gain and a much higher semi-

saturation coefficient. There were some small departures from Klistorner et al. (1997),

which could possibly be attributed to stimulating at a higher frame rate (75 Hz as

opposed to 67 Hz). An early waveform (K2.2N65P75) became apparent in the K2.2

response, with a similar CRF to the K2.1 response. This suggests that the K2.2N65P75 and

K2.2N95P130 waveforms can be traced back to M and P origins respectively. Evidence

from a study that used sparse ternary stimuli suggest that the CRFs for the K2.0

Volterra kernel response are also consistent with an M pathway generator (Maddess,

James, & Bowman, 2005).

Brown, Corner, Crewther, and Crewther (Submitted for Publication) found that

K2.1 amplitudes correlate negatively with psychophysically measured flicker fusion

thresholds. That is, individuals with higher flicker fusion frequencies in general possess

smaller amplitude second order first slice (K2.1) peak amplitudes. However, amplitudes

of the main K2.2 waveform do not correlate with flicker fusion thresholds. Previous

studies have shown that the human mechanisms for detecting high temporal frequency

stimulation are consistent with those observed in cat Y cells (Burbeck & Kelly, 1981).

Brown et al. interpreted these results in terms of more rapid neural recovery, and related

higher flicker fusion to increased neural efficiency of the M but not the P system.

2.8.3 Effects of spatial frequency on non-linear VEPs

The effects of spatial frequency on non-linear VEPs have been studied using

contrast reversing checkerboards (Momose & Kasahara, 2003), and sinusoidal gratings

(Araújo, Souza, Gomes, & Silveira, 2013; Momose, 2010). As spatial frequency

increases from 0.5 to 4.5 cycles per degree (Momose & Kasahara, 2003), or 0.5 to 9

30

cycles per degree (Momose, 2010), K2.1 amplitudes decrease and K2.2 amplitudes

increase. Although the authors only reported on the main K2.2 waveform (Momose &

Kasahara, 2003), an early K2.2 N65P75 waveform was observed in response to low, but

not high spatial frequency stimulation, which is consistent with evidence that the early

and late K2.2 waveforms have different generators (Jackson et al., 2013). Araújo et al.

(2013) performed more detailed comparisons of CRFs for the K2.1 and K2.2 responses

at different spatial frequencies, and found that under most conditions these waveforms

reflect M and P inputs respectively, but that at high spatial frequencies, there are some P

contributions to the K2.1 response. Based on what is known from primate physiology

(Derrington & Lennie, 1984), the effects of spatial frequency on non-linear VEPs

provides further evidence that under conditions with low spatial frequency or diffuse

stimulation, the K2.1 and early K2.2 waveforms originate from M input, and the late

K2.2 waveform originates from P input.

2.8.4 Effects of colour on non-linear VEPs

Klistorner, Crewther, and Crewther (1998) demonstrated dissociation between

the effects of diffuse red stimulation and achromatic stimulation on non-linear VEP

responses, with chromatic responses in both the K2.1 and K2.2 kernels to red, but not

green stimulation. Crewther and Crewther (2010) used non-linear VEP to investigate

responses to diffuse colour saturation (i.e.; surface colour) and to coloured line patterns

(i.e.; form colour), at 30% luminance contrast. For the diffuse colour conditions,

chromatic saturation had the greatest effects on K2.1 amplitudes. For the

appearance/disappearance of patterned stimuli, there was a clear dominance in the first

order kernel, with relatively small responses in the K2.1 and K2.2 kernels. Interestingly,

the K2.1 responses to diffuse stimulation were spectrally dependent, with stronger

responses for blue and red stimulation than for yellow green and cyan stimulation. By

contrast, K1 responses to pattern colour were robust for all colours.

These findings indicate that surface and edge colour representations have

different non-linear temporal structures. Although the strong K2.1 response to diffuse

colour is indicative of a system that recovers rapidly from stimulation, primate

physiological evidence suggests that the effects of diffuse red and blue colour on VEP

amplitudes are generated in V1 layers 2/3, as opposed to the M of P layers of the LGN

(Givre, Arezzo, & Schroeder, 1995). Cells within V1 cytochrome oxidase blobs respond

most strongly to diffuse red and blue inputs (Dow & Vautin, 1987), prefer high

31

temporal frequency stimulation (Shoham, Hübener, Schulze, Grinvald, & Bonhoeffer,

1997) and receive M, P and K input (Edwards et al., 1995). In summary, the studies

reviewed in this section indicate that VEP responses to diffuse surface colour are better

explained in terms of converging inputs from M, P and K cells to the cytochrome

oxidase blob centres in V1, than separate inputs from the subcortical afferent pathways.

2.8.5 Cortical sources of non-linear VEP signal

Slotnick, Klein, Carney, Sutter, and Dastmalchi (1999) performed dipole source

localisation of K2.1 responses to contrast reversing multifocal checkers. They showed

that the dipole polarity, retinotopic organisation and continuity across the horizontal

meridian are consistent with a V1 generator. K2.2 responses also appear to originate

from V1 (Crewther, Brown, & Hugrass, 2016; Fortune & Hood, 2003). Differences in

multifocal K2.1 and K2.2, and transient VEP responses to contrast reversing

checkerboards appear to depend heavily on the temporal frequency of stimulation

(Fortune & Hood, 2003). As m-sequence stimulation is slowed, the waveforms become

more similar to those obtained with transient VEP. These results suggest that while fast

m-sequence stimulation primarily activates V1, transient VEPs are more heavily

influenced by extrastriate sources. However, for unpatterned multifocal stimuli, K1 and

K2.1 MEG signals recorded from MT/V5 exhibit only a 1-2ms latency delay, relative to

responses from V1 (Crewther et al., 2016). This may reflect input from an M-driven

colliculo-pulvinar route to MT/V5, which plays a critical role in the development of the

primate dorsal stream (Bridge, Leopold & Bourne, 2016).

2.8.6 Summary of afferent contributions to non-linear VEP signals

The studies reviewed in this section showed that fast m-sequence stimulation

allows for relatively clean separation of M and P afferent inputs into the first and second

slices of the second order non-linear VEP responses. The K2.1 and early K2.2

waveforms appear to originate from M input to V1, and the main K2.2 waveform

appears to originate from P input to V1 (Jackson et al., 2013; Klistorner et al., 1997).

The effects of chromatic stimulation on the non-linear VEP kernels are somewhat more

difficult to interpret (Crewther & Crewther, 2010). Future studies that stimulate with S-

cone isolating multifocal stimuli may help to clarify M, P and K contributions to

different chromatic processing mechanisms in V1.

32

2.9 The VESPA approach

2.9.1 Introduction to VESPA

VESPA (visual evoked spread spectrum analysis) is a different approach to

measuring responses to stochastic stimulation. The contrast of a checkerboard stimulus

is rapidly modulated by a stochastic signal. This is used to estimate the linear impulse

response function (i.e. the first order Volterra kernel) of the visual system using least of

squares estimation (Marmarelis and Marmarelis, 1978). Similar to the non-linear VEP

techniques described above, the contrast of the stimulus is updated rapidly at the frame

rate of the display, yet the stimulus is not limited by the same temporal constraints,

which gives VESPA more flexibility in terms of stimulus design. Unlike binary m-

sequence stimulation, VESPA modulates the stimulus contrast over a wide range of

narrowly spaced levels. The resulting signals are similar, but not identical to transient

pattern reversal VEP waveforms, with similar C1 and P1 responses recorded from

occipital sensors (Murphy et al., 2012).

2.9.2 The effects of luminance and spatial frequency on VESPA signals

Lalor and Foxe (2009) designed different checkerboard VESPA stimuli to target

the M and P pathways (check size =.65 degree, temporal frequencies ranged from 1 to

30Hz). Based on the CFRs of primate M and P cells (Kaplan & Shapley, 1986), it was

proposed that the VESPA signal would be dominated by M and P input when the range

of modulation was restricted to low or high contrast. VESPA signals for the full-range

(0-100% contrast) and high-contrast (32-100%) conditions were highly similar, which

indicates that both are dominated by P (and possibly K) inputs. VESPA responses to

low contrast stimuli (0-10%) were lower in amplitude, and there was a slight C1 latency

delay of approximately 4-5ms.

Murphy et al. (2012) compared C1 and P1 responses, as recorded both with

transient pattern reversal VEPs (100% contrast) and VESPA (0- 100% contrast). For

VESPA, C1 and P1 amplitudes were maximal for electrode site OZ, whereas for

transient VEPs, larger P1 amplitudes were also present for occipto-temporal and parietal

electrode sites. Likewise, transient VEP C1, VESPA C1, and VESPA P1 amplitudes

were highly inter-correlated, suggesting a common generator, whereas the transient

VEP P1 responses did not correlate with the other measures. There was no low contrast

33

condition in this experiment, but it would be interesting for future studies to compare

the sources of transient VEP and VESPA responses to low contrast stimulation.

2.9.3 Summary of afferent contributions to VESPA signal

In summary, both C1 and P1 full-range and high-contrast VESPA responses

appear to be dominated by P (and possibly K) input to the striate cortex, whereas C1

and P1 low-contrast VESPA responses appear to reflect M input to the striate cortex. If

this signal reflects M input to V1, the latency delay for low-contrast VEP would appear

inconsistent with evidence that M input reaches V1 faster than P input (Nowak et al.,

1995); however single cell onset latencies are slower for stimuli with low contrast

(Maunsell et al., 1999). Hence, due to differences in stimulus contrast for the putative M

and P waveforms, it is difficult to ascribe differences in C1 latencies to differences in

the latencies of M and P inputs to V1. To date, studies of M and P dominated VESPA

responses have only reported the first-order Volterra kernel (i.e. the impulse response);

however future studies that analyse higher order kernels may prove useful in further

separating M and P contributions to the VESPA signal.

2.10 Discussion and Conclusions Many studies have been conducted to investigate M, P and K inputs to scalp

recorded VEPs. This is not an easy task, due to interactions between the afferent input

channels at the cortical level (Vidyasagar et al., 2002), and the presence of K

subpopulations with similar achromatic CRFs to those of the M and P streams (White et

al., 2001). Hence, the practice of tracing VEPs recorded from human scalps to their

subcortical origins, based on CRFs alone has been strongly criticized (Skottun, 2014).

However, converging evidence from experiments that have modulated the colour,

spatial frequency, eccentricity and temporal frequency of stimulation suggest it is

possible to identify components of VEP signals that are dominated by input from the M,

P and K pathways.

Signals that reflect P input to the cortex have been identified with each of the

VEP approaches discussed in this review. For instance, for high contrast achromatic

stimulation with transient VEP or VESPA, the C1 waveform appears to reflect P input

to V1 (Lalor & Foxe, 2009; Murphy et al., 2012). Likewise, P input appears to dominate

ssVEP responses to flickering stimuli with low temporal frequency, high luminance

contrast or isoluminant red-green levels (Gomes et al., 2006; Nelson & Seiple, 1992).

34

Analysis of the non-linear temporal structure of VEPs has shown that the effects of

contrast and spatial frequency on the main K2.2 waveform suggest that it originates

from P sources (Klistorner et al., 1997; Maddess et al., 2005; Momose, 2010).

As stated in Section 2.4, K cells form at least three physiologically distinct

pathways, which have overlapping sensitivities to the M and P pathways (Hendry &

Reid, 2000). Therefore, it is unlikely that K contributions to scalp-recorded VEPs would

manifest as a unitary component, or that they can be clearly disambiguated from M and

P dominated VEP signatures. By using s-cone isolating stimuli, some transient VEP and

ssVEP studies have inferred contributions from the sub-population of K cells that

transmits s-cone input to the cortex (Barboni et al., 2013; Gomes et al., 2006). K

thalamocortical neurons are only slightly more numerous than those of the M pathway,

but lack the power of M cells to dominate cortical circuits owing to their high

divergence factor and large axons (Hendry & Reid, 2000). The sluggish temporal

responses of K cells (Irvin, Casagrande, & Norton, 1993) would suggest that it is

unlikely that they contribute to the same components of the K2.1 and early K2.2

waveforms as M cells. The presence of K cells makes it difficult to cleanly separate M

and P subcortical inputs to VEPs. Future macaque studies that characterise VEPs arising

from the K LGN layers, or K inputs to V1 may provide evidence that would help

researchers to separate inputs from the M, P and K pathways to VEPs.

Based on the evidence reviewed here, the degree to which M input is manifest in

scalp-recorded VEP signals depends strongly on the stimulation approach. For transient

pattern-onset VEPs, the apparent absence of M contributions to the C1 waveform

indicates it is difficult to measure the initial volley of M input to the cortex (Foxe et al.,

2008). The low-contrast VESPA approach produces a waveform that is likely to

originate from M input to the striate cortex (Lalor & Foxe, 2009), however its latency is

delayed relative to the full-range VESPA signal. For ssVEP, responses to stimuli with

low contrast, low spatial frequency, and high temporal frequency appear to be

dominated by the M pathway (Nelson & Seiple, 1992). Fast m-sequence stimulation

allows for relatively clean isolation of M signals into the K2.1 and early K2.2

waveforms, particularly under conditions of low contrast or diffuse stimulation. A

major advantage of the m-sequence technique over the others reviewed here is that it is

possible to directly compare the timing of putative M and P responses under the same

stimulation conditions.

35

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Chapter 3: A review of Non-Linear Visual Evoked Potential Research

into Contributions from the Human M and P pathways to Cortical

Vision

3.1 Chapter guide Hugrass, L., & Crewther, D. (In Submission). A review of non-linear visual

evoked potential research into contributions from the human M and P pathways to

cortical vision.

This chapter comprises a re-formatted version of the article cited above, which

has been submitted to Experimental Brain Research as a review paper. In the previous

chapter, I reviewed a range of non-invasive VEP techniques for that are used to

investigate the human M and P pathways, including transient VEP, ssVEP, nonlinear

VEP and VESPA. Of the available techniques, non-linear analysis of multifocal flash

evoked VEP provides the cleanest separation of putative M and P signals. The literature

review presented in the current chapter provides an overview of how this technique has

been applied to study the roles of the human M and P pathways in visual processing at

the cortical level.

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3.2 Abstract Due to mixing of inputs from the afferent visual pathways, and a restriction

largely to non-invasive recording techniques, it has proved difficult to identify

magnocellular (M) and parvocellular (P) contributions to signals arising from the human

visual cortex, let alone contributions from the koniocellular (K) system. About 20 years

ago, non-linear analyses of visual evoked potentials (VEPs) revealed separable

signatures of putative M and P origins based on their contrast response functions and

latencies. Although several other VEP methods for studying M and P input to the cortex

have been proposed, the nonlinear analysis technique provides the cleanest separation of

the two pathways. Here we review studies that have applied this technique to investigate

M and P contributions to human vision. Such research is important for understanding

how temporal interactions within the two visual pathways contribute to individual

differences and abnormalities in visual processing.

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3.3 Introduction More than 20 years ago, Klistorner, Crewther, and Crewther (1997) reported that

the magnocellular (M) and parvocellular (P) afferent pathways to the primary visual

cortex can be studied separately through non-linear analysis of multifocal visual flash

evoked potentials (VEPs). There are several reasons why it is important to develop non-

invasive techniques to investigate the functions of the M and P pathways in isolation.

Firstly, primate studies show that the afferent pathways contribute in different ways to

foreground-background segmentation of the visual scene, so studying their

contributions to VEPs may shed light on object processing mechanisms in humans

(Bullier, 2001; Hupé et al., 1998). Furthermore, M input activates the cortex

approximately 10-30ms earlier than the initial wave of P input (Nowak, Munk, Girard,

& Bullier, 1995), a phenomenon termed the magnocellular advantage (Laycock,

Crewther, & Crewther, 2007). In addition, M pathway dysfunction may contribute to

various clinical conditions including dyslexia (Lovegrove, 1996; Stein & Walsh, 1997),

schizophrenia (Butler et al., 2006) and autism (McCleery, Akshoomoff, Dobkins, &

Carver, 2009).

We begin this review with a description of the major afferent pathways to the

visual cortex (§ 3.4), and an introduction to non-linear temporal analysis of VEPs (§

3.5). Next, we present an overview of evidence that temporal non-linearities in the M

and P pathways contribute to separate slices of the second order VEP kernel (§ 3.5.2),

and review studies that have applied this technique to research the human M and P

pathways (§ 3.6). We finish the review with some suggestions for future directions in

understanding contributions from the major afferent pathways to visual processing and

visual dysfunction (§ 3.7 and 3.8).

3.4 Primate physiology: Characteristics of the M and P pathways Primate research into the major visual pathways has been reviewed in detail

elsewhere (Hendry & Reid, 2000; Kaplan, 2014; Nassi & Callaway, 2009). M cells,

served by large diameter axons, have fast conduction speeds, a preference for high

temporal frequency stimulation, low chromatic contrast sensitivity, and luminance

contrast response functions (CFRs) with high contrast gain and rapid saturation. P cells

have slower conduction speeds, a preference for low temporal frequency input, high

red/green chromatic sensitivity, and non-saturating achromatic CRFs with low gain

(Derrington & Lennie, 1984; Kaplan & Shapley, 1986; Livingstone & Hubel, 1988).

47

Koniocellular (K) cells (konio: Greek, dust) were initially ignored by neuroanatomists

and neurophysiologists alike, who focused on the larger M and P cells when

characterising the visual system. However, it is now known that a subset of K

projections plays an important role in transmitting short-wavelength (blue) cone input to

the cortex. K cells have slow conduction speeds and heterogeneous CRFs, intermediate

to those of M or P cells (Casagrande, 1994; Ghodrati, Khaligh-Razavi, & Lehky, 2017;

Martin, White, Goodchild, Wilder, & Sefton, 1997; White, Solomon, & Martin, 2001),

presumably because the class represents multiple cell types that exhibit calbindin

immunoreactivity.

The role of the cortical dorsal and ventral streams has captured the attention of

scientists for some time. Initially related to “where” and “what” functions respectively,

the publications of Livingstone and Hubel (1988; 1987) caused an explosion of interest

as to whether the M pathway drove the dorsal stream and the P pathway drove the

ventral stream, or whether the situation was rather more complicated. The latter view

turned out to be correct. The current understanding is that after the afferent pathways

reach the cortex, the ventral ‘vision for perception’ stream receives predominantly P

input, and the dorsal ‘vision for action’ stream receives predominantly M input; yet a

substantial number of cells receive converging input from the different afferent

pathways (Nassi & Callaway, 2009; Nealey & Maunsell, 1994; Vidyasagar,

Kulikowski, Lipnicki, & Dreher, 2002). This means that it is not easy to separate M, P

and K contributions to cortical visual processing mechanisms.

Due to fast conduction speeds, the initial feed-forward volley of M input is

sufficiently rapid to allow feedback from extra-striate regions to modulate V1 responses

to P input (Bullier, 2001; Kveraga, Boshyan, & Bar, 2007; Laycock et al., 2007).

Furthermore, different rates of recovery from stimulation mean it is likely that M and P

cells contribute to different temporal slices of non-linear VEP responses (Klistorner et

al., 1997). Due to the heterogeneity of K receptive field properties, physiological

characterisation of the K pathways has remained elusive. Therefore, this review focuses

mostly on the study of non-linear VEP signatures of M and P processing, and then later

revisits possible K contributions.

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3.5 The non-linear VEP approach

3.5.1 Introduction to non-linear temporal analysis of VEPs

Although many VEP techniques, such as transient VEP, steady state VEP and

VESPA have been used to infer M and P contributions to human cortical responses

(Ellemberg, Hammarrenger, Lepore, Roy, & Guillemot, 2001; Lalor & Foxe, 2009;

Nelson & Seiple, 1992; Zemon & Gordon, 2006), there are several advantages of using

multifocal m-sequence stimulation and non-linear analysis techniques (Baseler &

Sutter, 1997; Klistorner et al., 1997; Momose, 2010). Firstly, due to the different

distributions of M and P cells for central and peripheral vision, and individual

differences in cortical anatomy, multifocal stimulation minimizes the blurring effects of

eccentricity on the temporal characteristics of VEP responses (Baseler, Sutter, Klein, &

Carney, 1994). Secondly, the visual system is temporally non-linear (i.e.; responses to a

train of stimuli cannot be estimated based on responses to each stimulus on its own), so

it is informative to study non-linear temporal responses to signals presented on

successive video frames (Victor, 1992). Finally, unlike other VEP methodologies that

rely on carefully selected stimulus parameters to target the M and P systems (i.e.; low

achromatic luminance contrast, or isoluminant chromatic stimulation), non-linear VEP

allows for direct comparison of putative M and P responses to the same stimuli.

Sutter’s development of the VERIS system and Fast Walsh Transform analysis

techniques brought multifocal VEPs and ERGs into mainstream ophthalmological

practice (Sutter, 1992), and in doing so, created a reliable platform for the scientific

study of the temporal structure of evoked responses. In order to equalize the signal to

noise ratio across the visual field (Baseler et al., 1994), the multifocal patch sizes can be

scaled with a linear approximation of the human cortical magnification factor (Horton

& Hoyt, 1991). Each of the patches are stimulated with temporally de-correlated,

pseudorandom, maximum-length sequences (m-sequences) (Golomb, 1967), which are

usually updated at the frame rate of the display screen. The stimulus patches either

alternate between binary levels of diffuse luminance (e.g., Klistorner et al., 1997) or

reverse in contrast (e.g., Baseler & Sutter, 1997). This enables fast Wiener kernel

decomposition of independent VEPs for each of the multifocal patches (Klein, 1992;

Sutter, 1992). For onset/offset presentation, the first order kernel (K1) is analogous, but

not identical to the conventional onset VEP response, or the impulse response function

of a linear system. The first and second slices of the second order kernel (K2.1 and

49

K2.2) measure non-linear temporal responses over one and two video frames

respectively (Sutter, 1992). An example to illustrate typical K1, K2.1 and K2.2

waveforms is provided in Figure 3.1.

Figure 3.1 Illustration of typical (a) K1, (b) K2.1 and (c) K2.2 waveforms from a single observer, as measured from the central patch of a multifocal VEP stimulus, at 70% luminance contrast, and a display update rate of 60Hz.

3.5.2 The multifocal VEP approach to identifying M and P inputs to cortical

vision

Baseler and Sutter (1997) stimulated with a multifocal dartboard. Individual

patches were filled with contrast reversing checkerboards, at an m-sequence base rate of

66.7 reversals/s, i.e., 33.3Hz. For pattern reversal stimulation, there is no substantial

first order response, yet there are robust second order responses. The K2.1 waveform

could be decomposed into C1 and C2 components, based on differences in the latency

of K2.1P60 (60-80ms) and K2.1P90 (75-115ms) peaks at different eccentricities. The first

peak was small for the central patch, increased in amplitude outside of the fovea, and its

latency did not change greatly with eccentricity. The amplitude and latency of the

50

second peak dropped for representations outside of the fovea. The effects of luminance

contrast, spatial frequency and colour on the C1 and C2 components are consistent with

generators in the M and P pathways respectively (see also, Laron, Cheng, Zhang, &

Frishman, 2009).

Presenting diffuse multifocal stimuli at higher temporal frequencies results in

cleaner separation of M and P VEP signatures into different slices of the second order

response. Klistorner et al. (1997) presented diffuse (i.e. unpatterned) multifocal stimuli.

Temporally de-correlated m-sequences (updated at 66.7 Hz) modulated the binary

luminance levels for each patch. Temporal luminance contrast was varied from 1% to

94%, and CRFs were plotted for peak-to-peak waveform amplitudes for the K1, K2.1,

K2.2 and K2.3 kernels. Modelling of the K1 waveform suggests that it reflects the sum

of two components, one with high contrast gain and rapid saturation (putative M) and

the other with lower contrast gain but no saturation (putative P). Consistent with an M

origin, the K2.1N70P100 waveform had high contrast gain and saturated at around 43%

contrast (semi saturation coefficient ~ 20%). Consistent with a P origin, the main K2.2

N90P130 waveform gradually increased in amplitude with contrast, and the CRF showed

no sign of saturation. The third slice of the second order response (K2.3) had a similar

CRF to the K2.2 response, but the signal to noise ratio was lower. The results of a

related study that used a sparse, ternary stimulation method suggest that the CRF of the

K2.0 Volterra kernel response is also consistent with an M pathway generator

(Maddess, James, & Bowman, 2005).

In order to establish the saturation properties of the K1, K2.1 and K2.2 CRFs,

Jackson et al. (2013) carried out Naka-Rushton fits of the waveforms in adults with

normal vision (see §3.6.2.1 for their analyses of individual differences). Consistent with

Klistorner et al. (1997), the K2.1N60P90 waveform had high contrast gain and rapid

saturation, whereas the K2.2N95P130 waveform had lower contrast gain and a much

higher semi-saturation coefficient. There were some small departures from Klistorner

et al. (1997), which could possibly be attributed to stimulating at a higher frame rate

(75Hz as opposed to 67Hz). An early waveform (K2.2N65P75) became apparent in the

K2.2 response, with contrast gain levels similar to those of the K2.1 response. The

semi-saturation coefficient was lower for the K2.2N65P75 waveform than for the K2.1

waveform; however with increasing contrast, the early K2.2 response is likely to have

51

been dominated and distorted by the later (and larger) K2.2 waveform. This may have

affected estimation of its semi-saturation coefficient.

Crewther, Brown, and Hugrass (2016) used magnetoencephalography (MEG) to

localize K2.1 and K2.2 responses on the cortical surface. As with the previous EEG

studies that stimulated at higher frame rates (Jackson et al., 2013; Klistorner et al.,

1997), they found that K2.1 response amplitudes were already saturated at 25%

contrast, whereas the major waveform for the K2.2 response increased with stimulus

contrast. As expected based on previously reported EEG waveform latencies, peak

power in the MEG waveforms was approximately 30ms earlier for the K2.1 response

than for the K2.2 response. Consistent with EEG source localisation studies of second

order multifocal VEP responses (Fortune & Hood, 2003; Slotnick, Klein, Carney,

Sutter, & Dastmalchi, 1999), the second order kernel waveforms produced strong

responses in V1. Furthermore, there was very early cortical activation both in V1 at 42

and 43ms and MT+ at 43 and 44ms, for the K1 and K2.1 waveforms respectively.

To further test the theory that K2.1 responses originate from the M pathway,

Brown, Corner, Crewther, and Crewther (Submitted for Publication) investigated the

relationship between achromatic flicker fusion sensitivity (as measured with a 4AFC

flickering LED setup) and K2.1 and K2.2 peak amplitudes (as measured with diffuse

low and high contrast multifocal VEP stimuli, 75Hz display). K2.1 waveform

amplitudes increased with flicker fusion thresholds. There were some correlations

between early (putative M) K2.1 amplitudes and flicker fusion thresholds, but the main

(putative P) K2.2 waveform amplitudes were not correlated with flicker thresholds.

Previous studies have shown that the human mechanisms for detecting high temporal

frequency stimulation are consistent with those observed in cat Y cells, which are

analogous to primate M cells (Burbeck & Kelly, 1981). Hence, Brown et al.’s results

suggest that K2.1 amplitudes can be seen as a measure of M temporal efficiency; faster

achromatic flicker frequencies are indicative of an M system that recovers quickly from

previous stimulation, which in turn reduces the power of VEP responses in the second

order kernel.

The effects of spatial frequency on K2.1 and K2.2 non-linear VEP responses

have also been studied using contrast reversing checkerboards (Momose & Kasahara,

2003), and sinusoidal gratings (Momose, 2010, Araújo, Souza, Gomes, & Silveira,

2013). Under conditions when the m-sequence is updated every 10ms (100 reversals/s,

52

i.e.; 50Hz), K2.1 amplitudes decrease and K2.2 amplitudes increase when the spatial

frequency of patterned stimulation increases from 0.5 to 4.5 cycles per degree (Momose

& Kasahara, 2003), or 0.5 to 9 cycles per degree (Momose, 2010). Although the authors

only reported on the later K2.2 waveform (Momose & Kasahara, 2003), there was a

small, early K2.2 N65P75 waveform that was observable under conditions of low, but not

high spatial frequency stimulation. This is consistent with evidence that the early and

late K2.2 waveforms have different generators (Jackson et al., 2013); however, when

the stimuli have high spatial frequencies, there appears to be some P input to the K2.1

waveform (Araújo et al., 2013). Based on what is known from primate physiology

(Derrington & Lennie, 1984), the effects of spatial frequency on VEPs provide further

evidence that the K2.1 and early K2.2 waveforms originate from M input, and the late

K2.2 waveform originates from P input.

Some researchers have criticized attempts to separate M and P contributions to

scalp-recorded VEPs (Skottun, 2014; Skottun & Skoyles, 2010). One potential issue is

that K cells have intermediate CRFs to P and M cells (Hendry & Reid, 2000; Xu et al.,

2001). Given the heterogeneity of K receptive field properties it is conceivable that they

could contribute to the K2.1 and K2.2 responses, however the uniformity of the main

K2.1 and K2.2 waveforms with increasing contrasts suggests they are more likely to

originate from a neural population with homogenous CRFs (Klistorner et al., 1997). K

cells tend to have low discharge rates, relatively long response latencies to light, lower

peak firing rates (Irvin, Norton, Sesma, & Casagrande, 1986) and lower temporal

frequency cut-offs than M cells (Solomon, White, & Martin, 1999), so it seems unlikely

that they would contribute substantially to K2.1 responses (i.e. response recovery over

one video frame). Finally, K cells are up to 10 times less numerous than P cells, so if

there is a K contribution to the main K2.2 nonlinearity, it is likely to be small.

In summary, several studies have investigated the non-linear temporal structure

of visual evoked potentials (Jackson et al., 2013; Klistorner et al., 1997; Maddess et al.,

2005; Momose, 2010; Momose & Kasahara, 2003). Comparisons of the CRFs, spatial

frequency preferences and peak latencies against primate M and P physiology have

provided converging evidence that within a range of temporal stimulation rates (~50 –

75Hz), the scalp-recorded K2.1 and early K2.2 responses are consistent with an M

pathway generator, and the main K2.2 response is consistent with a P pathway

generator.

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3.6 Applications Since Klistorner et al.’s (1997) study, non-linear flash VEP has been applied in

several different investigations of the human M and P pathways. This section reviews

literature that has used non-linear VEPs to investigate topics including the development

of visual processing (§3.6.1), visual processing differences that are linked to

developmental disorders (§3.6.2), the effects of omega-3 fatty acid dietary

supplementation on visual processing (§3.6.3), and the effects of colour on cortical

responses (§3.6.4).

3.6.1 Developmental changes in M and P function

Studies of primary school aged children have reported developmental changes in

the non-linear temporal structure of VEP responses (Crewther, Crewther, Barnard, &

Klistorner, 1996; Crewther, Crewther, Klistorner, & Kiely, 1999). K2.2 waveforms

shared the same general features for all age groups. By contrast, two small, short latency

K2.1 peaks are present for 6-8 year olds, yet there is only a single, much larger, short

latency peak for older children and adults. The authors suggested that in young children,

different M projections produce two early K2.1 peaks, which converge into a single

early peak as the visual system develops (Crewther et al., 1999).

On the surface, these findings appear to be inconsistent with the results of a

study that used conventional VEPs (as opposed to non-linear VEPs) to investigate CRFs

in infants from birth to 52 weeks (Hammarrenger et al., 2003). A previous study in

adults showed that the CRFs of the N1 (50-90ms) and P1 (80-140ms) VEP components

are consistent with P and M origins respectively (Ellemberg et al., 2001).

Hammarrenger et al. (2003) showed that the P1 component appears earlier in life and

levels off faster with age than the N1 component. Based on these findings, the authors

argued that the M pathway reaches functional maturity earlier than the P pathway.

However, based on the timing of the N1 and P1 components, it is likely that the P1

reflects both striate and extrastriate processing (Di Russo, Martínez, Sereno, Pitzalis, &

Hillyard, 2002; Hall et al., 2005). By comparison, fast m-sequence nonlinear VEP

responses appear to be less strongly influenced by extrastriate feedback (Fortune &

Hood, 2003).

Hammarrenger et al.’s (2003) argument that the M pathway develops earlier

than the P pathway is supported by studies into the development of achromatic and

chromatic contrast sensitivities (Dobkins, Anderson, & Lia, 1999; Norcia, Tyler, &

54

Hamer, 1990), low and high spatial frequency sensitivities (Norcia et al., 1990), and

peripheral and central vision (Dobkins et al., 1999). However, Crewther et al.’s (1999)

nonlinear VEP study suggests that M function continues to develop in school aged

children. A plausible neural mechanism is that in early life, M input reaches the striate

cortex via the LGN, as well as via an alternative retina-superior colliculus-pulvinar-MT

route, which is pruned during normal development (Bridge, Leopold, & Bourne, 2016;

Warner, Kwan, & Bourne, 2012). The ongoing development of M temporal

nonlinearities was not apparent using conventional VEP (Hammarrenger et al., 2003).

In summary, a more complete understanding of M and P changes throughout

development is achieved by studying both conventional and non-linear VEPs.

3.6.2 M and P function in developmental disorders

It has been proposed that differences in the timing of M and P afferent inputs

may contribute to visual processing abnormalities in disorders such as autism,

schizophrenia and dyslexia (Laycock et al., 2007). This section reviews multifocal VEP

studies that have investigated differences in M and P functionality across the autistic

spectrum (including trait-level comparisons within the neurotypical population), and in

groups with dyslexia and mathematical impairment.

3.6.2.1 The Autism Spectrum

Several studies have compared K2.1 and K2.2 waveforms in groups of

neurotypical adults with low and high levels of autistic tendency, as measured with the

AQ scale (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001). Sutherland

and Crewther (2010) recorded non-linear VEPs and local/global psychophysics in

groups with low and high AQ. Relative to the low AQ group, the high AQ group

exhibited lower K2.1 amplitudes at 24% contrast, delayed K2.1 peaks at 96% contrast,

and poorer ability to identify global letters made up of locally salient elements. Jackson

et al. (2013) investigated the CRFs of non-linear VEPs in groups with low, mid and

high AQ scores. The K2.1 and early K2.2 amplitudes (both with M-like CRFs) were

greatest for the high AQ group, whereas the K2.2 response was no different across the

three groups. Furthermore, the effects of dot-lifetime on motion coherence thresholds

differed across AQ groups. Burt, Hugrass, Frith-Belvedere, and Crewther (2017) found

that K2.1 amplitudes were elevated for a high cf. low AQ group, whereas the effects of

facial emotion on early components of event related potentials were diminished. Taken

55

together, findings from these studies suggest that inefficient recovery from stimulation

in the M pathway may contribute to global object, face, and motion processing

differences across the autistic personality spectrum.

Crewther, Crewther, Bevan, Goodale, and Crewther (2015) compared the effects

of saccadic suppression on non-linear VEP and psychophysical thresholds in groups of

neurotypical adults with low and high AQ scores. Psychophysically, the low AQ group

exhibited similar levels of saccadic suppression for low and high spatial frequency

gratings, whereas the high AQ group exhibited greater suppression for low spatial

frequency gratings. For the non-linear VEP experiment, observers viewed a pseudo-

randomly flashing bar during steady fixation, and again when they were making

periodic saccades between two points (with frequency ~2Hz, de-correlated with respect

to the m-sequence stimulation). Despite large differences in the K1 and K2.1

waveforms for the low and high AQ groups, the effects of saccades on these waveforms

were surprisingly consistent across groups. There was no strong evidence for a general

M theory of saccadic suppression; however the results for the high AQ group imply that

selective suppression of low spatial frequency information accompanying saccadic eye

movements may bias perception towards a local, detailed, and high spatial frequency

style, as reported in autism.

Brown and Crewther (2017) measured non-linear VEPs and local/global

inspection time thresholds in groups of neurotypical and ASD male children. The

groups were matched for age and non-verbal IQ (as measured with Raven’s Coloured

Progressive Matrices). To reduce between-subjects variation in recording conditions

(i.e. skull thickness) ratios of the first to second order amplitudes were computed for the

most prominent M and P waveforms (Jackson et al., 2013; Klistorner et al., 1997). For

low contrast multifocal stimulation, there were no differences in VEPs for the two

groups. For high contrast stimulation, the P ratio (K2.2N90P130/K1P90N130) was

significantly lower in the ASD group than in the typically developing group. This is a

sign of increased P temporal responsivity in ASD (i.e. a readiness of P neurons to fire

after stimulation). This could explain why those with ASD have been shown to have

enhanced local search skills (Mottron & Burack, 2001). Interestingly, in the ASD group,

but not in the typically developing group, there was a negative relationship between the

P ratio and non-verbal IQ. The authors interpreted these findings in terms of a constraint

56

on pathways available for cognitive response, if there is impaired M temporal

processing, those with ASD may become more reliant on the P pathway.

In summary, the study of putative M and P driven non-linear evoked potentials

has complimented psychophysical evidence of visual pathway abnormalities across the

autistic personality spectrum. This body of research has shown that differences in the

non-linear temporal structure of VEPs for groups with low and high autistic tendency

may contribute to differences in motion, object and face perception. This literature

suggests that perhaps the ‘M advantage’ (Laycock et al., 2007) in temporal processing is

diminished in groups with high levels of autistic tendency, which in turn can influence

how local and global input is prioritized in the autistic brain.

3.6.2.2 Dyslexia and mathematical impairment

Crewther et al. (1999) compared multifocal VEP responses for typically

developing and reading disabled children, with matched non-verbal IQ scores. Due to

age differences in non-linear VEP responses (Crewther et al., 1996), responses for the

reading disabled sample were compared against those of age-match controls. To

specifically investigate M and P functioning, ratios were computed for the major peak-

to-peak K2.2 waveform at 96% contrast and K2.1 waveform at 24% contrast. For both

the reading disabled and typically developing groups, P: M ratios decreased with age

and reached an asymptote at approximately 11 years of age, with no qualitative

developmental differences between the groups. These findings suggest there is not a

unitary M deficit in reading disabilities, or that only certain aspects of M-directed

processing (such as motion, spatial awareness or transient attention) are affected in

reading disabled children. However, the sample size was small, with only 6 reading

disabled children, who were between 7 and 15 years old.

Jastrzebski, Crewther, and Crewther (2015) compared non-linear VEPs for

adults with mathematical impairment and a control sample (matched for non-verbal

intelligence). At 24% temporal luminance contrast, there were no significant differences

in K2.1 or K2.2 amplitudes for the two groups. At 96% temporal luminance contrast,

there was much higher variance in the VEP responses for the mathematically impaired

group, with some evidence of an increased amplitude K1N60 waveform, and a delayed

K2.1P90 waveform. Interestingly, psychophysical data from this paper showed that

numerical inspection time thresholds for the two groups were matched at 24% contrast,

yet at 96% contrast, thresholds became faster for the control group and slower for the

57

mathematically impaired group. The authors suggest that the delay in the M processing

(as evidenced by a later K2.1 peak) may lead to impaired centre-surround and transient

attentional mechanisms at high contrast.

In summary, the study of M and P signatures in non-linear evoked potentials

does not support a unitary M deficit for children with dyslexia and adults with

mathematical impairment. Although psychophysical studies have provided substantial

evidence of dyslexic abnormalities on putative M visual processing tasks (Lovegrove,

1996; Stein, 2014), there has been some controversy regarding the M theory for

dyslexia (Skottun, 2000). A potential explanation is that in normal readers, M input

rapidly activates attentional mechanisms and gates spotlighting of sequential letters or

numbers (Vidyasagar & Pammer, 2010). Abnormal temporal interactions between

inputs from the M and P pathways might impair this mechanism in people with reading

difficulties (and perhaps also in people with numeric difficulties). Non-linear VEP

studies with larger samples of children with reading and numerical difficulties may shed

light on whether these impairments are associated with temporal processing

abnormalities in the M and P pathways.

3.6.3 Psychopharmacology and nonlinear VEP: Effects of Omega-3 PUFA diets

Omega-3 PUFAs such as eicosapentaenoic acid (EPA) and docosahexaenoic

acid (DHA) are major components of the lipid bilayer of cell membranes in the brain

(Horrocks & Farooqui, 2004). Retinal development in infants varies with the dietary

supply of Omega-3 PUFAs (Birch, Birch, Hoffman, & Uauy, 1992), and there is reason

to believe that DHA is important for M function (Stein, 2014; Taylor & Richardson,

2000).

In a double-blind cross-over design, healthy adults were administered DHA and

EPA rich fish-oil supplements for 30 days prior to non-linear VEP recordings (Bauer et

al., 2011). Following EPA supplementation, there was a significant reduction in the M-

driven K2.1 and early K2.2 non-linear VEP components, and a significant improvement

in choice reaction times, relative to baseline measurements. DHA supplementation

decreased the early K2.2 amplitude, but it did not influence later K2.2 amplitudes

(putative P) or simple motor reaction times. The reduction in M-driven non-linearity

after EPA administration suggests that it plays a role in improving the efficiency of M

recovery from temporal stimulation.

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While several authors have argued that Omega-3 PUFA supplementation may be

beneficial for children with autism, ADHD or learning disabilities (Sinn & Bryan, 2007;

Taylor & Richardson, 2000; Vancassel et al., 2001), the results have been inconclusive.

There has been a lack of well-controlled studies into whether PUFA supplementation

helps children with learning disorders (Tan, Ho, & Teh, 2012). Further research would

be required to establish whether an improvement in M efficiency might mediate the

effects of PUFA supplementation on perceptual and behavioural outcomes for children

with developmental disorders.

3.6.4 Colour processing

There has been a lot of debate as to whether luminance and colour are

represented separately in the brain (Shapley & Hawken, 2011). At the level of the LGN

the afferent streams have different sensitivities to colour, P cells respond well to

equiluminant red/green gratings, M cells are insensitive to chromatic contrast (Wiesel &

Hubel, 1966), and a subpopulation of K cells transmits s-cone signal to the cortex

(Chatterjee & Callaway, 2003). Colour and form processing becomes mixed at the

cortical level (Edwards, Purpura, & Kaplan, 1995). For instance, V1 layers 2/3 receive

uniformly distributed PC input (via V1 layer 4cβ), sparse MC input to centres of

cytochrome oxidase blobs (via V1 layer 4Cα) and KC input via the LGN (Ding &

Casagrande, 1998; Livingstone & Hubel, 1982).

Klistorner, Crewther, and Crewther (1998) demonstrated dissociation between

the effects of diffuse red stimulation and achromatic stimulation on non-linear VEP

responses, with chromatic responses in both the K2.1 and K2.2 kernels to red, but not

green stimulation. Crewther and Crewther (2010) used non-linear VEP to investigate

responses to diffuse colour saturation (i.e.; surface colour) and to coloured line patterns

(i.e.; form colour), at 30% luminance contrast. For diffuse colour, chromatic saturation

had the greatest effects on K2.1 amplitudes. For patterned stimuli

(appearance/disappearance presentation mode), there was a clear dominance in the first

order kernel, with relatively small responses in the K2.1 and K2.2 kernels. Interestingly,

K2.1 responses to diffuse stimulation were spectrally dependent, with stronger

responses for blue and red stimulation than for yellow green and cyan stimulation. By

contrast, K1 responses to pattern colour were robust for all colours.

These findings indicate that surface and edge colour representations have

different non-linear temporal structures. M cells are often described as ‘colour blind’, so

59

it is surprising that the K2.1 kernel response increased with chromatic saturation.

Although the M system is not colour selective, it is sensitive to chromatic signal

(Conway, 2014). A potential explanation for the spectral dependency of K2.1 responses

to diffuse colour (Crewther & Crewther, 2010) is that cells within the cytochrome

oxidase blobs of V1 layers 2/3 respond most strongly to diffuse red and blue light (Dow

& Vautin, 1987; Givre, Arezzo, & Schroeder, 1995). Furthermore, cells within blobs

receive M, P and K input, and tend to exhibit higher temporal resolution and lower

spatial resolution than cells between blob centres, which receive predominantly P input

(Edwards et al., 1995). This implies that chromatic sensitivity in the K2.1 waveform

may represent a rapid feed-forward, global figure segmentation signal that precedes

more detailed analyses of edges and surfaces (Bullier, 2001).

Steady state VEP (ssVEP) methods have also been applied to study non-linear

temporal responses to chromatic stimulation. Evidence from ssVEP research suggests

that responses to isoluminant chromatic flicker at the fundamental frequency are

dominated by P and K chromatic processing mechanisms, and that responses at the

second harmonic are dominated by M signals (Barboni et al., 2013; McKeefry, Russell,

Murray, & Kulikowski, 1996), which presumably arise from non-linear summation of

long- and medium-wavelength cone input (Lee, Martin, & Valberg, 1989). It would be

of interest for future studies to relate the chromatic processing signals in the K2.1 and

K2.2 nonlinear VEP waveforms to those observed in the fundamental and second

harmonic ssVEP responses.

3.7 Summary and future directions

As reviewed in Section 3.5, converging evidence indicates that the K2.1 and

early K2.2 waveforms originate from M input, whereas the main K2.2 waveform

originates from P input to the primary visual cortex (Brown et al., Submitted for

Publication; Crewther et al., 2016; Crewther & Crewther, 2010; Jackson et al., 2013;

Klistorner et al., 1997; Momose, 2010). However, it is important to keep in mind that

VEP responses are summed from multiple sources, so there is the possibility that the

recorded CRFs of these waveforms reflect combined input from different afferent

pathways (Skottun, 2000).

As reviewed in Section 3.6, studies that have applied this technique to

investigate the M and P pathways have mostly used unpatterned (i.e. diffuse) multifocal

stimuli. However, form can influence the non-linear temporal structure of VEPs (D. P.

60

Crewther & Crewther, 2010). Primate M and P spatial frequency sensitivities have been

well documented (Derrington, Krauskopf, & Lennie, 1984; Irvin, Casagrande, &

Norton, 1993). Although some previous studies have compared the effects of spatial

frequency on K2.1 and K2.2 responses to high contrast, pattern reversal stimuli

(Momose, 2010; Momose & Kasahara, 2003), to our knowledge there have been no

investigations into the combined effects of spatial frequency and luminance contrast on

non-linear VEPs. Future studies that analyse responses to patterned stimuli at various

spatial frequencies and achromatic contrast levels may further support the theory that

the main K2.1 and K2.2 waveforms originate in the M and P pathways respectively.

The contributions of K cells to non-linear VEP waveforms have not been well

studied, presumably owing to difficulties in designing stimuli to target this homogenous

population. The use of tritan stimuli may help to characterise the non-linear temporal

structure of responses produced by the K subpopulation that transmits s-cone opponent

signal to the cortex (Chatterjee & Callaway, 2003). Klistorner et al. (1998) used the

silent substitution method (Estévez & Spekreijse, 1974) to investigate interactions

between red-green chromatic and luminance contrast stimulation, and showed that in

dichromats, the minimum amplitudes of non-linear VEP responses were shifted from

the point of photopic equiluminance, to the red-green silent substitution point. This

suggests it could be informative to compare non-linear VEP response amplitudes

recorded with achromatic stimuli against those recorded with tritan stimuli around the

blue-yellow silent substitution point.

Most of the studies in this review focused on foveal and perifoveal vision. It is

known that the distribution of M and P cells varies with eccentricity (Dacey, 1993), and

that putative M and P non-linear VEP signatures vary with retinal eccentricity (Baseler

& Sutter, 1997; Laron et al., 2009). To date, none of the investigations into non-linear

VEPs in participant groups with potential M abnormalities have investigated the effects

of eccentricity on K2.1 and K2.2 amplitudes. In addition to temporal non-linearities,

non-linear spatial interactions are ubiquitous throughout the visual processing pathway.

Extending non-linear VEP analyses beyond the central patch would also allow for the

investigation of interactions between adjacent patches, which could be achieved through

the extraction of spatially mutual kernels (Zhang, 2003). This may help to further

characterise individual differences in M and P contributions to cortical visual processing

mechanisms.

61

The studies discussed in Sections 3.6.1 and 3.6.2 applied non-linear VEP to

investigate individual differences in M and P contributions to cortical vision. There is a

large degree of inter-subject variability in retinotopic maps of V1, as determined with

fMRI (DeYoe et al., 1996; Engel, 2012) and contrast-reversing multifocal VEPs (Zhang

& Hood, 2004). This is presumably owing to individual variability in cortical folding

patterns. Such differences can complicate comparisons of scalp-recorded VEP signals

across individuals. When principle components analysis (PCA) is applied to multifocal

VEP data via singular value decomposition (SVD), two principle components account

for a high percentage of intra- and inter-subject variance in the data (Dandekar, Ales,

Carney, & Klein, 2007; Zhang & Hood, 2004). When SVD is applied in combination

with MRI, it is possible to resolve VEPs arising from very close cortical sources, such

as V1 and V2 (Ales, Carney, & Klein, 2010; Carney, Ales, & Klein, 2008). These

techniques may improve the reliability of future non-linear VEP studies into changes in

putative M and P responses throughout development and across the autistic personality

spectrum.

3.8 Conclusions In summary, the characteristics of the K2.1 and K2.2 non-linear VEP kernels

indicate they reflect cortical processing of inputs from the M and P afferent pathways

respectively. Over the past 20 years, several studies have applied non-linear VEP

techniques to investigate human M and P functioning. In particular, when this technique

has been paired with psychophysical data, it has added richness to our understanding of

individual differences in visual processing.

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Chapter 4: The Effects of Red Surrounds on Visual Magnocellular

and Parvocellular Cortical Processing and Perception

4.1 Chapter guide Hugrass, L., Verhellen, T., Morrall-Earney, E, Mallon, C & Crewther, D.P. (In Press).

The effects of red surrounds on visual magnocellular and parvocellular cortical

processing and perception. Journal of Vision

This empirical chapter presents a re-formatted version of the original research

article cited above, which has been accepted for publication in Journal of Vision. This

work was conducted with the assistance of an undergraduate student group, who helped

to recruit participants and run the experimental testing sessions. The preliminary

analyses were presented at the Vision Sciences Society conference in 2017.

As reviewed in Chapters 2 and 3, it is not easy to extend results from primate

physiological recordings of the afferent visual pathways to human VEP and behavioural

responses. It is known from primate physiology that red surrounds suppress firing for a

subgroup of M cells. This chapter uses both non-linear VEP and psychophysics to

investigate the long-standing assumption that red backgrounds suppress M contributions

to human cortical responses and perception.

4.1.1 Highlights

• Magnocellular (M) non-linear VEP signatures were not affected by background

colour

• P temporal non-linearity in VEPs was lower with a red than green background

• The effects of background colour on putative M pedestal sensitivity varied with

pedestal chromaticity, luminance and eccentricity

• There were effects of red background on putative P pedestal sensitivity with

grey, but not chromatic pedestals

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4.2 Abstract More than 50 years ago, Hubel and Wiesel identified a subpopulation of

geniculate magnocellular (M) neurons that are suppressed by diffuse red light. Since

then, many human psychophysical studies have used red and green backgrounds to

study the effects of M suppression on visual task performance, as a means to better

understand neurodevelopmental disorders such as dyslexia and schizophrenia. Few of

these studies have explicitly assessed the relative effects of red backgrounds on the M

and P (parvocellular) pathways. Here we compared the effects of red and green diffuse

background illumination on well-accepted cortical M and P signatures, both

physiologically through non-linear analysis of visual evoked potentials (VEPs; N = 15),

and psychophysically through pulsed and steady pedestal perceptual thresholds (N = 9

with grey pedestals and N = 8 with coloured pedestals). Red surrounds reduced P-

generated temporal non-linearity in the VEPs, but they did not influence M-generated

VEP signatures. The steady and pulsed pedestal results suggest that red surrounds can

have different effects on M and P contrast sensitivities, depending on whether the target

is coloured grey or red, presented centrally or peripherally, or whether it is brighter or

dimmer than the surround. Our results highlight difficulties in interpreting the effects of

red backgrounds on human VEPs or perception in terms of M specific suppression.

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4.3 Introduction Multiple parallel pathways transmit visual information from the retina to the

cortex, leading to perception of form, colour and motion. The parvocellular (P) pathway

is highly sensitive to (red/green) colour, but it is less sensitive to luminance contrast,

and has a preference for high spatial frequency/low temporal frequency stimulation. The

magnocellular (M) pathway is highly sensitive to luminance contrast but not colour, and

has a preference for low spatial frequency, high temporal frequency stimulation

(Derrington & Lennie, 1984; Kaplan & Shapley, 1986; Livingstone & Hubel, 1988).

The koniocellular (K) pathways are made up of an amalgam of cells with different

properties and presumably different functions, including the transmission of input from

short-wavelength (blue) cones to the visual cortex (Casagrande, 1994; Ghodrati,

Khaligh-Razavi, & Lehky, 2017; Hendry & Reid, 2000; Martin, White, Goodchild,

Wilder, & Sefton, 1997).

There are several reasons why psychologists and cognitive neuroscientists have

studied M function. Firstly, M input reaches the primary visual cortex (V1) faster than P

input, playing an important role in foreground-background segmentation of the visual

scene (Bullier, 2001; Hupé, James, Payne, & Lomber, 1998), a primary stage of object

recognition. Secondly, the dorsal ‘vision for action’ stream receives predominantly M

input (Maunsell, Nealey, & DePriest, 1990). Furthermore, the M system is implicated in

rapid threat detection because it feeds into the collico-pulvinar route to the amygdala

(Schiller, Malpeli, & Schein, 1979). Finally, there is evidence of M dysfunction across

various clinical populations including dyslexia (Lovegrove, 1996; Stein & Walsh,

1997), schizophrenia (Butler et al., 2006) and autism (Laycock, Crewther, & Crewther,

2007). Therefore, it is important for scientists to develop non-invasive techniques to

investigate how M function influences perception and behaviour.

Over 50 years ago, primate single cell studies identified a subpopulation of M

neurons that are suppressed by diffuse red light (Wiesel & Hubel, 1966). These ‘Type

IV’ cells respond transiently to light presented in their receptive field (RF) centres, and

exhibit tonic suppression when long wavelength (red) light is presented in the RF

surround (de Monasterio, 1978; Wiesel & Hubel, 1966). Type IV RF characteristics

have been observed in M (parasol) retinal ganglion cells (RGCs) (de Monasterio, 1978),

the ventral layers of LGN (Wiesel & Hubel, 1966), and within the cytochrome oxidase

blobs in V1 layers 2 and 3 (Livingstone & Hubel, 1984). In the retina, there is a clear

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distinction between Type IV and Type III M RGCs, which have spatially opponent, but

not spectrally opponent RFs (de Monasterio, 1978). Unlike Type III cells, Type IV cells

tend to have a central retinal distribution, they frequently receive input from short-

wavelength cones, and they do not project to the superior colliculus, as verified by the

absence of antidromic stimulation of Type IV RGCs from electrodes placed in the

superior colliculus (de Monasterio, 1978). At the level of the LGN, the distinction

between Type III and IV cells is less clear, with almost all M cells showing some degree

of chromatic and spatial opponency (Derrington, Krauskopf, & Lennie, 1984).

Based on these physiological studies, many cognitive neuroscientists have

presented tasks on a red background to suppress processing contributions from the M

pathway (Awasthi, Williams, & Friedman, 2016; Bedwell, Brown, & Orem, 2008;

Breitmeyer & Williams, 1990; Chapman, Hoag, & Giaschi, 2004; Edwards, Hogben,

Clark, & Pratt, 1996; West, Anderson, Bedwell, & Pratt, 2010; Williams, Breitmeyer,

Lovegrove, & Gutierrez, 1991). For instance, West et al. (2010) found that with a green

background, fearful faces are perceived more rapidly than neutral faces, whereas with a

red background, this temporal precedence is diminished. The authors suggested that the

red surround could have suppressed M input to the colliculo-pulvinar route to the

amygdala (Schiller et al., 1979). However, this interpretation seems unlikely because

Type IV M cells do not project to the superior colliculus (de Monasterio, 1978).

Despite physiological evidence that most P receptive fields are spatially and

chromatically opponent (Derrington et al., 1984), the psychological studies discussed

above did not adequately consider the effects of a red background on the P pathway.

Skottun (2004) calculated the effects of red and green filters on long, medium and short-

wavelength cone pigments, and on the four broad classes of chromatically opponent

receptive fields (De Valois, Abramov, & Jacobs, 1966). Skotton’s calculations showed

that a red filter would have large effects on red-green and blue-yellow colour opponent

neurons, whereas the green filter had relatively little effect on the modelled responses.

Due to the heterogeneity of K receptive field properties (Hendry & Reid, 2000; White,

Solomon, & Martin, 2001), it is unclear how K cells might contribute to the effects of

red backgrounds on visual processing. Yet, to our knowledge, no behavioural or

neuroimaging experiments have been conducted to measure the effects of a red

surround on the central M and P pathways.

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In order to investigate the effects of red surrounds on M and P processing, we

used well-validated electrophysiological (Experiment 1) and psychophysical

(Experiment 2) paradigms. Temporal processing in the M and P pathways can be

inferred using non-linear VEP (Baseler & Sutter, 1997; Jackson et al., 2013; Klistorner,

Crewther, & Crewther, 1997) and non-linear MEG (Crewther, Brown, & Hugrass,

2016). In multifocal VEP experiments, multiple patches of light are flashed in

pseudorandom binary sequences, each de-correlated from the others. This not only

allows for simultaneous recordings across the visual field, but also for the analysis of

higher-order temporal non-linearities through Wiener kernel decomposition (Sutter,

1992; Sutter & Tran, 1992). For a temporally linear system, the first order kernel is the

impulse response function of the system (Benardete & Victor, 1994). The first and

second slices of the second order kernel (K2.1 and K2.2) are measures of non-linearity

over one and two video frames respectively. K2.1 responses (and the early components

of K2.2 responses) have high contrast gain and a saturating contrast response function,

consistent with an M pathway generator (Jackson et al., 2013; Klistorner et al., 1997).

The later component (N100-P140) of the K2.2 waveform has low contrast gain and a

non-saturating contrast response function, consistent with a P pathway generator

(Klistorner et al., 1997). A highly efficient system (i.e.; one that recovers rapidly from

stimulation) would produce large K1 responses, with no non-linear responses. Higher

amplitude K2.1 and K2.2 responses are associated with lower temporal efficiency in the

M and P pathways respectively (Bauer et al., 2011; Thompson et al., 2015). Hence, if a

red background decreases M temporal efficiency, we would expect it to increase the

K2.1 amplitude, whereas if it decreases P temporal efficiency, we would expect it to

increase the K2.2 amplitude (Experiment 1).

M and P responses can also be measured psychophysically using pulsed and

steady pedestal paradigms (Pokorny, 2011; Pokorny & Smith, 1997). In both

paradigms, observers are required to detect a brief luminance increment in one of four

pedestal stimuli. When the pedestals are lighter or darker than the background, neural

responses are mediated by ‘on’ or ‘off’ centred cells respectively (Schiller, 1992;

Zemon & Gordon, 2006), such that the spike rate of cells with the preferred polarity

increases along the contrast response function (Pokorny, 2011). When observers adapt

to steady pedestals in between target presentations, thresholds are interpreted as steady-

state M sensitivity, which tends to increase with pedestal luminance, regardless of the

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surround luminance (Pokorny & Smith, 1997). When the pedestals and target are

pulsed simultaneously, M responses are saturated and detection thresholds are

interpreted in terms of P contrast sensitivity (Pokorny & Smith, 1997). Hence, when

pulsed-pedestal detection thresholds are plotted against pedestal contrast, they form a

‘V’ shape around the point of equiluminance (Pokorny, 2011). If a red surround

suppresses the M pathway, we would expect it to decrease sensitivity to steady

pedestals, whereas if it suppresses the P pathway, we would expect it to decrease

sensitivity to pulsed pedestals (Experiment 2).

4.4 Experiment 1: Method

4.4.1 Participants

Fifteen participants (3 males, M = 21.8 years, SD= 2.5 years) gave written

informed consent for the experiment, which was conducted with the approval of the

Swinburne Human Research Ethics Committee and in accordance with the code of

ethics of the Declaration of Helsinki. The first four authors were included in the sample.

All participants had normal, or corrected to normal, visual acuity as measured with a

Snellen chart, and normal colour vision, as tested with Ishihara colour plates.

4.4.2 Stimuli

The stimuli (illustrated in Figure 4.1) were presented on a 60Hz LCD monitor

(ViewSonic, 80% rise latency = 3ms, 20% fall latency= 2ms, resolution 1024 x 768)

with linearized colour output (measured with a ColorCal II), at a viewing distance of

70cm. The 9-patch multifocal dartboard was created using VPixx software (version 3.2,

http://www.VPixx.com), with a 5.4-degree diameter central patch and two outer rings of

four patches (21.2° and 48°). The luminance for each patch was modulated at the video

frame rate (60Hz) in a pseudorandom binary m-sequence (m = 14), at either low (10%

Michelson) or high (70% Michelson) temporal contrast. The m-sequences for each

patch were maximally offset, so we could record independent responses across the

visual field.

The stimuli are specified in CIE1931 colour space. For the purpose of this

experiment, we only analyzed responses to the central, achromatic patch (42 cd/m 2,

CIE x = 0.32, CIE y = 0.33). Separate recordings were made with red (42cd/m2, CIE x =

0.65, CIE y = 0.34) and green (42cd/m 2, CIE x = 0.33, CIE y = 0.60) surrounds. For

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each experimental condition, the m-sequences were split into four approximately one-

minute recording segments, with the recordings lasting 16 minutes in total for the four

conditions. Participants were instructed to maintain strict fixation during the recordings

and to rest their eyes between recordings.

Figure 4.1 Dartboard stimulus configuration for the green low contrast (a), green high contrast (b), red low contrast (c) and red high contrast (d) conditions. We compared VEP kernel responses to the central patch for the conditions with red and green surrounds.

4.4.3 EEG recording and analysis

EEG was recorded using a 64-channel cap (Neuroscan, Compumedics). The data

were sampled at 1KHz and band-pass filtered from 0.1-200Hz. Electrode site AFz

served as ground and linked mastoid electrodes were used as a reference. EOG was

monitored using electrodes attached above and below one eye. Data were processed

using Brainstorm (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011), which is

documented and freely available for download online under the GNU general public

license (http://neuroimage.usc.edu/brainstorm). EEG data were band-pass filtered (1-

40Hz) and signal space projection was applied to remove eye-blink artefact. Custom

Matlab/Brainstorm scripts were written for the mfVEP analyses in order to extract K1,

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K2.1 and K2.2 kernel responses for the central patch. K1 is the difference between

responses to the light and dark patches (S1 and S2), i.e.K1= 0.5*(S1-S2). K2.1 measures

neural recovery over one frame (16.67ms) by comparing responses when a transition

did or did not occur, i.e., K2.1 = 0.25*(S11+S22-S12-S21). K2.2 measures neural recovery

over two frames (33ms), it is similar to K2.1, but includes an interleaving frame of

either polarity.

For each participant, the electrode with the highest amplitude responses was

selected for group-level averages. The highest amplitude responses were recorded at Oz

for 12 participants, POz for two participants and O2 for one participant. Peak and

trough amplitudes and latencies for the kernel waveforms were identified in Labview,

and exported to SPSS for linear mixed-effects modelling.

4.5 Experiment 1: Results and Discussion Grand averages for the K1, K2.1 and K2.2 responses were calculated for all

experimental conditions (red and green surrounds, high and low contrast). As illustrated

in Figure 4.2, there were some individual differences in the waveforms, yet the

averaged traces for K1 and K2.1 recorded with red and green surrounds overlap almost

perfectly (Fig 4.2a and b), and the K2.2 traces diverge. Separate linear mixed effects

models were computed (using the case ID codes to account for random effects for

participants), to investigate the effects of background colour (red vs. green) and

temporal contrast (10% vs. 70%) on the peak amplitudes of the K1, K2.1 and K2.2

responses.

4.5.1 K1 Amplitude

The results of Klistorner et al. (1997) suggest that the first order response (K1)

is produced by complex interactions between the M and P pathways. Running paired-

samples t-tests (Figures 4.2a, d) showed no significant effect of surround colour on VEP

amplitude, except in the 70% contrast condition at approximately 30ms latency,

however this difference was very small. More detailed analyses of the effects of

surround colour and luminance contrast were performed with linear mixed-effects

models of the main peak-trough amplitudes (K1N60-P90 and K1N120-P150). For both the

early and late peak-trough complexes, there were no significant main effects of

surround colour on K1 peak amplitudes (K1N60-P90: F (1,56) =0.02, p =0.91; K1N120-P150:

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(F (1,56) = 0.03, p =0.87), nor were there any significant surround by contrast

interactions (K1N60-P90: F (1,56) = 0.04, p =0.84; K1N120-P150: (F (1,56) = 0.05, p =0.83).

As expected, there were significant main effects of contrast on K1 amplitudes, with

greater responses at 70% than 10% temporal contrast (K1N60-P90: F(1,56) = 46.58, p

<0.001; K1N120-P150: F (1,56) = 58.19, p <0.001). In summary, K1 amplitudes are greater

when the central patch is high contrast, but they are not affected greatly by the surround

colour.

Figure 4.2 K1, K2.1 and K2.2 responses to the central patch at 10% (a, b, c) and at 70% (d, e, f) temporal contrast. The bold red and green lines correspond to the averaged waveforms for the conditions with red and green backgrounds respectively. Responses from each participant are illustrated in the faint red and green traces. VEP amplitudes for the red and green surrounds were compared using running paired samples t-tests (df = 14). The absolute t-values are shown in the black traces at the bottom of each panel, with the dashed and dotted horizontal lines signifying the p < .05 and p < .01 two-tailed significance thresholds respectively. Times when the VEP traces differed significantly are flagged with * (p < .05) and ** (p< .01).

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4.5.2 K2.1 Amplitude

Previous results suggest that the K2.1N60-P90 amplitude is of M pathway origin

(Jackson et al., 2013; Klistorner et al., 1997). Running paired-samples t-test

comparisons (below Figures 4.2b and 4.2e) showed no significant effect of surround

colour on VEP amplitude within the N60-P90 latency range, yet there were some

differences for the later potentials. Linear mixed-effects models were computed to

compare the effects of surround colour and luminance contrast on the N60-P90 and

N115-P140 and peak-to-trough amplitudes.

There was no significant main effect of surround colour on amplitude (K2.1N60-

P90: F (1,56) = 0.04, p =0.85, K2.1N115-P140: F (1,56) = 0.14, p =0.72). As expected, there

was a significant main effect of contrast on amplitude, with greater responses at 70%

than 10% temporal contrast (K2.1N60-P90: F (1,56) = 21.49, p <0.001, K2.1N115-P140: F

(1,56) 4.85, p=0.03). The mean contrast ratio (70%/10%) for the major K2.1N60-P90 peak

was similar with the red (M = 1.98, SD = 0.80) and green (M= 1.97, SD = 0.80)

surrounds, and there was no surround colour by contrast interaction (F (1,56) = 0.04 p

=0.85). These results suggest that, as expected K2.1 response amplitudes increase with

contrast; yet contrary to expectation K2.1 amplitudes are not greatly affected by the

background colour. Therefore, our results suggest that a red surround does not influence

temporal non-linearity generated by the M pathway.

4.5.3 K2.2 Amplitude

Previous studies indicate that the short latency K2.2N60-P80 waveform is also of

M origin (Jackson et al., 2013). As illustrated in the running t-test comparisons (Figures

4.2c, f), differences between the red and green traces did not reach significance at these

latencies. The linear mixed effects model of the K2.2N60-P80 amplitude showed there

were no significant main effects of surround colour (F (1,56) = 0.14, p =0.71), or

contrast (F (1,56) = 3.11, p =0.08), and there was no colour by contrast interaction (F

(1,56) = 0.03, p =0.88). Hence, consistent with our findings for the K2.1 amplitudes, a

red surround does not appear to affect temporal non-linearity in the M pathway.

Previous studies indicate that the K2.2N100-P140 waveform is of P origin (Jackson

et al., 2013; Klistorner et al., 1997). As illustrated in the running t-test comparisons

(Figures 4.2c, f), peak amplitudes were significantly lower with the red surround in both

the 10% and 70% contrast conditions. A linear mixed effects model showed a

significant main effect of surround colour (F (1, 56) = 4.91, p = 0.03), on average

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responses to the central patch were smaller with the red surround than with the green

surround. There was also a main effect of contrast, with greater K2.2 responses at 70%

than at 10% contrast (F (1, 56) = 86.07, p < 0.001). The mean contrast ratio was similar

with red (M = 2.85, SD = 1.14) and green surrounds (M= 2.92, SD = 1.01), and the

mixed effects model showed there was no significant surround colour by contrast

interaction (F (1, 56) = 0.28, p = .60). These results indicate that red surrounds reduce

temporal nonlinearity in the P-pathway, but they do not appear to affect the contrast

response function.

4.5.4 Summary

As expected based on the contrast response functions for non-linear VEPs

(Jackson et al., 2013; Klistorner et al., 1997), the effect of temporal contrast on response

amplitude was greater for the P-driven K2.2N100-P140 waveform than for the M-driven

K2.1N60-P90 and K2.2N60-P80 waveforms. This is consistent with the K2.1 response

showing higher contrast gain and saturation than the K2.2 response.

Contrary to expectation, the M driven responses were unaffected by surround

colour, but the P driven nonlinear responses were significantly smaller with the red

surround than with the green surround. This result was surprising, and could be

interpreted either in terms of the red surround reducing P output, or increasing P

temporal sensitivity. The former interpretation seems unlikely given that colour did not

affect the amplitude or contrast response for the linear (K1) kernel. The latter

interpretation seems plausible, given that a system with less efficient recovery from

stimulation would show increased power in the non-linear VEP response kernels

(Jackson et al., 2013). In summary, our results do not support the hypothesis that red

surrounds suppress the M pathway at the level of the cortical evoked response. On the

contrary, our results indicate that red surrounds increase temporal efficiency in the P

pathway.

4.6 Experiment 2: Method

4.6.1 Participants

There were 9 participants (3 males, M = 24.0 years, SD= 4.5 years) for the

experiment with grey pedestals on coloured backgrounds. The first two authors

participated in the experiment, but no other participants from this sample participated in

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Experiment 1. There were 8 participants (1 male, M = 28.5 years, SD= 5.8 years) for the

experiment with all red and all green stimuli (the first author and two others participated

in both psychophysics experiments). Participants gave written informed consent for the

experiment, which was conducted with the approval of Swinburne Human Research

Ethics Committee and in accordance with the code of ethics of the Declaration of

Helsinki. All participants had normal, or corrected to normal, visual acuity and normal

colour vision.

4.6.2 Stimuli

We used a gamma-corrected PROPixx data projector (120Hz, VPixx.com) to

rear-project the images to a screen at a viewing distance of 70cm. The contrast

discrimination tasks were created using VPixx and our stimulus design was based on

previous studies (McKendrick, Badcock, & Morgan, 2004; Pokorny & Smith, 1997).

The steady and pulsed contrast discrimination stimuli are illustrated in Figures 4.3a and

4.3b respectively. The stimuli are specified in CIE1931 colour space. The background

colour was set to either red (30cd/m2, CIE x= 0.66, CIE y= 0.33) or green (30cd/m2,

CIE x =0.13, CIE y=0.73). In both the steady and pulsed paradigms, the 30ms test

stimulus was a luminance increment in one of the four grey pedestals (squares with 1°

edges, CIE x = 0.33 CIE y = 0.39). Observers used a RESPONSEPixx button pad

(vpixx.com) to report which of the pedestals contained the luminance increment

(4AFC). For the steady conditions, observers adapted to the pedestals for 3 seconds

between each test presentation, whereas for the pulsed conditions, observers adapted to

the background in between test presentations. The pedestal luminance levels were

varied from decrements through to increments (-15, -6, 0, 8, 30 or 45 cd/m2) relative to

the coloured background (30 cd/m2). In the peripheral condition, the same stimuli were

presented in the upper right of the screen, 3.5 degrees away from fixation.

As illustrated in Figures 4.3a-d, the borders between the grey pedestals and

coloured backgrounds are defined by both colour and luminance, which may influence

the effects of pedestal luminance contrast on detection thresholds. In order to ensure our

results could be interpreted in terms of previous findings (Pokorny, 2011), we

conducted an additional experiment with versions of the stimuli that were all red (Figure

4.3e; CIE x= 0.64 CIE y= 0.33) or all green (Figure 4.3f; CIE x= 0.15 CIE y= 0.70).

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Figure 4.3 Illustration of the steady (a and c) and pulsed (b and d) pedestal paradigms on green and red backgrounds. An additional experiment was performed with pedestals and targets that were all red (e) or all green (f). In the steady paradigms, observers adapted to the pedestals for 3 seconds prior to a 30ms test stimulus. They were required to identify the location of the luminance increment (the top square in this case). The pulsed paradigms were the same except observers adapted to the background, rather than to the pedestals.

Increment/Decrement detection thresholds were measured using separate 30 to

40-trial VPESTs (the PEST inbuilt in VPixx) for each of the 48 stimulus conditions. To

allow time to adapt to the background, two fixed-value repetitions were completed with

high contrast test stimuli prior to the onset of the PEST. The experiment was split into

eight blocks (2 (central vs. peripheral) x 2 (red vs. green background) x 2 (steady vs.

pulsed pedestal)) of 6 PESTs (one at each pedestal luminance). The order of the

pedestal-luminance PESTs was randomised within blocks, and the order of the blocks

was counterbalanced across participants. To reduce fatigue and allow for recovery from

adaptation, observers took breaks between blocks (~10 minutes), and completed no

more than three blocks per lab visit.

For any given condition, most observers’ thresholds fell within narrow ranges.

When outliers were detected (>3 SD from the mean), thresholds for the same observer

in different conditions tended to fall within the 2 SD of group mean. Furthermore,

roughly equal numbers of outliers were identified across the red and green surround

conditions. Therefore, we assumed that any outliers reflected a measurement error, such

that the PEST failed to converge on the observer’s true threshold. Based on this logic,

we replaced outliers for a condition with the group mean for that condition.

4.7 Experiment 2: Results and Discussion Before discussing the effects of red backgrounds on the steady and pulsed

pedestal tasks, we begin by comparing our results against previous studies that used

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achromatic stimuli. Previous studies showed that steady pedestal thresholds increase

monotonically with pedestal luminance, whereas pulsed pedestal thresholds form a ‘V’

shape around the point of equiluminance (McKendrick et al., 2004; Pokorny, 2011;

Pokorny & Smith, 1997). In our experiment with grey pedestals (Figures 4.4a and 4.4b),

steady pedestal thresholds dropped substantially when the pedestals were equiluminant

with the background, and pulsed pedestal thresholds departed from the classic ‘V’

shape. These discrepancies could be because our pedestals were not the same colour as

the background, so they remained visible at equiluminance. Hence, we repeated the

experiment with pedestal stimuli that were the same colour as the backgrounds (Figures

4.3e and 4.3f). Under these conditions, the averaged linear fits for the red and green

steady pedestal tasks (Figures 4.4c and 4.4d, solid yellow traces) are similar to those

reported in previous studies that used achromatic stimuli, and the pulsed pedestal

thresholds formed the classic ‘V’ shape (McKendrick et al., 2004; Pokorny, 2011;

Pokorny & Smith, 1997).

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Figure 4.4 Plots of mean log luminance increment thresholds versus log pedestal luminance for the centrally (a) and peripherally (b) presented grey pedestal tasks (N = 9) and for the centrally (c) and peripherally (d) presented coloured pedestal tasks (N=8). Results for the red and green background conditions are shown in the red and green traces respectively. Results for the steady pedestal task are shown in the filled markers (solid lines), whereas results for the pulsed pedestal task are shown in the unfilled markers (dashed lines). For the coloured pedestal tasks, the yellow markers are the average of thresholds obtained in the red and green steady pedestal conditions, and the yellow solid line illustrates the linear fit. The backgrounds have been shaded in dark and light greys to show when the pedestals were decrements and increments relative to the background luminance. The error bars denote ± 1 SEM.

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4.7.1 Grey steady pedestal tasks

For the grey versions of the steady pedestal tasks, thresholds measured with the

red and green backgrounds were almost perfectly overlapping (solid red and green

traces, Figures 4.4a and 4.4b), except for the peripherally presented, increment pedestals

(Figure 4.4b), where thresholds were higher with the red background. There were no

significant effects of background colour on mean thresholds for pedestals that were

equiluminant with (central: t(8) = 0.78, p = 0.50, peripheral; t(8) = 0.04, p = 0.97), or

dimmer than the background (central: F(1,8) = 0.52, p = 0.49, peripheral: F(1,8) = 0.30,

p = 0.87). For increment pedestals, there was no effect of surround colour on thresholds

when the stimuli were presented centrally (F(1,8) = 1.39, p = 0.27). When the stimuli

were presented peripherally, steady increment thresholds were significantly higher with

the red background (F(1,8) = 11.19, p = 0.03, ηp2 = 0.47), but there was no significant

interaction between the effects of background colour and pedestal luminance (F(2,16) =

1.77, p = 0.20). Overall, these results are only partially consistent with the prediction

that red surrounds reduce psychophysical measures of M sensitivity.

4.7.2 Grey pulsed pedestal tasks

Results for the task with grey, pulsed pedestals are illustrated in Figures 4.4a

and 4.4b (dashed lines). At equiluminance, pulsed pedestal thresholds were much lower

with the green background than with the red background (central; t(8)=4.51, p =0.002,

peripheral: (t(8)=4.92, p =0.001). This suggests that M responses are swamped by the

appearance of grey targets on red backgrounds, but can still contribute to contrast

sensitivity when grey targets appear on green backgrounds. For the centrally presented

task, there was a significant interaction between the effects of decrement pedestal

luminance and surround colour (F(1,8) = 7.16 p = .028, ηp2 = .47). Increment pulsed-

pedestal thresholds were significantly higher with the red background (F(1,8) = 8.74, p

= 0.018, ηp2 = 0.52). For the peripherally presented task, thresholds were significantly

higher with the red surround for the decrement (F(1,8) = 14.96, p = 0.005, ηp2 = 0.65),

and increment-pulsed pedestals (F(1,8) = 14.96, p = 0.005, ηp2 = 0.65). There was also a

significant interaction for the peripheral increment pedestals, (F(2,16) = 10.68, p =

0.001, ηp2 = 0.57), with shallower slopes for the red surround. These results indicate that

red surrounds can reduce P sensitivity to grey target stimuli.

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4.7.3 Coloured steady pedestal tasks

In a subsequent experiment, we created all red and all green versions of the

pedestal stimuli. This subtle difference in the stimuli substantially altered the effects of

background colour on steady pedestal thresholds (solid red and green markers, Figures

4.4c and 4.4d). When the thresholds are averaged across the red and green conditions,

the linear fits (yellow traces, Figures 4.4c and 4.4d) are consistent with previous

evidence of a monotonic increase in thresholds with pedestal luminance (Pokorny,

2011). Curiously, thresholds recorded with the +8cd/m2 pedestals tended to fall below

the linear fits.

For the steady decrement pedestal stimuli, a repeated measures ANOVA showed

significant interactions between the effects of colour and pedestal luminance on contrast

detection for the centrally (F(1,7) = 1472.78, p < 0.001, ηp2 = 0.995) and peripherally

presented stimuli (F(1,7) = 8.91, p = 0.02, ηp2 = 0.56). Thresholds tended to increase

with pedestal luminance for the green stimuli and decrease with pedestal luminance for

the red stimuli. At equiluminance, mean thresholds were significantly lower for the red

stimuli when the target was centrally presented (t(7) = 100.88 , p <0.001), but not when

it was in the periphery (t(7) = 1.97 , p = 0.09). For the centrally presented steady

increment pedestals, there was a significant colour by luminance interaction (F(1,7) =

7.18, p = 0.007, ηp2 = 0.51), with reduced thresholds for red steady increment pedestals

at +30cd/m2 (t(7) = 4.17 , p =.004). For the peripherally presented steady increment

pedestals, there was a main effect of surround colour (F(1,7) = 9.16, p = 0.02, ηp2 =

0.57), with significantly elevated thresholds for the red stimuli at +8cd/m2 (t(7) = 3.09 ,

p =.002). These results suggest that a red surround can improve or impair M sensitivity,

depending on whether the target stimuli are presented centrally or peripherally, and

whether they are brighter or dimmer than the background.

4.7.4 Coloured pulsed pedestal tasks

For the all red and all green, pulsed pedestal stimuli (Figures 4.4c and 4.4d,

dashed lines), there were no significant main effects of colour on thresholds, nor were

there any significant colour by pedestal luminance interactions (p >.05). For the

centrally presented task, thresholds for the +8cd/m2 pulsed pedestals were slightly

higher with the red background, and this difference was approaching significance

(t(7)=2.08, p = .076). This pattern of results is different from the experiment with grey,

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pulsed pedestals, and indicates that a red surround does not influence P sensitivity when

there is no chromatic edge between the target and the surround.

4.7.5 Summary

Overall, the results we obtained with the coloured pedestal stimuli (Figures 4.4c

and 4.4d) align closely with the classic linear and ‘V’ fits of steady and pulsed pedestal

thresholds respectively. The results we obtained with the grey pedestals on coloured

backgrounds depart from these classic fits (Figures 4.4a and 4.4b). Therefore, we can be

more confident in interpreting the coloured pedestal results in terms of evidence that

links steady and pulsed pedestal thresholds with M and P functions (McKendrick et al.,

2004; Pokorny, 2011; Pokorny & Smith, 1997).

Our results for the steady pedestal experiments suggest that it would be an

oversimplification to say that red surrounds suppress M sensitivity. We found that red

surrounds can either improve or impair M sensitivity, depending on whether the target

stimuli are presented centrally or peripherally and whether they are brighter or dimmer

than the background. This could be due to the fact that L cones are more numerous than

M cones (Pandey Vimal, Pokorny, Smith, & Shevell, 1989; Vos & Walraven, 1971) and

M RGCs cells receive more input from L cones than M cones (Diller et al., 2004). In

both the grey and coloured versions of the experiment, thresholds for the peripherally

presented, steady-increment pedestals tended to be higher with the red surround.

Responses to increment and decrement pedestals are dominated by ‘on’ and ‘off’

centred neurons respectively (Pokorny, 2011; Schiller, 1992; Zemon & Gordon, 2006),

so our findings are consistent with evidence that Type IV M ganglion cells tend to have

‘ON’ centres (de Monasterio, 1978). However, these findings cannot easily be

explained in terms of Type IV M RGCs, which tend to have a more central distribution

than Type III M cells (de Monasterio, 1978). Given that the error bars for the peripheral

increment thresholds were large, there might be some individual differences in the

effects of surround colour on M sensitivity.

Our results for the pulsed pedestal stimuli provide mixed evidence as to whether

red surrounds affect P contrast sensitivity. For the grey, pulsed pedestal stimuli, we

observed an overall elevation in thresholds with the red surround, both for the centrally

presented and peripherally presented stimuli. This suggests that red surrounds decrease

P contrast sensitivity. These results could be explained by the fact that we did not

attempt to match colour contrast levels between the grey pedestals and the red and green

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backgrounds (Pammer & Lovegrove, 2001). Hence, we repeated the experiment with all

red and all green stimuli. Under these conditions, there was no effect of background

colour on pulsed pedestal thresholds. This suggests that when the effects of colour

contrast have been accounted for, P contrast sensitivity is unaffected by the colour of

the background.

4.8 General discussion Taken together, our results from Experiments 1 and 2 show it is difficult to

interpret the effects of red backgrounds on VEP amplitudes and psychophysics solely in

terms of M pathway suppression. In the non-linear VEP experiment, we observed

almost identical K2.1 responses with the red and green backgrounds, and smaller K2.2

amplitudes with the red background. This indicates that a red background does not

influence temporal non-linearity in the M pathway, yet it does reduce P-driven temporal

non-linearity (Klistorner et al., 1997). Our results for Experiment 2 indicate that red

backgrounds have different effects on putative M and P psychophysical measures,

depending on whether the target stimuli are red or grey, central or peripheral, or brighter

or dimmer than the background.

Previous studies have reported effects of red backgrounds on a range of tasks

including meta-contrast masking (Bedwell & Orem, 2008; Breitmeyer & Williams,

1990; Pammer & Lovegrove, 2001), motion processing (Bedwell, Miller, Brown, &

Yanasak, 2006; Breitmeyer & Williams, 1990; but see Pammer & Lovegrove, 2001)

and face processing (Awasthi et al., 2016; Bedwell et al., 2013; West et al., 2010). The

authors interpreted the effects of red surrounds on task performance in terms of M

suppression. We showed that under some conditions, a red background has the expected

suppressive effects on putative M psychophysics, but only when the pedestal stimuli are

presented in the periphery and have higher luminance than the background. This

indicates that the existing literature regarding the effects of red backgrounds on task

performance should be reconsidered, depending on the colour, eccentricity and

luminance contrast of the target stimuli.

Although a red background can almost completely suppress Type IV M cells, its

effects on putative “M” psychophysics are more variable and subtle (Pammer &

Lovegrove, 2001). We were surprised to find that the red background enhanced putative

M psychophysics under some conditions. For instance, the red background lowered

steady pedestal detection thresholds for the centrally presented, coloured pedestal

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stimuli, and for the peripherally presented coloured decrement pedestals. Although there

are many processing stages in between LGN afferent responses and perception, these

results imply that contrast sensitivity is enhanced when Type IV M cells are suppressed.

It is worthwhile considering the different roles that Type III (i.e. broadband) and Type

IV M cells might play in visual processing, particularly given their different retinal

distributions, and the absence of Type IV M projections to the superior colliculus (de

Monasterio, 1978).

We also found that red backgrounds can influence P signatures in non-linear

VEPs and psychophysics. In the non-linear VEP experiment, K2.2 responses were

lower with the red surround than with the green surround. The more rapidly neurons

recover from stimulation, the smaller their contributions to non-linear VEP responses

(Bauer et al., 2011; Sutter, 2000; Thompson et al., 2015). Based on this reasoning, our

results suggest that red surrounds increase temporal sensitivity in the P pathway, with

an immediate prediction of enhanced L-M colour fusion frequencies. In Experiment 2,

the red surround decreased contrast sensitivity for grey, pulsed pedestal stimuli. This

may suggest a reduction in P sensitivity; however Pammer and Lovegrove (2001)

argued that confounds between the effects of colour and luminance contrast make it

difficult to interpret the effects of red backgrounds in terms of the M and P afferent

streams. Consistent with this argument, there were no significant differences in pulsed

pedestal thresholds for the coloured pedestal versions of the experiment. This suggests

that when colour contrast has been taken into account, P sensitivity to achromatic

contrast is unaffected by the surround colour.

Although it is well known that red surrounds suppress Type IV M cells in the

retina and LGN (de Monasterio, 1978; De Valois et al., 1966; Derrington et al., 1984;

Wiesel & Hubel, 1966), we cannot rule out the possibility that the effects of chromatic

surrounds on perception also reflect cortical interactions. Livingstone and Hubel (1984)

identified cells with Type-IV receptive fields within the cytochrome oxidase blobs in

V1 layers 2 and 3. Within cytochrome oxidase blobs, cells tend to prefer low spatial

frequency red and blue stimulation, whereas between blobs, cells tend to prefer oriented

edges and green-yellow colours (Dow & Vautin, 1987). Crewther and Crewther’s

(2010) chromatic non-linear VEP study showed that K2.1 responses to diffuse surface

colours almost disappear for yellow-grey or green-grey stimuli, whereas K1 responses

to oriented edges are robust for all colours. Hence, although previous studies have

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interpreted the effects of red surrounds on task performance in terms of the subcortical

M and P pathways (Skottun, 2004), an alternative explanation could be that red

surrounds influence ‘surface’, but not ‘edge’ representations.

A limitation of studying human M and P responses with VEP and psychophysics

is that we can only infer processing in the afferent pathways indirectly, based on what

we know from primate physiology. The temporal characteristics and contrast response

functions of the K2.1 and K2.2 VEP waveforms (Jackson et al., 2013; Klistorner et al.,

1997) and steady and pulsed pedestal paradigms (reviewed Pokorny, 2011) provide

converging evidence of their origins in the M and P pathways respectively.

Approximately 10% of LGN cells are koniocellular (Hendry & Reid, 2000) and K cells

within ventral and dorsal LGN regions have contrast response functions similar to those

of M and P cells respectively (White et al. 2001). This complicates interpretations of

‘M’ and ‘P’ VEP and psychophysiological measures. Yet due to their relatively small

population and heterogeneous receptive field properties, it seems unlikely that K cells

would contribute substantially to steady and pulsed psychophysical thresholds or K2.1

and K2.2 non-linear VEP amplitudes. Furthermore, due to sluggish K responses to

temporal modulation (Irvin et al., 1986), one would expect K-driven nonlinear VEPs to

exhibit different temporal structures to M and P driven non-linear VEPs.

When making inferences about human visual processing based on primate single

cell studies it is important to consider potential differences between the primate and

human visual systems. To our knowledge there have not been any direct recordings

from Type-IV M cells in humans, but comparative studies suggest that excitatory input

to V1 layers 4Ca and 4Cb are highly in macaques and humans (Garcia‐Marin, Ahmed,

Afzal, & Hawken, 2013). While midget (P) RGCs are highly similar in macaques and

humans, parasol (M) RGCs tend to have larger dendritic field sizes in humans than

macaques (Dacey & Petersen, 1992). This may lead to some functional differences in

the human and macaque M and P pathways.

In conclusion, we applied two different techniques to test claims that red

backgrounds suppress human cortical measures of putative M responses while sparing

cortical measures of putative P responses. Our results for the electrophysiology

experiment did not provide any evidence that red surrounds suppress the M pathway;

however the K2.2 results imply that red surrounds affect temporal non-linearity

generated by the P-pathway. Our results for the second experiment suggest that red

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surrounds can influence either M or P psychophysical signatures, depending on the

colour, eccentricity and luminance of the target stimuli. We argue that it was an

oversimplification for previous studies to have interpreted the effects of red

backgrounds on behavioural performance solely in terms of M suppression. Our results

highlight difficulties in predicting human perceptual effects based on subcortical M and

P physiology.

4.9 Acknowledgements We would like to thank our reviewers for suggesting the additional psychophysics

experiment with all red and all green, pedestal stimuli. This research was funded by the

Australian Government, through the Australian Research Council.

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Chapter 5: The Temporal Structure of Evoked MEG Responses:

Effects of Chromatic Saturation

5.1 Chapter guide

Hugrass, L., & Crewther, D. (In Preparation). The temporal structure of evoked MEG

responses: Effects of chromatic saturation

This empirical chapter presents a re-formatted version of the original research

article cited above, which is in preparation for submission in Frontiers of Neuroscience.

The preliminary analyses were presented at the 2017 Australasian Cognitive

Neurosciences meeting. The overall aim of the work presented in this chapter was to

investigate the cortical sources of non-linear VEP signal that are sensitive to chromatic

saturation. In this experiment, brain signals were measured using MEG rather than

EEG. For the most part, when recorded at the scalp both methods reflect the same

neural processes (da Silva, 2013); however, there are some differences. For instance,

EEG is sensitive to both radial and tangential dipole sources, whereas MEG is only

sensitive to the tangential components of dipole sources (Ahlfors et al., 2010). On the

other hand, the advantage of MEG is that it allows for better spatial resolution of

cortical sources, due to less spatial smearing than EEG (da Silva, 2013). For this reason,

MEG was the most appropriate methodology for the experiment presented in this

chapter.

5.1.1 Highlights

• Non-linear temporal analyses revealed MEG responses that are sensitive to

diffuse blue light

• Cortical sources of responses to saturated blue light were retinotopically mapped

• The effects of chromatic saturation on VEPs were not as clearly mapped on the

cortical surface

• The strongest effects of blue saturation were in the K2.1 response, at

approximately 70ms

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• This indicates that the underlying neural mechanism recovers quickly from

stimulation

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5.2 Abstract Human electrophysiological studies have shown that diffuse blue chromatic

saturation has strong effects on non-linear VEP amplitudes. We used MEG to further

investigate the effects of chromatic saturation on the timing and cortical sources of non-

linear VEP signals. MEG responses were analysed from a sample of 8 adults with

normal visual acuity and colour vision. Central and peripheral quadrants of the visual

field were stimulated with pseudorandom binary luminance alternations (30% temporal

luminance contrast, updated every 16.67ms). Chromatic saturation of the darker grey

was varied from 0% to 95% of the maximum blue level of the display projector. As

expected, first order (K1) and second order (K2.1 and K2.2) response amplitudes tended

to increase with chromatic saturation. The strongest effects were observed for the K2.1

response, at a latency of approximately 70ms. Partial least of squares analyses indicated

that cortical K2.1 and early K2.2 responses are reliably modulated by chromatic

saturation, as early as 55ms post stimulation. These findings suggest that the effects of

diffuse chromatic saturation on VEPs may reflect rapid processing of M (and possibly

K) inputs to the cytochrome oxidase blob centres in V1 layers 2/3.

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5.3 Introduction The major post-receptoral visual pathways play different roles in colour

processing. The rapid magnocellular (M) pathway sums input from L- and M-

wavelength cones at the level of the bipolar cells of the retina (Nassi & Callaway,

2009), and also receives some input from S-cones (Chatterjee & Callaway, 2002).

Although it is highly sensitive to luminance contrast, it is generally considered to be

‘colour blind’ (Hubel & Livingstone, 1987). The parvocellular (P) pathway carries

chromatically opponent L and M cone signals. It has better spatial acuity than the M

pathway at any eccentricity, however P input takes approximately 20ms longer than M

input to activate V1 in monkeys (Bullier, 2001; Nowak, Munk, Girard, & Bullier,

1995). The koniocellular (K) system was characterised more recently (Casagrande,

1994). The best-characterised type of K cell carries s-cone (i.e.; blue on/yellow off)

signal (Hendry & Reid, 2000; Pietersen, Cheong, Solomon, Tailby, & Martin, 2014;

Schiller & Malpeli, 1978).

In V1 layers 2/3, P input is spread uniformly across the cytochrome oxidase blob

and inter-blob regions, whereas M and K inputs are focused around the blob centres

(Edwards, Purpura, & Kaplan, 1995; Hendry & Reid, 2000). The majority of colour

selective cells within the inter-blob regions respond strongly to oriented edges, and

weakly to the chromatic interiors of objects (Johnson, Hawken, & Shapley, 2008). Inter-

blob cells are similar to P cells, in that they have poor luminance contrast sensitivity,

band-pass spatial frequency tuning, and prefer low temporal frequency stimulation. On

the other hand, cells near the centres of blobs lack orientation selectivity (Ts'o &

Gilbert, 1988). Cells close to blob centres are often selective for red/green or

blue/yellow stimulation; however they share some properties with M cells, in that they

exhibit high firing rates, temporal frequency sensitivity and luminance contrast

sensitivity, and they prefer diffuse or low spatial frequency stimulation (Edwards et al.,

1995; Gur & Snodderly, 1997; Pietersen et al., 2014; Shoham, Hübener, Schulze,

Grinvald, & Bonhoeffer, 1997). This indicates that cells within and between the centres

of cytochrome oxidase blobs handle different aspects of chromatic processing.

Chromatic visual evoked potential (VEP) studies in humans have mostly used

isoluminant chromatic grating stimuli (e.g., Berninger, Arden, Hogg, & Frumkes, 1989;

Foxe et al., 2008; Murray, Parry, Carden, & Kulikowski, 1987). The responses to such

stimuli are likely to be dominated by cells in the inter-blob regions, which respond well

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to chromatic edges (Johnson et al., 2008). However, some interesting results have

emerged from studies that presented diffuse, unpatterned stimuli. For instance, a

transient VEP study revealed a fast latency (~87ms) chromatic response, that was higher

in amplitude for red diffuse stimuli than green or yellow stimuli (Paulus, Hömberg,

Cunningham, Halliday, & Rohde, 1984). A study in macaques showed that responses

from V1 layers 2/3 were higher in amplitude for red or blue diffuse light than for green

or grey light (Givre, Arezzo, & Schroeder, 1995). These wavelength-dependent effects

might reflect responses from cells within cytochrome oxidase blobs, which tend to

respond most strongly to diffuse red or blue light (Dow & Vautin, 1987).

Further support for this idea has come from studies that used multifocal VEP to

analyse the non-linear temporal structure of responses to diffuse colour. Pseudorandom

binary stimulation allows for non-linear temporal analyses through Wiener kernel

decomposition (Sutter, 1992). The first order kernel (K1) is the difference in response to

the light and dark patches, and is analogous to the impulse response function for a linear

system. The first and second slices of the second order response (K2.1 and K2.2)

measure neural recovery over one and two video frames respectively (Klistorner,

Crewther, & Crewther, 1997; Sutter, 1992, 2000).

Klistorner, Crewther, and Crewther (1998) compared responses to diffuse grey-

grey, green-grey and red-grey stimulation, all at the same temporal luminance contrast.

Responses to green-grey stimulation were not different from responses to red-grey

stimulation, whereas chromatically sensitive components emerged for the red/grey

condition. The effects of red chromatic saturation on response amplitudes were

strongest for the K2.1 waveform. Crewther and Crewther (2010) also reported

wavelength-dependent effects of diffuse chromatic stimulation on K2.1 amplitudes,

with stronger responses to blue and red stimulation, than to green or yellow stimulation.

Consistent with the temporal sensitivity of cells close to blob centres, the effects of

colour on the K2.1 waveform suggest that VEP responses to diffuse blue and red

stimulation are dominated by a population of cells that recover rapidly from temporal

stimulation.

The above-mentioned non-linear VEP analyses were based on a single, central

stimulus patch (Crewther & Crewther, 2010; Klistorner et al., 1998). Due the changes in

the polarity and amplitude of responses from across the visual field, this might have

blurred the VEP waveforms. This problem can be reduced by stimulating different

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quadrants of the central visual field with temporally de-correlated m-sequences (Baseler

& Sutter, 1997; Baseler, Sutter, Klein, & Carney, 1994). The aim of the current study

was to use MEG to further investigate the effects of chromatic saturation on non-linear

VEP responses. Based on existing EEG results (Crewther & Crewther, 2010), we

expected K2.1 magnetic responses to increase with the level of blue chromatic

saturation. Although it has been shown that responses to achromatic multifocal stimuli

originate from retinotopically arranged sources in V1 (Slotnick, Klein, Carney, Sutter,

& Dastmalchi, 1999), no previous studies have mapped non-linear kernel responses to

diffuse chromatic stimuli. We used partial least of squares (PLS) analyses (Krishnan,

Williams, McIntosh, & Abdi, 2011) to investigate the cortical sources of K1, K2.1 and

K2.2 responses that vary reliably with the degree of blue chromatic saturation.

5.4 Methods

5.4.1 Participants

9 participants (2 males, M = 24.4 years, SD= 5.0 years) gave written informed

consent for the experiment, which was conducted with the approval of the Swinburne

Human Research Ethics Committee and in accordance with the code of ethics of the

Declaration of Helsinki. All participants had normal (or corrected to normal) visual

acuity and normal colour vision, as tested with Ishihara colour plates. The results from

one participant were excluded from the analyses, due to low signal to noise ratios in the

recordings.

5.4.2 Stimuli

The stimuli were presented on a Panasonic PTDS100x LCD projector (60Hz,

resolution 1900 x 1200) at a viewing distance of 115 cm. The 8-patch multifocal

dartboard was created using VPixx software (version 3.20, http://www.VPixx.com) and

a DATAPixx display driver (24 bit). The dartboard consisted of 4 central quadrants

(radius = .25°-3.50°) and 4 outer segments (radius = 3.5°-14°). Each patch was

modulated in a pseudorandom binary m-sequence (m = 14) generated in LabVIEW

(National Instruments, USA), which was updated every video frame (16.7ms).

The m-sequences were maximally offset to allow for independent analyses of

responses from each stimulus patch. For the purpose of this experiment, only responses

from the central four quadrants were analysed, and CIE1931 colour space was used to

specify the stimuli. In the achromatic (0% blue saturation) condition, binary exchange

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occurred between the dark (CIE x = .300, CIE y = .300, L = 9.87 cd/m2) and light (CIE

x = .300, CIE y = .300, L =12.82 cd/m2) grey patches. Temporal luminance contrast was

kept constant at 30% across conditions, but the chromatic saturation of the darker patch

was varied from 25% (CIE x = .265, CIE y = .247), 50% (CIE x = .229, CIE y = .117),

75% (CIE x = .194, CIE y = .108), to 95% (CIE x = .166, CIE y = .045), where

percentage saturation was defined as the progression along a line in CIE space from

grey to the maximum blue level for the projector (see Figure 5.1).

Figure 5.1 Illustration of the chromatic saturation levels for the blue multifocal stimuli. The central quadrants of the multifocal stimuli are illustrated in the panels on the right, at levels of 0, 25, 50, 75 and 95% blue saturation. For each chromatic saturation condition, temporal luminance contrast = 30%

5.4.3 MEG recording

MEG was recorded at Swinburne University of Technology Neuroimaging

Facility using an Elekta TRIUX MEG scanner (Helsinki, Finland) with 102

magnetometer, and 204 planar gradiometer SQUID detectors. Recordings were carried

out in a dark magnetically shielded room (MSR), with active shielding against magnetic

transients. Head position was tracked using five continuous head position indicator

(cHPI) coils that were attached to the left and right mastoids, and three locations near

the hairline of the forehead. The shape of the participant’s head, the locations of the

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cHPI coils, three head fiducials (nasion, and bilateral preauricular points) were digitised

using a Polhemus FASTRAK (Polhemus Inc., Colchester, VT, USA). EOG and ECG

were recorded using disposable electrodes placed above and below the right eye, and on

the left and right wrists respectively, with a ground electrode attached to the back of the

participant’s elbow.

Separate recordings were made for each of the five chromatic saturation

conditions. In order to prevent fatigue, the m-sequence for each condition was split into

four approximately one-minute blocks (4096 frames), with one-second overlapping

fringes at the start and end of each block (20 blocks in total). Participants were

encouraged to maintain strict fixation on a central marker within blocks, and to blink

and rest their eyes in-between blocks.

5.4.4 Structural T1

A 3T Siemens TIM Trio magnetic resonance imaging (MRI) system (Siemens,

2016, Erlangen, Germany, 32-channel head coil acquisition system) was used to acquire

structural T1 images for each participant. T1-weighted images were acquired on a

sagittal plane with a magnetisation prepared rapid gradient echo (MP-RAGE) pulse

sequence with an inversion recovery (176 slices, slice thickness= 0.75mm, voxel

resolution= 0.75mm3, TR= 1900ms, TE= 2.52ms, TI= 900ms, bandwidth= 170Hz/Px,

flip angle= 9°, orientation sagittal).

5.4.5 MEG analyses

MaxFilter (Version 2.1, Helsinki, Finland Elekta, 2016) was applied to each raw

recording for Temporal Signal Space Separation (tsss) filtering. Subsequent analyses

were performed using the GUI-based MatLab script Brainstorm (Tadel, Baillet, Mosher,

Pantazis, & Leahy, 2011) which is documented and freely available for download

online under the GNU general public license (http://neuroimage.usc.edu/brainstorm).

MEG data were low-pass filtered (0- 40Hz) and signal space projection was applied to

remove ECG and eye-blink artefact.

For each frame of stimulation, a 300ms epoch (-133 to 267ms relative to the

video frame onset) was imported into the Brainstorm database. Custom mfVEP analysis

scripts were written in Matlab/Brainstorm in order to extract K1, K2.1 and K2.2 kernel

responses for the four central quadrants. K1 is the difference between responses to the

light and dark patches. K2.1 measures neural recovery over one frame (16.7ms) by

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comparing responses when a transition did or did not occur. K2.2 measures neural

recovery over two frames (33.3ms); it is similar to K2.1, but includes an interleaving

frame of either polarity (Sutter, 1992). This procedure created 60 output files for each

participant (4 quadrants x 3 kernels x 5 saturation levels).

Cortical reconstruction and volumetric segmentation for the T1-weighed images

was performed with the Freesurfer image analysis suite, which is documented and freely

available for download (http://surfer.nmr.mgh.harvard.edu/). Freesurfer morphometric

procedures have been demonstrated to show good test-retest reliability across scanner

manufacturers and across field strengths (Fischl, 2012). Anatomical scans were aligned

with the MEG sensors in Brainstorm using six fiducial points (nasion, left and right

preauricular points, anterior and posterior commissure and interhemispheric points). An

iterative algorithm was used to refine the coregistration based on the additional digitised

head points.

Head models for each participant were created using Brainstorm’s overlapping

spheres technique. Data covariance matrixes were computed from the recordings, and

the noise covariance matrix was computed from a noise recording collected within one

day of as the subject’s recordings. Next, cortical sources of MEG sensor activity were

estimated using Minimum Norm Imaging (MNI), and then z-score baseline

normalisation was applied. Absolute values were calculated because the signs of the

minimum norm maps are relative to the current with respect to the surface normal,

which can vary between subjects. Source maps for individual participants were then re-

interpolated on to a common template (the ICBM152 brain in the Brainstorm anatomy

folder), with 3mm full width half maximum spatial smoothing. After this stage, further

statistical comparisons were performed on the source maps.

5.4.6 MEG statistical analyses

Mean-centred partial least squares (PLS) analyses (Krishnan et al., 2011) as

implemented through Brainstorm (Shafiei, 2017), were used to asses relationships

between the chromatic saturation conditions and cortical responses. 12 separate PLS

analyses were performed for the four stimulus quadrants and three kernel responses.

PLS operates on the entire data structure at once, to extract the patterns of maximal

covariance between two data matrices i.e., the chromatic saturation levels and cortical

source data. Statistical significance was assessed in two steps. Firstly, singular value

decomposition (SVD) was applied to the mean-centred matrix, to produce a latent

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variable (LV) that relates the two matrices (Shafiei, 2017). To test for statistical

significance, p-values for the latent variables were calculated from a 500-trial

permutation test, such that high bootstrap ratios indicate a large contribution of the

latent variable and a small standard error (Efron & Tibshirani, 1986). Hence, if we

presume an approximately normal distribution, bootstrap ratios can be seen as

equivalent to z-scores, with values greater than 2.58 indicating reliable effects (Shafiei,

2017).

5.5 Results

5.5.1 Sensor space analyses

An initial sensor-based analysis was performed on K1, K2.1 and K2.2

waveforms for a cluster of posterior/occipital gradiometers (principal components

derived). For the sake of comparison with previous EEG studies, the polarity of each

cluster waveform was corrected based on existing knowledge of the major peak and

trough latencies for the first and second-order kernel VEP responses (as per Crewther,

Brown, & Hugrass, 2016). Similar responses were recorded from each of the central

multifocal quadrants, so the polarity-corrected waveforms were averaged across

quadrants and participants. Mean K1, K2.1 and K2.2 responses are presented in Figures

5.2a, b and c respectively, with coloured shading to illustrate the chromatic saturation

levels. Peak amplitudes and latencies are plotted against saturation in Figures 5.2d –

5.2i, for the K1N60, K1P90, K2.1N70, K2.1P100, K2.2N75, and K2.2P110 waveforms

respectively, with separate traces for each of the central multifocal quadrants. These

waveform latencies are broadly consistent with previous studies that used EEG

(Crewther & Crewther, 2010) and MEG (Crewther et al., 2016).

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Figure 5.2 Sensor space analyses. Mean magnetic evoked K1 (a), K2.1 (b) and K2.2 (c) waveforms are shown for an occipital sensor cluster (principle component derived). The shading represents the blue chromatic desaturation series from 95% to 0%. Mean amplitudes and latencies for the first major troughs and peaks are presented for the K1 (d-e), K2.1 (f-g) and K2.2 (h-i) waveforms, with chromatic saturation plotted on the x-axes. Responses to the central multifocal quadrants are presented in different coloured traces (red: top right, orange: top left, green: bottom left, purple: bottom right). The error bars denote ± SEM.

The effects of blue saturation on the kernel waveform amplitudes and latencies

tended to be similar for each of the central multifocal quadrants (as illustrated in the

different coloured traces within Figures 5.2d-i), so we describe these results together.

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For the K1 waveform, chromatic saturation had stronger effects on P90 amplitudes than

N60 amplitudes (Figures 5.2d and e), with peak response latencies tending to decrease

with chromatic saturation. Consistent with Crewther and Crewther (2010), K2.1P100

response amplitudes increased steadily with the level of blue saturation (Figure 2f). In

addition there was a very strong effect of chromatic saturation on K2.1N70 amplitudes,

which was not present in Crewther and Crewther’s results. On average, K2.1 peak

latencies increased up to the 50% blue level, and then decreased with chromatic

saturation. Interestingly, the onset of the K2.1N70 response was earliest for the 95%

saturation condition (Figure 5.2b). By comparison, the effects of blue saturation on

K2.2 amplitudes and latencies were quite modest.

5.5.2 Source localisation

Minimum Norm Imaging maps were plotted to investigate the cortical sources

of the first- and second-order kernel responses to the central multifocal quadrants, at

95% blue saturation. Figure 5.3 presents separate z-score normalised maps of K2.1 and

K2.2 responses. Retinotopic organisation was as expected. Stimulation in the right and

left hemifields mapped strongly to the left and right hemispheres, and stimulation in the

upper and lower hemifields mapped strongly to visual cortical regions below and above

the calcarine sulcus. As illustrated in Figure 5.3, for each of the stimulus quadrants, the

same sources contributed to the K2.1 and K2.2 response peaks, both at 70ms and 95ms,

with stronger activations in the K2.1 response.

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Figure 5.3 Group averaged (n = 8) maps of z-score normalised MNI sources for the 95% blue saturation conditions. The separate rows illustrate the K2.1 response at 70ms (a) and 95ms (b), and the K2.2 response at 70ms (c) and 95ms (d). For each column of cortical maps, the corresponding stimulus quadrants are illustrated in the blue segments at the top of the panel (top right, top left, bottom right and bottom left). The same colour bar range was applied for all maps, displaying z-scores ranging from 0 (dark red) to >10 (white). Due to much stronger K2.1 activations, the thresholds were set to z > 6 and z > 3 for the K2.1 and K2.2 maps respectively.

To further investigate the timing of these responses, separate scouts (i.e., regions

of interest) were created for each central quadrant, which were seeded from the

maximum source response and grown to include the surrounding areas. Each scout

covered a cortical surface area of ~8.0 cm2. K1, K2.1 and K2.2 scout responses to the

top-right stimulus quadrant are shown in Figure 5.4; however, similar effects were

observed for scout responses to the other three quadrants. As illustrated in Figure 5.4,

activations were strongest for the stimulus with 95% blue chromatic saturation, with

clear effects in the K1, K2.1 and K2.2 responses. The effects of chromatic saturation on

the K2.1 response were particularly striking, with a strong and transient effect apparent

from approximately 50-75ms.

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Figure 5.4 Group averaged (n = 8) time courses of K1 (a), K2.1 (b) and K2.2 (c) responses to the top-right stimulus quadrant, as measured from a corresponding V1 scout (region of interest). The shading represents the blue chromatic desaturation series from 95% to 0%

5.5.3 PLS analyses

In order to identify cortical sources that vary reliably with chromatic saturation,

we performed 12 separate PLS analyses (i.e.; 4 quadrants x 3 kernel waveforms, see

methods section), with chromatic saturation entered as the repeated-measures condition.

The resulting cortical maps are plotted in Figure 5.5. As illustrated in Figures 5.5d, f

and i, the PLS analyses for each stimulus quadrant (separate colours) and kernel

waveform (separate plots) revealed significant latent variables (p < 0.05) that increased

with the degree of blue saturation. Scout time course of the bootstrap ratios for each

quadrant showed that chromatic saturation had reliable effects on cortical activation at

approximately 95ms in the K1 waveform (Figure 5.5e), and 70ms in the K2.1 and K2.2

waveforms (Figures 5.5f and 5g).

Cortical source maps of the K1, K2.1 and K2.2 bootstrap ratios are presented in

Figures 5.5a – c, at latencies corresponding to the peak bootstrap ratios. Each map

displays sources that are positively correlated with the latent variable (i.e. sources with

activation amplitudes that vary reliably with chromatic saturation). In comparison with

the z-score maps of responses to the 95% blue stimulus (Figure 5.3), the PLS analysis

revealed sources that were more scattered, and not as clearly mapped to retinotopically

corresponding regions of the visual cortex.

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Figure 5.5 Partial least squares analyses. Maps of the bootstrap ratios for the K1 (a) K2.1 (b) and K2.2 (c) responses, at latencies of 95ms, 70ms and 70ms respectively. The same colour bar range was applied for all maps, to illustrate bootstrap ratios from -10 (red = strong negative correlation with the latent variable) to 10 (blue = strong positive correlation with the latent variable). The thresholds were set so that only the reliable sources were mapped (Bs. ratio > 2.58). For panels a-c, the top left, top right, bottom left and bottom right cortical maps reflect responses to the corresponding stimulus quadrants. Panels d (K1), f (K2.1) and h (K2.2) are plots of the latent variables vs. saturation, with different coloured traces for each quadrant (red = top right, orange = top left, green = bottom left, blue = bottom right). Panels e (K1), g (K2.1) and i (K2.2) illustrate the bootstrap ratios for each scout time series. The horizontal dotted lines on the bootstrap ratio plots indicate the threshold over which values are considered reliable (Bs. ratio = 2.58).

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5.6 Discussion We investigated the non-linear temporal structure of magnetic evoked fields, for

a blue chromatic saturation series of diffuse multifocal stimuli, with the temporal

luminance contrast held constant across conditions (30% Michelson). Our analyses

revealed that chromatic saturation modulates cortically evoked responses at very early

stages of visual processing. Interestingly, the PLS analyses showed that the strongest

effects of chromatic saturation on cortical response amplitudes were in the first slice of

the second order non-linear temporal kernel (K2.1). As discussed below, these results

suggest that responses to chromatic saturation are dominated by a visual mechanism

that recovers rapidly from temporal stimulation, which may reflect M (and possibly K)

input to the cytochrome oxidase blob centres in V1 layers 2/3.

Our MEG findings are broadly consistent with EEG results from Crewther and

Crewther (2010). The key difference is that they observed strong effects of chromatic

saturation on the K2.1 response peak at approximately 105ms, with relatively small

effects on the preceding negativity. By contrast, our analyses consistently showed a

strong effect of chromatic saturation on the K2.1 response at approximately 70ms, with

a lesser effect for the peak at 95ms. A possible explanation for this difference could be

that Crewther and Crewther (2010) analysed responses to a single foveal stimulus patch

(4 degrees diameter), so there may have been some signal cancelation from cortical

sources above and below the calcarine sulcus. We addressed this issue in the current

experiment by stimulating the central visual field with temporally de-correlated

quadrants. This is likely to have reduced the degree of source cancellation and temporal

blurring in the waveforms (Baseler & Sutter, 1997; Baseler et al., 1994). Alternatively,

these differences in our results may, at least in part, reflect differences in the sensitivity

of MEG and EEG to radial and tangential sources (Ahlfors, Han, Belliveau, &

Hämäläinen, 2010).

For the 95% blue saturation level, cortical sources of the major K2.1 and K2.2

waveforms were strongly and consistently localised to V1 regions corresponding

retinotopically to the top left, top right, bottom left and bottom right quadrants of the

visual field. The PLS analysis revealed cortical source responses that reliably co-varied

with chromatic saturation; however the maps of the bootstrap ratios across the visual

cortex were not as clearly retinotopically organised. This might in part reflect

differences in the peak latencies of the evoked field responses to stimuli at different

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blue saturation levels. Alternatively, this may reflect differences in the mapping of

surface and form colour in the cortex. Future studies that perform more detailed source

modelling, taking into account individual differences in cortical folding (Ales, Carney,

& Klein, 2010), may shed further light on the cortical mapping of responses to

chromatic saturation.

Consistent with the results of a macaque physiological study (Givre et al., 1995),

the highly saturated blue multifocal stimulus evoked the fastest VEP onset latencies.

This finding is intriguing, given that blue-on signal is transmitted to the cortex via the K

pathway (Hendry & Reid, 2000), yet K cells in the LGN tend to have sluggish temporal

responses (Irvin, Casagrande, & Norton, 1993). It has been shown that blue-on and

blue-off signals leaving the LGN via the K pathways are slower than signals leaving

from the P and M pathways (Pietersen et al., 2014). This latency delay could be reduced

at the cortical level, due to the direct projection of blue-on input from the LGN to

cytochrome oxidase blobs in V1 layer 3 (Chatterjee & Callaway, 2003). Another

possible explanation of the decrease in VEP response latencies with blue light could be

that S-cones provide approximately 10% of the input to M cells (Chatterjee &

Callaway, 2002), and some M cells project to the centres of cytochrome oxidase blobs

in V1 layers 2/3 (Edwards et al., 1995).

In agreement with previous EEG studies (Crewther & Crewther, 2010;

Klistorner et al., 1998), diffuse chromatic saturation had stronger effects on K2.1 than

K2.2 amplitudes. The K2.1 and K2.2 non-linearities are interpreted in terms of neural

recovery over one and two video frames (in this case 16.7 and 33.3ms) respectively.

Studies of the achromatic contrast response functions suggest that the K2.1 and early

K2.2 waveforms reflect input from the M pathway, and the main K2.2 waveform

reflects input from the P pathway (Jackson et al., 2013; Klistorner et al., 1997).

Consistent with a M generator, K2.1 amplitudes correlate well with psychophysically

measured flicker fusion thresholds (Brown, Corner, Crewther, & Crewther, Submitted

for Publication). For diffuse stimulation, the P pathway does not appear to contribute to

the K2.1 waveform (Klistorner et al., 1997). Although contributions from the K system

to the K2.1 and K2.2 waveforms have not been specifically investigated, the temporal

responses of K cells (Pietersen et al., 2014) indicate it is unlikely that they would

contribute strongly to the K2.1 waveform. Therefore, our results imply that the effects

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of blue saturation on VEPs are generated by a neural mechanism with rapid, ‘M –like’

recovery from stimulation.

Evidence from macaque physiology suggests that the effects of blue and red

chromatic saturation on VEPs are unlikely to reflect chromatic processing at the level of

the LGN (Givre et al., 1995). In V1, red and blue lights tended to evoke higher

amplitude responses than green or white lights, particularly in layers 2/3 and 4Cβ. There

was no evidence of any chromatic enhancement at the level of the LGN, which suggests

these wavelength-specific effects of diffuse colour on VEPs arise at the cortical level.

Therefore, we interpret the effects of diffuse blue saturation on K2.1 responses in terms

of M input to the cytochrome oxidase blobs. This is consistent with evidence that

temporal frequency sensitivity and firing rates tend to be higher for cells within than in-

between blob centres (Economides, Sincich, Adams, & Horton, 2011; Gur & Snodderly,

1997; Shoham et al., 1997). Therefore, the effects of diffuse chromatic saturation on

VEPs may reflect rapid processing of M (and possibly K) inputs to the cytochrome

oxidase blob centres.

5.7 Conclusions In conclusion, we observed very early effects of blue chromatic saturation on

non-linear magnetic evoked fields. PLS analyses showed that the effects of chromatic

saturation are strongest for the fast-latency K2.1 response. This suggests a potential role

for the M and K projections to V1 cytochrome oxidates blob centres, in the rapid

processing of diffuse chromatic signal. This may facilitate figure ground segmentation

during the first-pass analysis of the visual scene.

5.8 Acknowledgements This research was funded by the Australian Government, through the Australian

Research Council (DP150104172).

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Chapter 6: Intranasal Oxytocin Modulates Very Early Visual

Processing of Emotional Faces

6.1 Chapter guide Hugrass, L., Labuschagne, I., Price, A., & Crewther, D. (In Submission). Part

1: Intranasal oxytocin modulates very early visual processing of emotional faces.

Chapter 6 comprises a re-formatted version of the original research article cited

above, which has been submitted to Hormones and Behaviour as the first of a two-part

original research article. The second part is presented in Chapter 7. David Crewther

presented the preliminary analyses at the 2017 Australasian Cognitive Neurosciences

Society meeting. Taken together, Chapters 6 and 7 contribute to the understanding of

the neural mechanisms by which oxytocin influences affective processing.

For the work presented in the current chapter, I used conventional VEP to

investigate the effects of OXT on the timing of affective face processing. For the work

presented in Chapter 7, I investigated the effects of OXT on M or P signatures of non-

linear VEPs. Data for these two experiments were collected in the same study, for the

same sample of healthy male adults.

6.1.1 Highlights

• Nasal oxytocin affected visual evoked potentials (VEPs) to emotional face

stimuli

• At 40-60ms, oxytocin reduced amplitudes of right and central VEPs to fearful

faces

• At ~100ms, oxytocin reduced amplitudes of left VEPs to fearful faces

• At 400-600ms, oxytocin reduced VEP amplitudes to fearful, happy and neutral

faces

• Oxytocin did not influence the accuracy or latency of emotion identification

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6.2 Abstract Functional imaging and behavioural studies have shown that the neuropeptide

oxytocin influences processing of emotional faces. However, it is not clear whether

these effects reflect modulation at the early or late stages of affective processing. We

investigated the effects of oxytocin administration on early and late visual evoked

potentials (VEP) in response to faces with neutral, fearful and happy expressions. In a

randomized, double-blind, cross-over design, 27 healthy male participants self-

administered a nasal spray of either oxytocin (24 IU) or placebo. At very early latencies

(40-60ms), oxytocin reduced right-temporal responses to fearful faces (d = .51), and

central responses to both fearful (d = .48) and neutral faces (d = .54). At left occipito-

temporal electrode sites, oxytocin decreased P100 reactivity to fearful expressions (d =

0.72). At later stages of visual processing, oxytocin decreased the amplitudes of the

vertex positive potential (140-180ms) and late positive potential (400-600ms),

regardless of whether the faces had fearful, happy or neutral expressions. These results

suggest that at early stages of visual processing, oxytocin modulates responses to facial

emotions, whereas at later stages of visual processing, it appears to influence more

general face processing mechanisms.

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6.3 Introduction The neuropeptide oxytocin (OXT) is most commonly known for its powerful

role in childbirth and mother-infant bonding, but has also shown to have a crucial role

in modulating complex social behaviours in humans (Bartz & Hollander, 2008; Meyer-

Lindenberg, 2008; Meyer-Lindenberg, Domes, Kirsch, & Heinrichs, 2011). Some

authors have argued that this modulation can be traced to the effects of OXT on sensory

processing of affective cues (Domes et al., 2007). The amygdala plays an important role

in processing social stimuli (Adolphs, 2008; LeDoux, 1998). Early amygdala responses

to affective stimuli appear to be automatic, and do not require awareness, whereas later

responses are modulated by attention and are more highly tuned for behaviourally

relevant input (Adolphs, 2008; Anderson, Christoff, Panitz, De Rosa, & Gabrieli, 2003).

Functional imaging studies have shown that intranasal OXT administration can suppress

amygdala responses to fearful and happy faces (Domes et al., 2007; Kirsch et al., 2005),

and influence functional connectivity between the amygdala and the superior colliculus

(Gamer, Zurowski, & Büchel, 2010) and frontal cortex (Sripada et al., 2012). Intranasal

OXT has also been shown to improve recognition of facial emotions (Lischke et al.,

2012; Marsh, Henry, Pine, & Blair, 2010; Schulze et al., 2011). Interestingly, intranasal

OXT normalizes amygdala responses to affective faces in groups with generalized

social anxiety disorder and autism (Domes, Heinrichs, et al., 2013; Domes, Kumbier,

Heinrichs, & Herpertz, 2014; Labuschagne et al., 2010).

Due to the limited temporal resolution of fMRI, it is unclear whether OXT

primarily influences affective processing at early or late stages of the visual hierarchy.

Other techniques, such as visual evoked potentials (VEPs) have been applied to

investigate the timing of affective processing (Hajcak, Dunning, & Foti, 2009; Pourtois,

Schettino, & Vuilleumier, 2013). The visual P100 is a fast response that typically peaks

between 80 and 120ms, and appears to originate from striate and extrastriate neural

generators (Allison, Puce, Spencer, & McCarthy, 1999; Clark & Hillyard, 1996).

Several studies have shown that P100 responses are influenced by the perceptual

processing of facial expressions, with greater amplitudes in response to viewing fearful

and happy faces than to neutral faces (Batty & Taylor, 2003; Vlamings, Goffaux, &

Kemner, 2009). There is also evidence that viewing of facial emotions influences EEG

and MEG activity even prior to the P100, as early as 40-60ms post stimulus

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presentation (Liu & Ioannides, 2010; Morel, Beaucousin, Perrin, & George, 2012;

Morel, Ponz, Mercier, Vuilleumier, & George, 2009).

The N170 and the vertex positive potential (VPP) are responses that occur

between 140 and 190ms, over right occipito-temporal and central sensors, respectively

(Henson et al., 2003; Joyce & Rossion, 2005; Vlamings et al., 2009). Both the

negativity and positivity reflect configural face processing, with greater responses to

faces than objects, and to upright than inverted faces (Bentin, Allison, Puce, Perez, &

McCarthy, 1996; Joyce & Rossion, 2005). While these responses are sensitive to

emotion, some evidence suggests their amplitudes are modulated by emotional

intensity, rather than valence (Luo, Feng, He, Wang, & Luo, 2010). Although the

literature has treated the N170 and VPP as two separate components, there is some

evidence to suggest they are two sides of the same generator (Joyce & Rossion, 2005).

The late positive potential (LPP) is a centrally generated, positive potential that

begins 300 to 400ms after stimulus presentation and continues for several hundred

milliseconds (Crites, Cacioppo, Gardner, & Berntson, 1995). Previous studies have

shown that LPP amplitudes are greater for both pleasant and unpleasant stimuli than for

neutral stimuli (Hajcak et al., 2009; Pastor et al., 2008). LPP amplitudes appear to

reflect both automatic capture of attention, and goal-directed attention towards

motivationally relevant stimuli (Hajcak et al., 2009).

There have not been many VEP studies into the effects of OXT administration

on affective processing (reviewed, Wigton et al., 2015). In a sample of healthy females,

Huffmeijer et al. (2013) investigated the effects of OXT administration on VPP and

LPP responses to happy and disgusted faces, which were presented as performance cues

in a flanker task. OXT increased VPP and LPP amplitudes, regardless of the emotional

valence of the faces. However, the study did not include a neutral face condition, so it is

unclear whether these results specifically reflect modulation of affective processing, or

face processing mechanisms in general. Althaus et al. (2015) compared the effects of

intranasal OXT on LPP responses to positive, negative and neutral scenes in males with

functioning autism and a healthy male control group. There were no effects of OXT for

either group; however, they found some effects of OXT on LPP responses to scenes

featuring people, but only for healthy participants with high sensitivity to punishment,

and for autistic participants with high blood plasma levels of OXT at baseline (Althaus

et al., 2016). Differences in the effects of OXT on affective processing for these two

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studies (Althaus et al., 2015; Huffmeijer et al., 2013) may be due to the fact that OXT

has a greater modulatory influence on amygdala responses to faces than to scenes

(Kirsch et al., 2005).

To our knowledge, no previous studies have investigated the effects of OXT

administration on the early visual responses to affective faces. We therefore compared

early and late VEP responses to neutral, fearful and happy faces after the administration

of nasal sprays containing OXT or a placebo. Previous studies have shown that OXT

administration does not influence P100 or N170 responses to non-affective face stimuli

(Herzmann, Bird, Freeman, & Curran, 2013; Rutherford et al., 2017). However, based

on evidence that OXT administration inhibits amygdala reactivity to fearful and happy

faces (Domes et al., 2007), we hypothesized that it would reduce the effects of facial

emotion processing on early (40-60ms), P100, N170 and VPP responses. Later

potentials (LPP) are influence by both stimulus salience and goal directed attention

(Hajcak et al., 2009), and therefore it was unclear whether OXT would enhance or

diminish the effects of facial emotion processing on LPP amplitudes. We also

investigated whether individual differences in trait-level autism and social anxiety may

influences the effects of OXT on VEP responses.

6.4 Methods

6.4.1 Participants

A power analysis, calculated using G*Power (Faul, Erdfelder, Lang, & Buchner,

2007), indicated that a sample of 27 participants is adequate to detect a mean difference

ERP responses between the OXT and placebo sessions, with α = 0.05, β = 0.80, and

moderate effect size (Cohen’s d = 0.5). 27 healthy males aged 18 to 40 (24 right

handed, M = 25.22 years, SD = 4.72 years), gave written informed consent for the

experiment, which was conducted with the approval of the Australian Catholic

University Human Research Ethics Committee and in accordance with the code of

ethics of the Declaration of Helsinki. One additional participant completed the placebo

session, but did not return for the oxytocin session and hence his results were excluded

from the analysis. The happy facial expression condition was not added to the protocol

until after the first four participants had completed the experiment, so we only have data

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for 23 participants in this condition. People who regularly used other nasal sprays were

not invited to participate in the study.

6.4.2 Questionnaires

Prior to attending the lab sessions, participants completed online versions of

several scales. For the online emotion recognition test (Labuschagne et al., 2013),

participants reported the emotion (anger, disgust, fear, happiness, sadness, and surprise)

of 60 faces from the Ekman and Friesen face set (Ekman & Friesen, 1976). The Autism

Spectrum Quotient (AQ) (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley,

2001) measures autistic personality traits, with higher scores indicating higher levels of

trait autism (range, 0–50). The Social Interaction Anxiety Scale (SIAS) (Mattick &

Clarke, 1998) was used to measure social anxiety, with higher scores indicating a

greater degree of social anxiety (range, 0–80). The State-Trait Anxiety Inventory

(Spielberger, 2010) was administered to measure state anxiety prior to nasal spray

administration (STAIpre), and the change in state anxiety by the end of the session

(STAIchange). State anxiety results from this sample are reported in more detail in Part 2

(Hugrass & Crewther, Submitted for Publication). In short, STAIchange was not

significantly affected by OXT or PBO administration so this variable was redundant,

and it was not included in the current analyses. We entered STAIpre as a covariate in the

preliminary models, to test whether subjective reports of state anxiety may influence the

results.

6.4.3 Facial emotion VEP task

The task was created using VPixx software (version 3.20,

http://www.VPixx.com), and presented using a DATAPixx display driver and a

Viewsonic LCD monitor (60Hz, 1024 x 768 pixel resolution) with linearized colour

output (as measured with a ColorCal II – Cambridge Research Systems). Seven face

identities (3 female) were selected from the Nimstim Face Set (Tottenham et al., 2009)

with neutral, fearful and happy expressions (all with closed mouths, to minimise low-

level differences between the stimuli). The images were converted to greyscale, the

external features (hair, neck and ears) were removed, and the luminance and root mean

square contrast were equated using a custom Matlab script (The Mathworks, Natick,

MA). The stimuli were presented within a 20 × 19.5 degree mid-grey frame (47 cd/m2)

on a grey background (65 cd/m2).

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Participants were seated 70 cm in front of the display. A phase-scrambled

neutral face (luminance and RMS contrast matched with the test stimuli) was presented

during the baseline period (1800ms), followed by the target face (500ms), and a central

fixation cross. After the face disappeared, participants used a RESPONSEPixx button

box to report whether the expression was fearful (right button), neutral (top button) or

happy (left button). Participants were told that this was not a speeded task, and were

instructed to respond as accurately as possible. In total, there were 240 trials (80

replications each, for the fearful, neutral and happy faces). To prevent fatigue, the task

was divided into two blocks of 120 trials, with a self-timed break in between blocks.

6.4.4 Procedure

Participants attended the laboratory twice, with at least a one-week washout

period in between sessions. Participants were asked on the day of testing to confirm that

they are not suffering from illness or congestion. Participants were instructed not to

drink alcohol on the night before their session, to refrain from drinking caffeine on the

day of their session, and to refrain from eating or drinking (except for water) within an

hour of their session. The order of treatment conditions was counterbalanced across

participants. The spray bottles were relabelled so that neither the experimenters nor

participants were aware of which spray bottle contained the oxytocin. Participants self-

administered OXT (24 IU) or placebo sprays (PBO, containing all ingredients except for

the peptide). Participants were given standardised instructions for how to administer the

sprays, as per the recommended guidelines (Guastella et al., 2013). Spray bottles were

first primed by puffing three sprays into the air, then participants inhaled a full spray in

one nostril (4 IU), and waited 45s before inhaling a full spray into the opposite nostril.

This was repeated until they had made three sprays per nostril. The facial emotion VEP

task commenced at approximately 45 minutes after the last spray application (OXT: M=

43.81, SD= 4.00, PBO: M = 44.37, SD= 2.99).

6.4.5 EEG recording and pre-processing

EEG was recorded using a 64-channel cap (Neuroscan, Compumedics). The data

were sampled at 1KHz and band-pass filtered from 0.1-200Hz. The ground electrode

was positioned at AFz and linked mastoid electrodes were used as a reference. EOG

was monitored using electrodes attached above and below one eye. Data were processed

using Brainstorm (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011), which is

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documented and freely available for download online under the GNU general public

license (http://neuroimage.usc.edu/brainstorm).

EEG data were band-pass filtered (1- 40Hz), signal space projection was applied

to remove eye-blink artefact and other segments of data containing low frequency noise

(1 – 7Hz) were removed from the analysis. Baseline corrected (100ms to -1ms) epochs

of data (-100 to 900ms) around the onset of each face presentation were imported into

the Brainstorm database. Epochs containing high amplitude noise (>75µV) were

excluded from the analysis. There were no consistent differences in the number of

artefact contaminated epochs for the OXT and PBO session; however in order to

minimise bias in each participant’s peak amplitude estimates (Luck, 2010), a subset of

trials from the condition with more epochs was randomly selected so that the same

number of trials contributed to the VEP waveforms for the OXT and PBO conditions

(total number of epochs for the OXT and PBO sessions: Neutral = 1,942; Fearful =

1,948; Happy = 1,614).

P100, N170, VPP and LPP amplitudes were detected using Labview (National

Instruments), within time windows and electrode clusters that were based on previous

research (Batty & Taylor, 2003; Luo et al., 2010; Pastor et al., 2008; Vlamings et al.,

2009). Early responses were defined as the mean amplitude from 40-60ms post-

presentation for the central (C1, Cz, C2, CP1, CPz, CP2, FC1, FCz, FC2), left (PO7,

P7), and right (PO8, P8) electrode clusters (Liu & Ioannides, 2010). N170 and P100

responses were detected from right (PO8, P8) and left (PO7, P7) clusters of occipito-

temporal electrodes. P100 was defined as the maximum amplitude between 80-120ms.

N170 was defined as the minimum amplitude between 140-190ms. VPP was defined as

the peak amplitude within the 140-190ms time-window for a central electrode cluster.

In our analyses, the LPP response tended to be strongest from 400-600ms post-

presentation, so LPP was defined as the average amplitude over the 400-600ms time

window for the central cluster of electrodes.

6.4.6 Statistical Analyses

The data were analysed using the linear mixed-effects modelling (LMM)

procedure in SPSS, because of its advantage in dealing with missing values, and ability

to handle unequal variance and correlated data. Separate analyses were performed for

the behavioural reporting (accuracy and latency) dependent variables, and for each of

the VEP dependent variables described above. For the main analyses, important

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predictors were entered as covariates, and treatment condition (OXT vs. PBO), facial

emotion (N, F, H) and the treatment by emotion interaction were entered as fixed

effects. To account for individual differences in VEP amplitudes, random intercepts

were modelled for each subject. In repeated-measures designs it is likely that the error

terms are correlated within subjects. Hence, we used a first-order, autoregressive (AR1)

covariance matrix for our models. The SPSS LMM procedure does not produce R2

effect sizes, because these definitions become problematic in models with multiple error

terms. Where appropriate, Cohen’s d was calculated as the effect size for treatment and

emotion comparisons.

For each result, we ran a preliminary LMM to investigate which of the variables

contribute significantly to the models. For the analyses of behavioural response

accuracy and latency, the preliminary covariates included AQ (range = 1-38, M =

17.48, SD= 7.71), SIAS (range = 11-50, M = 28.44, SD=1.69), and emotion recognition

scores (range = 42-58, M = 51.87, SD= 3.82), as well as STAIpre (as measured at prior

to the nasal spray administration: OXT range = 20-58, M = 33.00, SD= 8.66, PBO

range = 20-59, M = 32.22, SD= 9.60). To test whether other variables contributed to

variation in behavioural responses, we also included time of day, and task latency (i.e.,

the time from the last nasal spray administration to the start of the facial emotion VEP

task) as covariates. To take into account any relationship between accuracy and latency,

response latency and accuracy were included in the respective models. Given that

participants were instructed to wait to respond until after the face disappeared, we

expect variation in response latencies to reflect the degree of difficulty in identifying the

emotion, rather than the speed of identification per se.

For the VEP analyses, the preliminary LMMs included AQ, SIAS, STAIpre,

behavioural response latency, time of day, and VEP task latency (from the time of the

last spray administration) as covariates. To control for individual differences in

behavioural reporting, we included response latency instead of response accuracy as a

covariate, because these variables were correlated and there were ceiling effects for

response accuracy. If a variable contributed significantly to the preliminary LMM, it

was included as a covariate in the subsequent LMMs. For the P100 and N170 analyses,

the fixed-effects for the LLM included factorial comparisons of the treatment,

hemisphere, and emotion conditions. The VPP and LPP responses were recorded from a

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central cluster, so only treatment and emotion were included as repeated-measures

fixed-effects in these analyses.

6.5 Results

6.5.1 Preliminary analyses of behavioural data

Preliminary LMMs were conducted on response accuracy and latency (as

described above). Response latency (F(1, 69.3) = 7.47, p = .008) contributed

significantly to the model of response accuracy, so it was included as a covariate in the

main analysis. Response accuracy (F(1, 92.7) = 6.17, p = 0.015) and AQ (F(1, 16.7) =

14.31, p = 0.002) contributed significantly to the model of response latency, so these

variables were included as covariates in the main analysis. Behavioural response

latencies tended to be slower for participants with lower accuracies and with higher AQ

scores (see Figure 6.1c and d). SIAS, STAIpre, emotion recognition scores, time of day,

and VEP task latency did not contribute substantially to either model.

6.5.2 Main analyses of behavioural data

Estimated marginal means (EMMs) for behavioural accuracy (corrected for

response latency) and latency (corrected for response accuracy and AQ) are presented in

Figure 6.1a and b. The face stimuli had high intensity expressions and were presented

for 500ms, so it is not surprising that performance was often either at ceiling level or

close to ceiling level. The mixed-effects analysis for response accuracy revealed a

significant effect of emotion (F(2, 136.3) = 11.70, p < 0.001). Pairwise EMM

comparisons (Bonferroni corrected) showed that accuracy tended to be higher for happy

faces than for fearful (p = 0.028, d = 0.69) or neutral faces (p < 0.001, d = 1.09). The

main analysis for response latency revealed a significant effect of emotion (F(2, 99.9) =

173.24, p < 0.001). Pairwise EMM comparisons (Bonferroni corrected) showed that

latencies tended to be slower for neutral faces than for happy (p = 0.013, d = 0.43) or

fearful faces (p < 0.001, d = 1.97). For both the accuracy and latency LMMs, there was

no effect of treatment, and there was no interaction between the effects of treatment and

emotion (Fs ≤ .33, ps ≥ .57). In summary, regardless of treatment, responses tended to

be more accurate for happy faces than for fearful and neutral faces, and slower for

neutral faces than for affective faces.

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Figure 6.1 Behavioural results. Estimated marginal means for response accuracy (a) (corrected for individual differences in response latency), and (b) response latency (corrected for individual differences in response accuracy and AQ scores). Separate means are presented for the neutral (N), fearful (F) and happy (H) faces, error bars denote ±SEM. Scatter plots of (a) response accuracies versus latencies and (d) response latencies versus AQ scores. The results from the placebo (PBO) and oxytocin (OXT) sessions are presented in blue and red, respectively. Latencies prior to the response cue (i.e.; disappearance of the face stimulus) are shaded in grey.

6.5.3 Preliminary analyses of VEPs

Grand averages of the visual evoked potentials are presented in Figure 6.2, with

separate traces for the neutral (n = 27), fearful (n = 27), and happy (n = 23) face

conditions, and separate panels for the different electrode clusters and treatment

conditions.

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Figure 6.2 Grand mean visual evoked potentials. Panels (a), (b) and (c) present results from the placebo (PBO) session for the left (PO7, P7), right (PO8, P8), and central (C1-FC2) electrode clusters, respectively. Results from the oxytocin (OXT) session are presented for the left (d), right (e) and central (f) electrode clusters. Responses to the neutral (N), fearful (F) and happy (H) faces are presented in the dotted, solid and dashed traces, respectively. The yellow shading illustrates the time windows for the VEP analyses. Early responses from the clusters were averaged over the 40-60ms time window, P100 and N170 responses from the left and right clusters were detected within the 80-120ms and 140-190ms time windows respectively, whereas VPP and LPP responses from the central cluster were detected within the 140-190 and 400-600ms time windows respectively.

Preliminary LMMs (see Statistical Analyses section) of the early (40-60ms),

P100, N170, VPP and LPP responses were conducted to identify which covariates to

include in the main LMMs. Behavioural response latency contributed significantly to

models of P100 amplitude, N170 amplitude, N170 latency, and LPP amplitude (Fs ≥

5.00, ps ≤.027), and the effect was approaching significance for early amplitude (40-

60ms) (F(1,106.4) =3.67, p = .058) and P100 latency (F(1,145.2) =3.24, p = .074). At

greater behavioural response latencies, VEP amplitudes tended to be smaller and VEP

latencies tended to be longer. Hence, behavioural response latency was included as a

covariate in the main LMMs for these dependent variables. SIAS contributed

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significantly to the model of early amplitudes for the central cluster (F(1,21.3) =5.32, p

= .031); those with higher social anxiety tended to have stronger, central negativities

during the 40-60ms time window. AQ contributed significantly to the preliminary

LMM of N170 amplitudes (F(1,22.6) =8.92, p = .007), with amplitudes tending to be

higher in those with high AQ. Hence, AQ was included as a covariate in the main N170

amplitude LMM. STAIpre, time of day and task latency did not contribute to any of the

preliminary analyses, nor was there any evidence that they interacted with the effects of

treatment, so these variables were not included in the main LMMs.

6.5.4 Early effects (40-60ms)

We observed some very early effects of OXT administration. The VEP

topographies and EMMs of amplitudes (averaged over the 40-60ms time window) are

presented in Figure 6.3. We performed separate LMMs on these early amplitudes for the

left, right and central electrode clusters. There were no effects of emotion or treatment

on the left cluster amplitudes (Fs ≤ 1.04, ps ≥ .36). The effect of treatment was

significant for the central cluster (F(1, 44.9) = 8.99, p = 0.004), and the effect of

emotion was approaching significance for the right cluster (F(2, 98.5) = 2.85, p =

0.063). Paired samples comparisons of the EMMs (Bonferroni corrected) revealed that

for the right cluster, early responses to fearful faces were significantly reduced after

OXT administration (p = 0.048, d = .51). For the central cluster, early responses to

fearful (p = 0.013, d = .48) and neutral (p = 0.005, d = .54) faces were significantly

reduced after OXT administration.

Although the preliminary analyses showed that SIAS contributed significantly to

the model for the central cluster, a follow-up analysis showed that it did not contribute

significantly to the main analysis, nor was there any SIAS by treatment interaction (Fs ≤

2.5, ps ≥ .13).

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Figure 6.3 Very early VEP effects. (a) VEP topographies at 40ms for the neutral (N), fearful (F) and happy (H) faces, after placebo (PBO) and oxytocin (OXT) administration. The left, right and central electrode clusters are marked in orange. Panels (b) and (c) present the estimated marginal means amplitudes (averaged over the 40-60ms time-window) from the right and central electrode clusters, respectively. The y-axis for panel c is reversed, so that higher bars correspond with stronger negativities. Results from the PBO and OXT sessions are presented in the blue and red bars. The error bars denote ±SEM, * p < 0.05, ** p < 0.01.

6.5.5 P100

EMMs for left and right P100 amplitudes and latencies (after correcting for

behavioural response latencies) are presented in Figure 6.4. As illustrated in Figure 6.4b

and c, the effects of emotion and treatment on P100 amplitudes were different for the

left and right clusters. For the left cluster, there was a significant interaction between the

effects of treatment and emotion (F(2, 98.5) = 14.48, p < 0.001). Pairwise EMM

comparisons (Bonferroni corrected) showed that OXT decreased the amplitude of left

P100 responses to fearful faces (p < 0.001, d = 0.72). There was also a trend towards

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OXT increasing the amplitude of left P100 responses to happy faces, but this effect did

not reach statistical significance (p = 0.063, d = 0.43). There were no other significant

effects (Fs ≤ 1.69, ps ≥ .19). For the right cluster, there were no effects of treatment or

emotion and there was no emotion by treatment interaction (Fs ≤ 1.34, ps ≥ .25). For

LMMs of left and right P100 latencies (Figures 6.4d and e), there were no main effects

or interactions for the emotion and treatment conditions (Fs ≤ 2.19, ps ≥ .12).

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Figure 6.4 P100 results. (a) VEP topographies at 103ms for the neutral (N), fearful (F) and happy (H) faces, after placebo (PBO) and oxytocin (OXT) administration. The left, right and central electrode clusters are marked in orange. Estimated marginal means for (b) left P100 amplitude, (c) right P100 amplitude, (d) left P100 latency and (e) right P100 latency in response to neutral (N), fearful (F), and happy (H) faces. The estimated marginal means are corrected for individual differences in response latency. The results from the placebo (PBO) and oxytocin (OXT) sessions are presented in the blue and red bars. The error bars denote ±SEM, *** p < 0.001.

In summary, OXT administration modulates the effects of facial emotion on

P100 amplitudes from electrodes over left occipito-temporal regions. It decreases P100

amplitudes for fearful faces and it may also increase P100 amplitudes for happy faces.

However, it does not appear to influence right P100 amplitudes or P100 latencies from

either hemisphere.

6.5.6 N170 and VPP

N170 and VPP topographies are presented in Figure 6.5a, and EMMs for the

amplitudes and latencies are presented in Figures 6.5b-g. Emotion contributed

significantly to the LMM of right N170 amplitudes (F(2, 121.0) = 4.55, p = 0.012).

Pairwise EMM comparisons (Bonferroni corrected) showed that right N170 amplitudes

were greater (i.e., more negative) for happy faces than neutral faces (p = .010, d =0.26).

The AQ and behavioural response latency covariates contributed significantly to the

model of left N170 amplitudes (Fs ≥ 6.00, ps ≤ .022), with greater amplitudes for longer

behavioural response latencies and greater AQ scores. A follow-up analysis did not

reveal any interactions between AQ and treatment. There were no other significant main

effects or treatment by emotion interactions for the left or right N170 LMMs (Fs ≤ 1.55,

ps ≥ .22).

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Figure 6.5 N170 and VPP results (a) VEP topographies at 163ms for the neutral (N), fearful (F) and happy (H) faces, after placebo (PBO) and oxytocin (OXT) administration. The left, right and central electrode clusters are marked in orange. Estimated marginal means (EMMs) are presented for (b) left N170 amplitude (c) right N170 amplitude, (d) left N170 latency, (e) right N170 latency and (f) central VPP amplitude (g) central VPP latency. The N170 EMMs (b-e) are corrected for individual differences in response latency. The N170 amplitude EMMs (b-c) are also corrected for individual differences in AQ scores. The y-axes for panels (b) and (c) are reversed, so that higher bars correspond with stronger negativities. The results from the placebo (PBO) and oxytocin (OXT) sessions are presented in the blue and red bars, respectively. The error bars denote ±SEM. * p < 0.05, ** p < 0.01

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None of the effects for the right N170 latency LMM were significant (Fs ≤ 2.26,

ps ≥ .11). There was a significant effect of emotion on the left N170 latency LMM (F(2,

135.1) = 5.15, p = 0.007). Pairwise EMM comparisons (Bonferroni corrected) showed

that left N170 latencies were longer for happy faces than neutral faces (p =0.005, d

=0.64). There was no effect of treatment, and no treatment by emotion interaction (Fs ≤

1.50, ps ≥ .23).

For the model of VPP amplitude (Figure 6.5f), there was a significant effect of

treatment (F(1, 36.5) = 7.40, p = 0.010, d =0.27), with reduced VPP amplitudes for

OXT compared to PBO. However, there was no effect of emotion, nor was there an

interaction between the effects of treatment and emotion (Fs ≤ .41, ps ≥ .66).

For the model of VPP latency (Figure 6.5g), there was a significant effect of

emotion (F(2, 93.8) = 5.93, p = 0.004). Pairwise EMM comparisons (Bonferroni

corrected) showed that latencies were significantly shorter with neutral faces than

fearful faces (p = 0.005, d =0.36) or happy faces (p = 0.034, d =0.31). However, there

was no significant effect of treatment, nor was there a treatment by emotion interaction

(Fs ≤ 1.55, ps ≥ .22).

In summary, there were some effects of facial emotion on the N170 waveforms.

Relative to neutral faces, happy faces produced higher right N170 amplitudes and

slower left N170 latencies. However, regardless of AQ score, OXT did not influence

N170 amplitudes or latencies. By contrast, VPP amplitudes tended to be reduced with

OXT relative to PBO, regardless of facial emotion. VPP latencies tended to be shorter

for neutral faces than affective faces, regardless of treatment.

6.5.7 LPP results

LPP topographies and EMMs of LPP amplitudes are presented in Figure 6.6.

Treatment contributed significantly to the LMM of LPP amplitude (F(1, 37.8) = 9.05, p

= 0.005, d =2.22). This suggests that regardless of the facial emotion, LPP amplitudes to

faces tend to be lower after OXT than after PBO. There was no significant effect of

emotion, and no treatment by emotion interaction (Fs ≤ 1.85, ps ≥ .16).

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Figure 6.6 LPP results. (a) LPP topographies at 540ms for the neutral (N), fearful (F) and happy (H) faces, after placebo (PBO) and oxytocin (OXT) administration. (b) Estimated marginal means for LPP amplitude (corrected for behavioural response latency). The results from the placebo (PBO) and oxytocin (OXT) sessions are presented in the blue and red bars, respectively. The error bars denote ±SEM, ** p < 0.01.

6.6 Discussion To our knowledge, this is the first study to report modulation of early VEP

responses to facial emotion after OXT administration. We found that OXT

administration diminished the effects of facial emotion on early VEP amplitudes. After

PBO treatment, central VEP amplitudes from 40-60ms discriminated between happy

expressions and fearful or neutral expressions, whereas early right (40-60ms) and later

left P100 amplitudes discriminated between fearful expressions and neutral or happy

expressions. The latencies and topographies of these early effects are broadly consistent

with the existing EEG and MEG literature (Liu & Ioannides, 2010; Morel et al., 2012;

Morel et al., 2009). Affective discrimination at these early latencies is unlikely to rely

on the geniculo-cortical visual processing route; rather it is thought to reflect a fast

subcortical route to the amygdala, via the superior colliculus and pulvinar (Liddell et al.,

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2005; Vuilleumier, Armony, Driver, & Dolan, 2001). The decrease in early VEP

responses to facial affect after OXT administration are interesting in light of

psychophysical evidence that OXT improves recognition of briefly (18-53ms) presented

happy and angry faces (Schulze et al., 2011). Taken together, these results support the

theory that OXT modulates the salience of social cues at very early, automatic stages of

affective processing (Bartz, Zaki, Bolger, & Ochsner, 2011; Ebitz, Watson, & Platt,

2013).

The effects of OXT on left posterior P100 amplitudes depended on facial

emotion, with reduced amplitudes for fearful faces and a trend towards increased

amplitudes for happy faces. Consistent with previous studies that only used non-

affective face stimuli (Herzmann et al., 2013), OXT did not influence P100 responses to

faces with neutral expressions. We did not observe any effects of OXT on P100

amplitudes from right, posterior electrodes. This is surprising because affective

processing of faces tends to be biased towards the right hemisphere (Ley & Bryden,

1979). Studies have shown that different aspects of affective processing involve the left

(Morris et al., 1998; Vuilleumier et al., 2001) or right amygdala (Adolphs, Damasio,

Tranel, & Damasio, 1996; Pegna, Khateb, Lazeyras, & Seghier, 2005). Likewise, acute

OXT administration can modulate right (Domes et al., 2007), left (Domes et al., 2014)

or bilateral (Labuschagne et al., 2010) amygdala reactivity to emotional faces. The

neuromodulatory role of OXT on face processing appears to be more complicated than

one would predict based on a simple right-hemisphere dominated model of emotional

processing.

Despite the fact that N170 and VPP responses occur at the same time, and may

share a common generator (Joyce & Rossion, 2005), we observed differences in the

effects of OXT administration on the two potentials. Across the facial emotion

conditions, VPP amplitudes tended to decrease after OXT administration, whereas

N170 amplitudes were unaffected by treatment condition. Huffmeijer et al. (2013)

found that OXT increased the amplitude of VPP responses to disgusted and happy

faces. They interpreted this finding as evidence that OXT modulates early affective

processing, but their experiment did not include a neutral face condition. Our results

indicate that the effects of OXT administration on VPP responses are not specific to

affective stimuli, and may reflect a more general modulation of face processing.

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However, even if this is the case, it is unclear why OXT modulated VPP but not N170

responses.

Consistent with our VPP analyses, OXT administration decreased the amplitude

of LPP responses to faces with neutral, fearful and happy expressions. This is contrary

to Huffmeijer et al. (2013), who found that OXT increased the amplitude of VPP and

LPP responses to disgusted and happy faces. It is important to note that Huffmeijer et

al.’s participants were healthy females and ours were healthy males. There are

significant sex differences in brain responses to OXT; fMRI studies have found that

OXT suppresses amygdala reactivity to affective faces in healthy males (Domes et al.,

2007) while enhancing it in healthy females (mid-luteal phase) (Domes et al., 2010).

Future VEP studies that directly compare the timing of affective processing in male and

female participants are required to elucidate potential sex differences in the

neuromodulatory effects of OXT on affective processing.

We did not observe any effects of OXT administration on the accuracy or

latency of emotion identification. In order to evoke strong electrophysiological

responses, our face stimuli had strong emotional intensity, and long presentation

durations (500ms). This meant that identification accuracy was close to ceiling level in

both the PBO and OXT conditions. Previous studies have shown that OXT has greater

effects on emotion identification for face stimuli with low intensity expressions

(Lischke et al., 2012; Marsh et al., 2010) and short presentation durations (i.e., 100ms

cf. 500ms) (Domes, Sibold, et al., 2013). Hence, our results are not necessarily

inconsistent with the literature on the effects of OXT on facial emotion identification.

It is well known that there are individual differences in the effects of OXT on

social processing (Bartz et al., 2011). Previous studies have shown that OXT has

differential effects on neural responses to emotional faces in groups with autism and

generalized social anxiety disorder, relative to healthy control groups (Domes,

Heinrichs, et al., 2013; Labuschagne et al., 2010, 2012). Hence, for our healthy male

sample, we investigated whether differences in autistic traits and social anxiety may

influence the effects of OXT on VEPs. We found that participants with higher levels of

autistic tendency tended to have higher N170 amplitudes, yet we did not find any

evidence to suggest that autistic tendency or social anxiety interacted with the effects of

OXT administration. Although there were wide ranges of AQ and SIAS scores in our

sample (see analysis section), we did not specifically recruit people with extreme

141

scores, i.e; AQ scores greater than 23, which is 1 SD above the population mean

(Ruzich, et al., 2015). Measuring autistic traits in the general population: a systematic

review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of

6,900 typical adult males and females. Molecular autism, 6(1), 2.

). Given the small sample size, our study was not sufficiently powerful to detect

weak to moderate correlations, if they exist at the population level. However, previous

studies found that OXT only modulated LPP responses in healthy males with high

sensitivity to punishment, and autistic males with high baseline levels of OXT in their

blood (Althaus et al., 2015; Althaus et al., 2016). This suggests there may be nuanced

individual differences in the effects of OXT on VEPs.

6.7 Conclusions In summary, analysing VEPs enabled us to study the effects of OXT

administration on the early and late stages of affective processing. OXT administration

decreased reactivity to fearful expressions as early as 40-60ms for central and right

electrode clusters, and as early as the P100 for left electrode clusters. It may also

increase left P100 reactivity to happy expressions. By contrast, OXT administration

decreased VPP and LPP amplitudes regardless of facial emotion. Our results indicate

that OXT modulates the salience of social cues at very early stages of visual processing

(Bartz et al., 2011; Ebitz et al., 2013), and that it modulates more general face

processing mechanisms at later stages of visual processing.

6.8 Acknowledgements We are grateful to the undergraduate students who helped to collect the data. LH

and DC were supported by the Australian Research Council (ARC) (150104172). Nasal

sprays were purchased with funding from Swinburne University of Technology and the

Australian Catholic University.

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Chapter 7: Acute Intranasal Oxytocin does not Influence the Non-

Linear Temporal Structure of Cortical Visual Evoked Potentials

7.1 Chapter guide Hugrass, L., & Crewther, D. (In Submission). Part 2: Acute intranasal oxytocin does

not influence the non-linear temporal structure of cortical visual evoked

potentials.

Chapter 7 presents a re-formatted version of the original article cited above,

which has been submitted to Hormones and Behaviour as the second part of a two-part

original research article. The first part is presented in Chapter 6. David Crewther

presented the preliminary analyses at the 2017 Australasian Cognitive Neurosciences

Society meeting. Taken together, Chapters 6 and 7 contribute to the understanding of

the neural mechanisms by which oxytocin influences affective processing.

In the previous chapter, I use conventional VEP to investigate the effects of

OXT on the timing of affective face processing. In the current chapter, I investigate the

effects of OXT on M and P signatures of non-linear VEP waveforms. Data for these two

experiments were collected in the same study, for the same sample of healthy male

adults.

7.1.1 Highlights

• Non-linear visual evoked potentials (VEPs) were influenced by luminance contrast

• Social anxiety also influenced non-linear VEP amplitudes

• This effect was limited to the magnocellular (M) driven VEP waveforms

• Nasal oxytocin did not influence M or parvocellular VEP waveforms

• Nasal oxytocin did not modulate the effect of social anxiety on VEPs

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7.2 Abstract Oxytocin is a neuropeptide that plays an important role in a range of complex

social behaviours. Some evidence suggests that oxytocin enhances social information

processing by increasing the salience of socially relevant cues, even at the earliest

stages of visual processing. We used non-linear visual evoked potentials (VEPs) to

investigate whether oxytocin facilitates or impedes the temporal efficiency of visual

processing in the magnocellular and parvocellular afferent streams, and whether trait

level anxiety and autism modulate the effects of oxytocin on early visual processing. In

a randomized, double blind, crossover design, 27 healthy male participants self-

administered a nasal spray of either oxytocin (24 IU) or placebo prior to the recording

of non-linear VEPs. There were no effects of oxytocin on VEP peak amplitudes. We

observed some effects of anxiety on VEP amplitudes, with high levels of social anxiety

associated with increased M-driven non-linear VEP amplitudes. This suggests that trait

anxiety slows recovery from prior stimulation in the M pathway. Our results suggest

that OXT does not play a role in gating M and P afferent responses to non-social

stimuli. However, this does not rule out the possibility that it alters early visual

processing under conditions when socially relevant information is available.

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7.3 Introduction The neuropeptide oxytocin (OXT) modulates a range of complex social

behaviours, including social bonding, social anxiety and maternal behaviour (Bartz &

Hollander, 2008; Meyer-Lindenberg, 2008) via mechanisms that are not well

understood (Bartz, Zaki, Bolger, & Ochsner, 2011). One proposal is that OXT enhances

social processing by reducing anxiety (McCarthy, McDonald, Brooks, & Goldman,

1996). This theory is supported by evidence that OXT reduces the effects of social

stress on salivary cortisol levels (Heinrichs, Baumgartner, Kirschbaum, & Ehlert, 2003).

A reduction in anxiety may also explain the effects of OXT administration on amygdala

and prefrontal responses in groups with generalised social anxiety disorder or autism

spectrum disorders (Domes et al., 2013; Labuschagne et al., 2010, 2012).

Other theories suggest that OXT enhances processing of social information by

increasing affiliative motivation (Bartz & Hollander, 2008; Heinrichs, von Dawans, &

Domes, 2009), which could in turn bias goal directed attentional resources towards

socially relevant cues (Bartz et al., 2011). A third possibility is that OXT enhances the

perceptual salience of social cues (Ross & Young, 2009; Schulze et al., 2011). This

proposed mechanism is supported by evidence that OXT increases gaze to facial eye

regions (Ebitz, Watson, & Platt, 2013), functional coupling between the amygdala and

superior colliculi (Gamer, Zurowski, & Büchel, 2010) and resting-state functional

coupling between the amygdala and frontal cortex (Sripada et al., 2012). This suggests

that OXT may have profound effects on the earliest stages of social information

processing (Ebitz et al., 2013).

In Chapter 6, we presented evidence that OXT administration influences very

early (40-60ms) stages of affective face processing (Hugrass, Labuschagne, Price, &

Crewther, Submitted for Publication). At these early stages, social processing involves

the projection of coarse, but rapid visual input from the magnocellular (M) pathway to

the superior colliculus, LGN, amygdala and frontal cortices (Bar et al., 2006; Kveraga,

Boshyan, & Bar, 2007; Pessoa & Adolphs, 2010). Due to fast conduction speeds, visual

input into the M pathway enables rapid detection of potentially threatening stimuli, in

time to gate the processing of slower and more detailed visual input from the

parvocellular (P) pathway (Bar et al., 2006; Bullier, 2001; Hupé et al., 1998; Kveraga et

al., 2007; Vlamings, Goffaux, & Kemner, 2009). Differences relating to M pathway

functions may explain some of the facial emotion processing abnormalities in people

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with high levels of autistic personality traits (Burt, Hugrass, Frith-Belvedere, &

Crewther, 2017). Furthermore, fMRI and behavioural evidence suggests that trait

anxiety facilitates processing of ambiguous threat cues in the M pathway, while

impairing processing of clear threat cues in the P pathway (Im et al., 2017). However, to

our knowledge, no studies have explicitly assessed the effects of OXT administration on

magnocellular visual processing.

Temporal processing in the M and P pathways can be inferred through Wiener

kernel decomposition of non-linear visual evoked potentials (VEPs) (Klistorner,

Crewther, & Crewther, 1997). In multifocal VEP (mfVEP) experiments, diffuse patches

of light are flashed in de-correlated pseudorandom binary sequence of luminance levels

(Sutter, 1992). In a linear system, the first order kernel (K1) measures the impulse

response function generated by brief stimuli (Benardete & Victor, 1994). However, the

visual system cannot process extremely high temporal frequencies and hence we

observe higher-order VEP kernel responses. The K2.1 and K2.2 kernels are measures of

temporal non-linearity over one and two video frames respectively (see Figures 7.1b

and c). Analyses of contrast response functions indicate that K2.1 waveforms, and early

components of K2.2 waveforms originate from the M pathway, whereas the later

component of the K2.2 originates from afferents of the P pathway (Jackson et al., 2013;

Klistorner et al., 1997). Previous non-linear VEP studies have shown that high levels of

autistic personality traits are associated with higher amplitude K2.1 and early K2.2

responses (Burt et al., 2017; Jackson et al., 2013), which is indicative of temporal

inefficiency in the projection of information in the M pathway to the visual cortex

(Sutherland & Crewther, 2010).

If OXT facilitates M contributions to visual processing, one would expect it to

decrease the amplitude of M-driven nonlinear VEP amplitudes (i.e., K2.1 and early

K2.2 amplitudes). On the other hand, if OXT increases the temporal efficiency of P

processing, one would expect P-driven nonlinear VEP amplitudes (i.e., later K2.2

amplitudes) to decrease after OXT administration. Previous studies have shown that

autistic and anxious personality traits contribute to individual differences in the effects

of OXT on brain activation and behaviour (Alvares, Chen, Balleine, Hickie, &

Guastella, 2012; Scheele et al., 2014). Therefore, we investigated whether the effects of

OXT on VEP amplitudes would tend to be greater in participants with higher trait levels

of autism and social anxiety.

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7.4 Method

7.4.1 Participants

A power analysis, calculated using G*Power (Faul, Erdfelder, Lang, & Buchner,

2007), indicated that a sample of 27 participants is adequate to detect a mean difference

ERP responses between the OXT and placebo sessions, with α = 0.05, β = 0.80, and

moderate effect size (Cohen’s d = 0.5). The same sample of 27 healthy males aged 18 –

40 (M = 25.22 years, SD = 4.72 years), with normal or corrected to normal vision,

participated in this experiment, and the experiment presented in Chapter 6. One

additional participant completed the placebo session, but did not return for the oxytocin

session and hence any results were excluded from analysis. The participants gave

written informed consent for the experiment, which was conducted with the approval of

the Australian Catholic University Human Research Ethics Committee and in

accordance with the code of ethics of the Declaration of Helsinki.

7.4.2 Procedure

We utilised a randomized, double blind, placebo-controlled, crossover design.

Prior to attending the lab session, participants completed online demographic questions

and psychometric scales to measure their levels of autistic and anxious personality

traits. Participants were instructed not to drink alcohol on the night before their session.

They were also required to refrain from drinking caffeine on the day of their session,

and to refrain from eating or drinking (except for water) within an hour of their session.

The OXT and placebo (PBO) sessions were separated by a washout period of at least

one week. For both sessions, state anxiety was measured prior to treatment

administration (STAIpre). After treatment administration, VEPs were recorded during

presentation of the low and high contrast multifocal stimuli, and then a second state

anxiety (STAIpost) measurement was made.

Treatment conditions were randomized and counterbalanced, such that the

participants and experimenters were unaware of which spray bottle contained the

oxytocin. Subjects self-administered an intranasal spray of either OXT (24 IU) or

placebo (PBO, containing all ingredients except for the peptide) in three puffs of 4 IU

per nostril. Participants were given instructions for how to self-administer the nasal

spray, consistent with recommended guidelines (Guastella et al., 2013). Participants

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primed the bottle by puffing three sprays into the air. They inhaled a full spray in one

nostril and then waited 45s between applications until they had completed three sprays

per nostril, in alternating order. Before completing the tasks that are presented in this

chapter, participants completed the experiments presented in Chapter 6, at

approximately 45 minutes after the last spray. The first experimental block of low

contrast mfVEP started at approximately 60 minutes after the last spray application

(OXT: M= 57.70, SD= 4.20, PBO: M = 58.96, SD= 4.64). The second experimental

block of high contrast mfVEP started at approximately 65 minutes after the last spray

application (OXT: M= 62.93, SD= 5.03, PBO: M = 64.33, SD= 4.49). STAIpost was

recorded approximately 75 minutes after the last spray application (OXT: M= 76.22,

SD= 5.62, PBO: M = 77.22, SD= 6.06).

7.4.3 Questionnaires

Prior to attending the session, participants completed online demographic and

personality scales. Autistic personality traits were measured with an online version of

the Autism Spectrum Quotient (AQ) (Baron-Cohen, Wheelwright, Skinner, Martin, &

Clubley, 2001), with higher scores (range, 0 – 50) indicating higher levels of trait

autism. Social anxiety was measured using the Social Interaction Anxiety Scale (SIAS)

(Mattick & Clarke, 1998), with higher scores (range, 0 – 80) indicating a greater degree

of social anxiety. State anxiety was measured prior to OXT and PBO nasal spray

administration (STAIpre), and at the end of the testing sessions (STAIpost) using the

State-Trait Anxiety Inventory (Form Y1, range: 20 - 80, Spielberger, 2010).

7.4.4 mfVEP Stimuli

The stimuli were presented on a 60Hz LCD monitor (ViewSonic, resolution

1024 x 768) with linearized colour output (measured with a ColorCal II). The 9-patch

dartboard stimuli (Figure 7.1a) were created using VPixx software (version 3.20,

http://www.VPixx.com), with a central patch (5.4° diameter) and two outer rings of four

patches (21.2° and 48° diameter). The luminance for each patch was modulated at the

video frame rate (60Hz, mean luminance = 42 cd/m 2, CIEx = 0.32, CIEy = 0.33), in

pseudorandom binary m-sequences (m = 14), at either low (10% Michelson) or high

(70% Michelson) temporal contrast. The m-sequences for each patch were maximally

offset, so we could record independent responses across the visual field. For the purpose

of this experiment, we only analyzed responses to the central patch.

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Figure 7.1 Illustration of the central two rings of the multifocal stimulus. a) The patches alternate between light and dark grey in pseudorandom binary sequences, that are updated every video frame (60Hz). (b) The first-order kernel (K1) is the difference in response when the central patch was light or dark, K1=0.5*(SL-SD). (c) The first slice of the second order kernel (K2.1) takes the previous frame into consideration, comparing responses when a transition did or did not occur: K2.1=0.25*(SLL + SDD - SLD - SDL). The second slice of the second order kernel (K2.2) is similar to K2.1, but there is another intervening frame of either polarity: K2.2=0.25*(SL_L + SD_D - SL_D - SD_L).

7.4.5 EEG recording and pre-processing

EEG was recorded using a 64-channel cap (Neuroscan, Compumedics). The data

were sampled at 1KHz and band-pass filtered from 0.1-200Hz. Electrode site AFz

served as ground and linked mastoid electrodes were used as a reference. EOG was

monitored using electrodes attached above and below one eye. Data were processed

using Brainstorm (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011), a MatLab script

that is documented and freely available for download online under the GNU general

public license (http://neuroimage.usc.edu/brainstorm).

EEG data were band-pass filtered (1- 40Hz) and signal space projection was

applied to remove eye-blink artefact. Epochs of data were extracted from -100 to 500ms

around the onset of each video frame (n = 16384). Each epoch was baseline corrected

by subtracting the mean baseline amplitude (-100ms to -1ms). Custom

Matlab/Brainstorm scripts were written for the mfVEP analyses in order to extract the

K1, K2.1 and K2.2 kernel responses for the central patch. K1 is the difference between

responses to the light and dark patches (Figure 7.1b). K2.1 measures neural recovery

over one frame (16.67ms) by comparing responses when a transition did or did not

occur (Figure 7.1c). K2.2 measures neural recovery over two frames (33ms), it is

similar in form to K2.1, but includes an interleaving frame of either polarity (Figure

7.1d). Analyses of the contrast-response functions indicate that both the M and P

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pathways contribute to K1 amplitudes, K2.1 and early K2.2 responses originate from

the M pathway, and that the main K2.2 response originates from the P pathway

(Jackson et al., 2013; Klistorner et al., 1997).

Labview (National Instruments) scripts were written to identify peak amplitudes

and latencies in the VEP responses. Trough-to-peak K1N65P105, K2.1N70P105, K2.2N70P90

and K2.2 N130P160 amplitudes were exported for statistical comparisons. If a waveform

could not be detected above the noise level, it was marked as a missing value (five out

of 108 K2.1N70P10 responses were removed from the analysis).

7.4.6 Statistical Analyses

The data were analysed using the linear mixed modelling (LMM) procedure in

SPSS, because of its advantage in dealing with missing values, and its ability to handle

correlated data and unequal variance. This enabled us to investigate whether we would

need to take into account the effects of nuisance variables (e.g., order of treatment, time

of treatment and task latency), and whether scores on the questionnaires were important

covariates of STAIchange and VEP amplitudes. To account for individual differences in

VEP amplitudes, random intercepts were modelled for each subject. In repeated-

measures designs it is likely that the error terms are correlated within subjects. Hence,

we used a first-order, autoregressive (AR1) covariance matrix for our models.

In order to investigate the change in state anxiety over the testing session

(STAIchange), a preliminary mixed-effects analysis was conducted to investigate which

variables should be included as predictors in the main model. Treatment was entered as

the repeated-measures fixed effect and Subject ID was entered as the random intercept.

AQ, SIAS and STAIpre, order of treatment, time of treatment and latency of STAIpost

were entered as covariates. Only STAIpre contributed significantly to the preliminary

model (see results section), so it was included as a covariate in the main LMM of

STAIchange.

Separate preliminary mixed-effects analyses were conducted to investigate

important predictors of K1N65P105, K2.1N70P105, K2.2N70P90 and K2.2N130P160 amplitudes.

The distributions of the K1N65P105, K2.1N70P105, K2.2N70P90 and K2.2 N130P160 amplitudes

were all positively skewed. However, LMM does not require the DV to be normally

distributed, and the model fits were similar with and without log10 transforms. Hence, in

order to simplify interpretation of the estimated marginal means, analyses were

preformed on the untransformed amplitudes. For the preliminary analyses, treatment

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and contrast were entered as the repeated-measures fixed effects, AQ, SIAS, STAIpre ,

time of day, order of treatment and VEP latency were entered as covariates, and subject

ID was entered as the random intercept. If any variables contributed significantly to the

model for a given VEP waveform they were included in the main mixed-effects model

for that waveform (see results section).

7.5 Results

7.5.1 State anxiety preliminary LMM

The descriptive statistics (Table 7.1) indicate there was a good spread of scores

on the AQ, SIAS and state anxiety scales for the current sample. The preliminary LMM

revealed a significant effect of STAIpre on the degree of STAIchange (F(1, 39.4) = 57.02, p

< 0.001), so it was included as a covariate in the main LMM. AQ and SIAS did not

contribute significantly to the LMM, nor did order of treatment, time of day or task

latency (Fs ≤ 2.72, ps ≥ .12). Hence, these variables were not included as covariates in

the main analysis.

Table 7.1 Descriptive statistics for AQ, SIAS and state anxiety

Min Max M SD

AQ 1 38 17.48 7.71

SIAS 11 50 28.44 10.69

OXT STAIpre 20 58 33.00 8.66

PBO STAIpre 20 59 32.22 9.60

OXT STAIpost 20 48 31.19 7.58

PBO STAIpost 20 51 32.15 8.61

N = 27

7.5.2 State anxiety LMM

Mean state anxiety scores before and after the OXT and PBO sessions are

presented in Figure 7.2a For the main mixed-effects analysis, there was a significant

effect of STAIpre (F(1, 41.3) = 53.85, p < 0.001), with higher pre-treatment anxiety

levels associated with greater changes in anxiety by the end of the session (Figure 7.2b).

There was no significant effect of treatment, and there was no treatment by STAIpre

interaction (Fs ≤ .07, ps ≥ .80). These results indicate that when participants were very

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anxious at the start of the session, they tended to be less anxious by the end of the

session, regardless of whether they had received the OXT or PBO spray.

Figure 7.2 STAI results (a) Estimated marginal means for the change in state anxiety (STAIchange) after nasal spray administration, corrected for individual differences in baseline state anxiety (STAIpre). Results for the OXT and PBO sessions are presented in the red and blue bars respectively. The error bars represent ±1SE. (b) Scatter plots and linear fits of the relationships between STAIchange and STAIpre for the OXT (red markers and fit line) and PBO sessions (blue markers and fit line).

7.5.3 mfVEP preliminary LMMs

The preliminary LMMs for K1N65P105, K2.1N70P105, K2.2N70P90 and K2.2N130P160

amplitudes revealed significant effects of SIAS on the K1N65P105 (F(1, 21.3) = 10.18, p =

0.004) and K21N65P105 F(1, 18.9) = 10.42, p = 0.004) amplitudes, so it was included as a

covariate in the main LMMs for these waveforms. AQ, STAIpre, order of treatment, time

of day and task latency did not contribute significantly to any of the preliminary mfVEP

LMMs (Fs ≤ 2.66, ps ≥ .12), so they were not included as covariates in the main

analyses.

7.5.4 K1N65P105 LMM

The K1 response (i.e.; the difference in response when the patch was light or

dark) reflects the sum of inputs from the M and P afferent pathways (Klistorner et al.,

1997). K1 waveforms for the low and high temporal contrast stimuli are presented in

Figures 7.3a and 7.3b, and estimated marginal means (EMMs) of K1N65P105 amplitudes

(corrected for individual differences in SIAS) are presented in Figure 7.3c. The grand

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mean average waveforms show the expected increase in amplitude with contrast of the

triphasic disturbance.

Figure 7.3 The effects of treatment, contrast and SIAS on K1 responses. Mean waveforms for the low (a) and high (b) contrast stimuli are presented for OXT (red traces) and PBO (blue traces). The shading denotes ±1SE. Estimated marginal means of K1N65P105 amplitudes (c) (corrected for individual differences in SIAS) are presented for OXT (red bars) and PBO (blue bars) at low and high contrast. Linear fits of the correlations between K1 amplitudes and social anxiety scores are presented for the low (d) and high (e) contrast stimuli, under the OXT (red fit lines and markers) and PBO (blue fit lines and markers) treatment conditions.

Although there appears to be a small effect of OXT administration on K1

latencies (Figures 7.3a and 7.3b), an LMM on K1N65 latency (as measured at the peak

negativity) showed an effect of contrast (F(1, 60.0)= 7.24, p= 0.010), but there were no

treatment or treatment by contrast effects (Fs ≤ 2.48, ps ≥ .12). There were no effects of

contrast or treatment on K1P105 latency (as measured at the peak positivity), nor was

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there a contrast by treatment interaction (Fs ≤ 2.45, ps ≥ .12). This suggests that if OXT

administration influences K1 latencies, it does not do so at the major peaks.

The main mixed-effects analysis revealed there were significant effects of

contrast (F(1, 48.7)= 41.50, p< 0.001) and SIAS (F(1, 24.9)= 7.43, p= 0.012) on

K1N65P105 amplitudes. However, there was no significant effect of treatment, no

treatment by contrast interaction and no SIAS by treatment interaction (Fs ≤ .98, ps ≥

.33). Bivariate correlations (Figure 7.3d and 7.3e) showed that K1N65P105 amplitudes

tended to be greater for people with higher SIAS scores, both for the low (OXT r = .39,

p = .045; PBO r = .45, p = .017) and high contrast stimuli (OXT r = .39, p = .046; PBO r

= .44, p = .022). In summary, our results suggest that K1N65P105 amplitudes tend to

increase with social anxiety and stimulus contrast, but they are not affected by OXT vs.

PBO.

7.5.5 K2.1N70P105

The K2.1 response (i.e.; recovery of flash VEP over one video frame) is likely to

originate from M inputs (Klistorner et al., 1997). K2.1 waveforms for the low and high

temporal contrast stimuli are presented in Figures 7.4a and 7.4b, and EMMs of

K2.1N70P105 amplitudes (corrected for individual differences in SIAS) are presented in

Figure 7.4c. The grand mean waveforms show the expected increase in amplitude with

contrast.

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Figure 7.4 The effects of treatment, contrast and SIAS on K2.1 responses. Mean waveforms for the low (a) and high (b) contrast stimuli are presented for OXT (red traces) and PBO (blue traces). The shading denotes ±1SE. Estimated marginal means of K2.1N70P105 amplitudes (c) (corrected for individual differences in SIAS) are presented for OXT (red bars) and PBO (blue bars) at low and high contrast. Linear fits of the correlations between K1 amplitudes and social anxiety scores are presented for the low (d) and high (e) contrast stimuli, under the OXT (red fit lines and markers) and PBO (blue fit lines and markers) treatment conditions.

The LMM of K2.1 N70P105 amplitudes revealed significant effects of contrast

(F(1, 29.5) =32.89, p<0.001) and SIAS (F(1, 24.5) =7.16, p= 0.013) on K2.1N70P105

amplitudes. However, there was no significant effect of treatment and there was no

treatment by contrast interaction (Fs ≤ 1.23, ps ≥ .27). Bivariate correlations (Figure

7.4d and 7.4e) showed that K2.1N70P105 amplitudes tended to be greater for people with

higher SIAS scores for the PBO session (10% contrast r = .54, p = .005; 70% contrast r

= .41, p = .043), but these correlations were not statistically significant for the OXT

session (10% contrast r = .36, p = .060; 70% contrast r = .26, p = .196). However, the

LMM showed that the SIAS by treatment interaction was not statistically significant (F

=2.07, p = .16). Consistent with the analysis of K1 amplitudes, our results suggest that

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K2.1 amplitudes increase with stimulus contrast and social anxiety, but they are not

substantially affected by OXT vs. PBO administration.

7.5.6 Early and late K2.2 waveforms

The early and late K2.2 responses (i.e.; recovery of flash VEP over two video

frames) are likely to originate from M and P inputs respectively (Jackson et al., 2013;

Klistorner et al., 1997). The averaged K2.2 waveforms for the low and high contrast

conditions are presented in Figures 7.5a and 7.5b respectively and EMMs of the early

and late K2.2 amplitudes are presented in Figures 7.5c and 7.5d. The grand mean

waveforms show the expected increase in amplitude with contrast for the late K2.2

waveform. SIAS did not contribute significantly to the early or late K2.2 LMMs, so it

was not included as a covariate for these analyses.

Figure 7.5 The effects of treatment and contrast on K2.2 responses. Mean waveforms for the low (a) and high (b) contrast stimuli are presented for OXT (red traces) and PBO (blue traces). The shading denotes ±1SE. The estimated marginal mean amplitudes for the K2.2N70P90 (c) and K2.2N130P160 (d) waveforms are presented for low and high contrast stimuli (OXT: red bars, PBO: blue bars).

The LMM of early, K2.2 N70P90 amplitudes did not reveal any significant effects

of contrast or treatment, and there was no contrast by treatment interaction (Fs ≤ 1.24,

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ps ≥ .27). The LMM of K2.2 N130P160 amplitudes revealed a significant effect of stimulus

contrast (F(1, 24.5)=31.19, p<0.001). There was no effect of treatment, nor was there a

treatment by contrast interaction (Fs ≤ .41, ps ≥ .53) Our results suggest that late, but

not early, K2.2 amplitudes are influenced by contrast, and that neither early nor late

K2.2 waveforms are affected by OXT vs. PBO administration.

7.6 Discussion In summary, OXT had no effect on state anxiety, or on any of the nonlinear VEP

kernel amplitudes. Furthermore, there were no OXT by stimulus contrast interactions.

Based on these results, there is no evidence that OXT modulates activation of the

primary visual cortex via either the M or P afferent pathways (when using simple flash

stimuli). As expected, based on the existing literature, kernel responses were greater in

amplitude for the high contrast stimulus, and the effects of contrast on amplitude were

stronger for the P-driven (late K2.2) responses than for the M-driven (K2.1 and early

K2.2) responses (Jackson et al., 2013; Klistorner et al., 1997). Interestingly, some of

the individual variation in VEP amplitudes could be attributed to the participants’ scores

on a self-reported social anxiety measure. Specifically, M-driven (K2.1) amplitudes

increased with social anxiety, whereas P-driven (late K2.2) amplitudes were not

significantly associated with social anxiety. By contrast, autistic tendency did not

predict non-linear VEP amplitudes. We provide more detailed interpretations of the

effects of OXT, anxiety and autistic tendency on visual processing below.

There are several possible explanations as to why OXT did not affect VEP

amplitudes. Differences in OXT spray administration methods can influence its dosage

and bioavailability (Bradfield, 1965). Although we did not measure blood or salivary

levels of OXT, we followed procedures to maximise the likelihood of the drug being

absorbed efficiently at the mucosal surface (Guastella et al., 2013). The latencies from

nasal spray delivery to the VEP and STAI measurements were well within the periods

for which salivary OXT remains elevated (Van IJzendoorn, Bhandari, Van der Veen,

Grewen, & Bakermans-Kranenburg, 2012), and peripherally measured OXT

concentrations tend to be reliable indicators of central OXT concentrations after nasal

spray administration (Valstad et al., 2017). Although the sample size was relatively

small, the study was adequately powered to detect moderate to large effects of treatment

condition. Furthermore, many studies have reported effects of OXT on brain activation

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for crossover designs with similar or smaller sample sizes (Domes, Heinrichs, Michel,

Berger, & Herpertz, 2007; Kirsch et al., 2005; Labuschagne et al., 2010; Perry et al.,

2010; Sripada et al., 2012). The results presented in Chapter 6 (Hugrass et al.,

Submitted for Publication) are consistent with the interpretation that a

biologically relevant dose of OXT was administered to the brain. Therefore, perhaps the

most plausible explanation as to why OXT did not influence VEP amplitudes is that our

testing paradigm did not involve any social engagement.

The effects of OXT can vary depending on the context in which it is

administered (Bartz et al., 2011). According to the social salience hypothesis, OXT

enhances the salience of social cues (Ross & Young, 2009; Schulze et al., 2011). There

is evidence that the oxytocin receptor (OXT-R) gene impacts functional activation in

the visual cortex (O’Connell et al., 2012). Furthermore, OXT enhances functional

coupling between the amygdala and the superior colliculus (Gamer et al., 2010), a

subcortical region involved in gaze direction, which receives predominantly M input

(Wurtz & Albano, 1980). While it is plausible that OXT could modulate processing in

the afferent pathways, the results presented here and in Chapter 6 (Hugrass et al.,

Submitted for Publication) indicate that it only modulates early visual processing when

there is socially salient input.

In addition to contextual effects, stable individual differences, such as

endogenous plasma OXT levels and personality variables can also influence the effects

of OXT administration on brain and behavioural measures (Bartz et al., 2011). Previous

studies have shown that OXT has greater effects on amygdala activation in generalised

social anxiety disorder and in autism than in control groups (Domes et al., 2013;

Labuschagne et al., 2010). Trait anxiety can also moderate the effects of OXT on brain

and behavioural measures in non-clinical samples (Alvares et al., 2012). Therefore, we

were interested in whether social anxiety levels in a neurotypical sample contribute to

individual variation in non-linear VEP amplitudes. High social anxiety was associated

with higher amplitudes for the M-driven (K2.1) nonlinear VEP amplitudes. This is

consistent with evidence that trait anxiety improves M processing of ambiguous threat

cues (Im et al., 2017). Correlations between K2.1 amplitudes and social anxiety were

weaker after OXT administration; however our study was not sufficiently powerful to

detect whether OXT blunts the effect of SIAS on M-driven VEP amplitudes at the

population level.

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We did not observe any effects of autistic tendency on M or P driven VEP

amplitudes. By contrast, previous studies have shown that M-driven K2.1 amplitudes

tend to be higher in neurotypical groups with high, compared with low, autistic

tendency (Burt et al., 2017; Jackson et al., 2013). It is important to note that these

studies were specifically designed to compare groups with low and high AQ scores,

whereas we simply recorded AQ in order to control for any potential interactions

between the effects of AQ and OXT administration on VEP amplitudes. Although our

sample included some participants with very low and high AQ scores, the majority

scored in the mid range. Previous studies have shown that OXT administration can

improve face processing in groups with ASD (Domes, Heinrichs, Gläscher, et al.,

2007), and furthermore that M abnormalities may contribute to differences in affective

face processing in groups with low and high AQ scores (Burt et al., 2017). Therefore, it

is reasonable to ask whether OXT administration can reduce the effects of autistic

tendency on M-driven VEP amplitudes. In order to answer this question, future studies

will require larger numbers of participants at the low and high ends of the autistic

personality spectrum.

In summary, we investigated differences in the acute effects of nasal OXT

administration on the non-linear temporal structure of visual evoked potentials. We

were specifically interested in whether OXT would increase temporal efficiency in the

M pathway; however the absence of any treatment or treatment by temporal contrast

effects on non-linear VEP amplitudes suggest that OXT does not influence processing

in the main afferent streams to the primary visual cortex. High levels of social anxiety

were associated with increased M-driven non-linear VEP amplitudes; this suggests that

anxiety reduces temporal efficiency in the M pathway. However, this effect was not

significantly different for the sessions when participants received OXT or PBO.

Therefore although nasal OXT has been shown to bias top-down attentional resources

towards socially relevant visual stimuli (Bartz et al., 2011), our results indicate that it

does not play a role in gating non-social M and P input to the primary visual cortex.

7.7 Acknowledgements We are grateful to Izelle Labuschagne for her input and advice in designing the

study and preparing the manuscript. We would like to make a special thanks to the

undergraduate students who helped to collect the data. This project was funded by the

Australian Research Council (ARC) (150104172). Nasal sprays were purchased with

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funding from Swinburne University of Technology and the Australian Catholic

University.

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Chapter 8: General Discussion The overall aim of this body of work was to use non-invasive techniques to infer

cortical processing of visual input from the human M and P pathways. The two reviews

and four experimental chapters addressed different aspects of this aim.

Chapters 2 and 3 contributed to knowledge in this area by reviewing techniques

for identifying M and P dominated signals in scalp-recorded VEPs. There were two

main conclusions drawn from the review presented in Chapter 2. Firstly, in comparison

to currently available VEP techniques, non-linear VEP provides the cleanest separation

of putative M and P signals (Jackson et al., 2013; Klistorner, Crewther, & Crewther,

1997; Momose, 2010). Secondly, the major advantage of non-linear VEP is that it

allows for simultaneous comparison of M and P contributions to the same visual

stimuli, unlike other techniques that rely on different stimulus characteristics, in

separate recordings, to target the M and P pathways (e.g.; Ellemberg, Hammarrenger,

Lepore, Roy, & Guillemot, 2001; Foxe et al., 2008; Lalor & Foxe, 2009; Souza et al.,

2008). As reviewed in Chapter 3, the non-linear VEP technique has been applied to

address a broad range of questions regarding input from the human M and P pathways

to cortical visual processing mechanisms.

The experiments presented within this thesis have made several contributions to

knowledge in the area, which are discussed in more detail below. The work presented in

Chapter 4 improved the understanding of how red backgrounds affect visual processing

(Section 8.1). The work presented in Chapter 5 furthered current knowledge regarding

the effects of diffuse chromatic saturation on temporally non-linear responses arising

from the primary visual cortex (Section 8.2). Chapters 6 and 7 made substantial

contributions to knowledge regarding the effects of oxytocin on visual processing

(Section 8.3). The final sections of this chapter link the key findings together and

outline the general conclusions and implications of this thesis as a whole. Section 8.4

addresses limitations in inferring processing in the subcortical visual pathways based on

scalp-recorded VEPs, and suggests some directions for future research. Finally, the

main conclusions of this thesis are laid out in Section 8.5.

8.1 Understanding the effects of red surrounds on visual processing The results presented in Chapter 4 challenge the assumption that red

backgrounds selectively suppress M contributions to visual processing at the cortical

level. A large number of studies have interpreted the effects of red backgrounds on

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human behavioural and neuroimaging signals in terms of M specific suppression (e.g.;

Awasthi, Williams, & Friedman, 2016; Bedwell, Brown, & Orem, 2008; Bedwell et al.,

2013; West, Anderson, Bedwell, & Pratt, 2010). Therefore, this thesis has important

implications for this area of research.

8.1.1 Overview of original contributions

The experiments presented in Chapter 4 used non-linear VEP and psychophysical

techniques to compare widely accepted signatures of M and P responses to stimuli that

were presented on red and green backgrounds. Primate physiological studies identified a

subpopulation of M cells (i.e.; Type IV M cells), for which firing responses are

tonically suppressed when diffuse red light is presented in the receptive field surround

(de Monasterio, 1978; Derrington, Krauskopf, & Lennie, 1984; Hubel & Livingstone,

1990; Livingstone & Hubel, 1982). Based on this evidence, a large number of human

studies were conducted under the assumption that red backgrounds can be used as a

non-invasive tool for selectively suppressing M contributions to visual processing (e.g.;

Awasthi et al., 2016; Bedwell et al., 2008; Bedwell et al., 2013; West et al., 2010).

There are several reasons for questioning the validity of this assumption. Firstly,

there is no reason to believe a red surround would completely suppress M contributions

to visual processing. The majority of M cells (i.e.; Type III M cells) have receptive

fields that are spatially, but not chromatically opponent (Hubel & Livingstone, 1990).

Secondly, it is possible that a red background would also influence responses of the P

and K cells that transmit chromatic input to the cortex (Skottun, 2004) . Thirdly, in the

supragranular layers of macaque V1, transmembrane current flow is stronger in

response to red light than white or green light (Givre, Arezzo, & Schroeder, 1995). This

effect occurs to a lesser extent in layer 4Cβ, it does not reach statistical significance in

input layer 4Cα, and it is not observed in the M and P layers of the LGN. This suggests

that mechanisms within V1 may contribute to the effects of red backgrounds on visual

perception.

Based on these considerations, I designed non-linear VEP and steady and pulsed

psychophysics experiments to compare signatures of M and P responses to stimuli that

were presented on red and green backgrounds. The non-linear VEP experiment showed

that K2.1 responses were unaffected by background colour, yet the main K2.2 peak was

lower in amplitude with the red background than with the green background. These

results did not provide any evidence that a red background suppresses M contributions

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to scalp-recorded VEPs. On the contrary, they imply that a red background may

enhance P recovery from stimulation.

The results of the steady and pulsed psychophysics experiment were somewhat

more complicated. For the initial version of the psychophysics task, I had designed

stimuli with grey pedestals on red and green backgrounds. Under these conditions,

steady pedestal thresholds were unaffected by background colour, and pulsed pedestal

thresholds were elevated with the red background. On the surface, these results would

imply that a red background does not affect steady state contrast gain in the M pathway,

but that it suppressed contrast sensitivity of P cells (Pokorny, 2011). Yet, the presence

of chromatic edges between the pedestals and backgrounds, in addition to some

departures from the classic response curves, made it difficult to interpret these findings

in terms of Pokorny and Smith’s (1997) original, achromatic version of the task.

In response to suggestions from an anonymous peer reviewer, I conducted a

follow-up psychophysics experiment, in which the pedestals and targets were the same

colour as the background (Figure 4.3). This eliminated the presence of chromatic edges

between the pedestals and backgrounds. For the pulsed pedestal experiment, there was

no effect of background colour on contrast discrimination thresholds. When the steady

pedestals were dimmer than the red background, thresholds tended to be lower in the

red conditions. When the steady pedestals were brighter than the background, the red

condition was associated with lower thresholds for the central targets and higher

threshold for the peripheral targets. These results suggest that a red background can

have different effects on putative M and P contrast sensitivities, depending on whether

the targets are brighter or dimmer than the surround, and whether they are presented

centrally or peripherally.

8.1.2 Implications

An immediate implication of this work is that it is necessary to re-interpret

existing literature that used red surrounds to infer the effects of M suppression on

human psychophysical and neuroimaging results. These inferences were based on the

assumption that red surrounds specifically suppress M contributions to visual

processing. I have challenged this assumption by demonstrating that the effects of red

backgrounds on vision vary depending on the nature of the stimuli.

For instance, under conditions of fast m-sequence stimulation, there was no

evidence that the red background suppressed the M pathway. Rather, red surrounds

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appeared to decrease non-linearity in the K2.2 responses. This implies that diffuse red

light enables the P system to recover more rapidly from stimulation. For the

psychophysical task, under conditions when grey, pulsed pedestal stimuli were

presented on a coloured background, the current results suggest a red background

decreases the contrast gain of the P system (Pokorny, 2011). Under conditions when

steady pedestals were the same colour as the background stimuli, the results indicate

that a red background can either increase or decrease contrast detection thresholds,

depending on whether the pedestals are brighter or dimmer than the background, and

whether they are presented centrally or peripherally. This suggests that red surrounds

have different effects on the steady state gain of the ON and OFF subdivisions of the M

pathway. These mixed results demonstrate that it would be an oversimplification to

attribute all of the effects of red background on visual processing to selective

suppression of Type IV M cells.

The second key implication is that Type III and Type IV M cells might play

different roles in visual processing. Red surrounds produce profound and tonic

suppression of firing responses in Type IV M cells (de Monasterio, 1978; Hubel &

Livingstone, 1990), yet they do not appear to affect putative M signatures in non-linear

VEPs, and their effects on M contrast discrimination mechanisms range from subtle

facilitation to subtle suppression. This suggests that VEP and psychophysical signatures

of M processing are more sensitive to input from Type III than Type IV M cells, and

that the two M subpopulations might play different roles in visual processing. In support

of this idea, macaque physiological studies showed that Type IV M cells have a more

central retinal distribution than Type III M cells, and that unlike Type III M cells, they

do not project to the superior colliculus (de Monasterio, 1978).

8.2 Understanding the effects of diffuse chromatic saturation The MEG experiment reported in Chapter 5 was conducted to further investigate

EEG evidence that diffuse red or blue chromatic saturation increases K2.1 amplitudes

(Crewther & Crewther, 2010; Klistorner, Crewther, & Crewther, 1998). The novel

findings were an early peak in the K2.1 waveform that increased in amplitude for blue

chromatic saturation, and the localization of this peak to retinotopically mapped regions

of V1. This suggests that early processing of chromatic saturation reflects a neural

mechanism that recovers rapidly from stimulation. Such a mechanism may facilitate

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figure ground segmentation during the first-pass analysis of the visual scene. The

implications of this finding, in relation to the existing literature, are discussed below.

8.2.1 Overview of original contributions

The majority of studies within the chromatic VEP literature presented

isoluminant red/green or blue/yellow gratings, in order to identify VEP signatures of

chromatically opponent processing (Gomes et al., 2006; Rabin, Switkes, Crognale,

Schneck, & Adams, 1994). However, this makes it difficult to separate the relative

influences of particular colours. Klistorner et al. (1998) showed that for diffuse (i.e.

unpatterned) multifocal VEP stimulation; the effects of luminance on responses to

pseudorandom green/grey alternations, are relatively simple, with no evidence of

chromatically sensitive signals over and above responses to achromatic luminance

contrast. On the other hand, for pseudorandom red/grey alternations, the non-linear VEP

waveforms were more complex, indicating a significant contribution of red sensitive

signals to the K2.1 response. In a related study, Crewther and Crewther (2010) found

that the amplitude of the K2.1 response increased steadily with the degree of blue or red

chromatic saturation.

I investigated the effects of blue chromatic saturation on the nonlinear temporal

structure of MEG signals, with the particular aim of identifying the cortical sources of

K1, K2.1 and K2.2 signals, specifically those which vary in amplitude with chromatic

saturation. Consistent with previous studies (Crewther & Crewther, 2010; Klistorner et

al., 1998) the K1, K2.1 and K2.2 peak amplitudes increased with chromatic saturation.

These waveforms appeared to be generated by the same retinotopically mapped cortical

sources in V1. The greatest effects of chromatic saturation were observed in the K2.1

waveform, at approximately 70ms. This is somewhat earlier in latency than the

chromatic effects reported in Crewther and Crewther’s (2010) EEG experiment

(~105ms). The current study might have been more sensitive to the early peak, because

independent responses were obtained from each quadrant of the foveal region. Previous

EEG studies reported responses to a single region centered on the fovea (Crewther &

Crewther, 2010; Klistorner et al., 1998), which may have masked the early effects of

chromatic saturation, due to cancellation of sources with different polarities (Baseler &

Sutter, 1997).

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8.2.2 Implications

Given that the overarching aim of this thesis was to use non-linear VEPs to

investigate M and P contributions to visual processing, it was tempting to attribute the

effects of chromatic saturation on the early K2.1 waveform to signals arising from the

subcortical M pathway. For diffuse achromatic stimulation, converging evidence

suggests that the K2.1 waveform originates from M input to the visual cortex

(Crewther, Brown, & Hugrass, 2016; Jackson et al., 2013; Klistorner et al., 1997;

Momose, 2010). Due to receiving non-opponent input from both long and medium

wavelength cones, the M pathway does not appear to play a role in conscious perception

of hue, and it is commonly described as ‘colour blind’. Therefore, it would be intriguing

if the effects of chromatic saturation on the K2.1 response originated from the

subcortical M pathway; however closer examination of the literature led to a more

sophisticated interpretation.

The results of a physiological study in macaques shed light on the enhancement

of V1 responses to diffuse chromatic stimuli. Givre et al. (1995) used VEPs, current

source density and multi-unit activity to investigate responses to white, red, blue and

green light. Responses were recorded from V1 layers 4Cα, 4Cβ and 2/3, as well as from

the M and P layers of the LGN. Red and blue light tended to evoke higher amplitude V1

responses than green or white light, particularly in layers 2/3 and 4Cβ, and the effect

was always largest for red light. There was no evidence of chromatic enhancement of

responses at the level of the LGN, which suggests these wavelength specific effects

arise at the cortical level.

Crewther and Crewther (2010) found that the effects of diffuse chromatic

stimulation on K2.1 amplitudes are wavelength-dependent, with stronger responses to

blue and red stimulation, than to green and yellow stimulation. For patterned

stimulation, most of the response power is in the first order kernel, and amplitudes were

similar across a full range of hues. A plausible explanation is that the effects of diffuse

colour on VEPs arise from cells within the cytochrome oxidase blobs of V1 layers 2/3,

which respond most strongly to diffuse red and blue inputs (Dow & Vautin, 1987). P

input to layers 2/3 is spread uniformly across blob and inter-blob regions, whereas M

and K input is focused around the blob centres (Edwards, Purpura, & Kaplan, 1995;

Hendry & Reid, 2000). The majority of colour selective cells in V1 layers 2/3 show

strong responses to oriented edges, and relatively weak responses to the interiors of

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objects (Johnson, Hawken, & Shapley, 2008). Within these cells, the poor luminance

contrast sensitivity, and preference for high spatial frequency stimulation are consistent

with input from the P pathway. By contrast, cells near the centres of cytochrome

oxidase blobs tend to have similar properties to cells in the M pathway, in that they

show high luminance contrast sensitivity, high temporal frequency sensitivity and a

preference for diffuse or low spatial frequency stimulation (Edwards et al., 1995;

Shoham, Hübener, Schulze, Grinvald, & Bonhoeffer, 1997).

In summary, the results presented in this thesis contribute to evidence that K2.1

amplitudes increase with chromatic saturation. The key contributions made to this body

of work were the discovery of an early (~70ms) K2.1 waveform that is highly sensitive

to diffuse blue chromatic saturation. The use of MEG enabled cortical source

localisation of the effects of chromatic saturation on non-linear VEP amplitudes in V1.

The fact that most of the power of this blue-saturation dependent signal is in the K2.1

waveform (rather than in the K2.2 waveform) suggests that this signal is generated by

neurons that recover rapidly from stimulation. Current evidence suggests that M (and

possibly K) input to the cytochrome oxidase blobs in V1 may play a role in the rapid

processing of chromatic surfaces, which could facilitate figure ground segmentation

during the first-pass analysis of the visual scene.

8.3 Understanding of the effects of oxytocin on early visual processing The experiments reported in Chapters 6 and 7 were conducted to investigate the

ways in which the neuropeptide, oxytocin (OXT) influences visual responses to

emotional face stimuli, and whether these effects might reflect a general enhancement

of M processing. These experimental chapters present complementary results from the

same sample of healthy male participants. The key discovery presented in Chapter 6

was that OXT modulates very early responses to facial emotion. The key discovery

presented in Chapter 7 is that OXT does not influence M or P responses to non-social

stimuli (diffuse flashes). Taken together, these findings imply that OXT influences early

stages of visual processing, but only in response to socially relevant stimuli.

8.3.1 Overview of original contributions

OXT is well known for its effects on social processing (Bartz & Hollander,

2008). Neuroimaging studies have shown that intranasal OXT administration can

suppress amygdala responses to emotional faces (Domes et al., 2007; Kirsch et al.,

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2005), and influence the functional connectivity of the amygdala and superior colliculus

(Gamer, Zurowski, & Büchel, 2010) and frontal cortex (Sripada et al., 2012). It is also

known that intranasal OXT can improve recognition of facial emotions (Lischke et al.,

2012; Marsh, Henry, Pine, & Blair, 2010; Schulze et al., 2011). To date, very few

studies have used VEPs to investigate the effects of OXT on the timing of affective

processing, and none of these studies have included a condition with neutral facial

expressions (Althaus et al., 2015; Huffmeijer et al., 2013; Peltola, Strathearn, & Puura,

2018).

The experiments presented as a part of this thesis compared the acute effects of

OXT and placebo (PBO) administration on VEPs to fearful, happy and neutral faces

(Chapter 6) and non-linear VEPs to pseudorandom binary luminance modulation

(Chapter 7). To my knowledge, this is the first study to have reported modulation of

early (i.e.; 40- 100ms) VEP responses to facial emotion after OXT administration.

These results suggest that at early stages of visual processing, oxytocin modulates

responses to facial emotions, whereas at later stages of visual processing, it appears to

influence more general face processing mechanisms.

There is a growing body of evidence that the earliest stages of facial emotion

processing rely on the rapid projection of M input to the amygdala and frontal cortices

(Burt, Hugrass, Frith-Belvedere, & Crewther, 2017; Kveraga, Boshyan, & Bar, 2007;

Vlamings, Goffaux, & Kemner, 2009; Vuilleumier, Armony, Driver, & Dolan, 2003).

The experiment presented in Chapter 7 was the first to investigate whether OXT

modulates the flow of afferent input from the M and P streams to the cortex.

Interestingly, across the sample of healthy male participants, the magnitude of the K2.1

waveform was greater in participants with high trait social anxiety. This suggests that

trait anxiety slows recovery from prior stimulation in the M pathway. There were no

significant effects of OXT on the amplitudes or latencies of the K2.1 or K2.2 responses.

The key implication of Chapter 7 is that OXT does not play a role in gating M and P

afferent responses to non-social stimuli.

8.3.2 Implications

The results presented in Chapters 6 and 7 have made contributions to the

understanding of the mechanisms by which OXT modulates social and affective

processing (Bartz & Hollander, 2008). Theories regarding the effects of OXT on social

cognition can be grouped into three related categories, suggesting it enhances social

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processing either by: increasing affiliative motivation, reducing anxiety, or altering the

perceptual salience of social cues (reviewed, Bartz, Zaki, Bolger, & Ochsner, 2011).

Although the studies presented within this thesis did not address the affiliative

motivation theory, implications for the latter two theories are discussed below.

Support for the anxiety reduction theory has come from studies that demonstrated

beneficial effects of OXT in groups of participants with social anxiety disorder or

autism (Bartz & Hollander, 2008; Bartz et al., 2010). Although the current experiments

were performed on a non-clinical sample of healthy males, the results suggest that

social anxiety is associated with less efficient M recovery from rapid stimulation. In a

related study, my colleagues and I showed that for participants with high levels of

autistic personality traits, increased temporal nonlinearity in the M pathway may

underlie the blunted effects of facial emotion on early VEP waveforms (Burt et al.,

2017). However, the results presented in Chapters 6 and 7, did not provide any evidence

that OXT lowered state anxiety, nor that it reduced the effects of social anxiety on M-

driven non-linear VEP responses. Therefore, the results presented here are consistent

with the argument that anxiety reduction might only contribute to the effects of OXT on

social processing in participants with very high levels of trait anxiety, or under

conditions when the experimental task increases state anxiety (Bartz et al., 2011).

Support for the theory that OXT enhances the perceptual salience of social cues

(Ross & Young, 2009; Schulze et al., 2011) has come from evidence that it increases

gaze to facial eye regions (Ebitz, Watson, & Platt, 2013), as well as functional coupling

between the amygdala and superior colliculi (Gamer et al., 2010). The results presented

in Chapter 6 are consistent with the claim that OXT has profound effects on the earliest

stages of social information processing (Ebitz et al., 2013). Whereas the differences in

the results presented in Chapters 6 and 7 highlight the importance of context in

mediating the effects of OXT on brain signals (Bartz et al., 2011). The current results

suggest that OXT moderates the rapid, M-driven volley of affective input to the

amygdala, but that it does not gate M-input to the cortex in the absence of social or

affective visual input.

8.4 Limitations and future directions

As discussed above, this body of work made contributions to knowledge across

a broad range of topics. The experiments presented within this thesis applied non-linear

VEP, in combination with other techniques, to make inferences about M and P

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contributions to visual processing. Specific limitations and future directions were

discussed in each chapter; however there are some issues that apply to this body of work

as a whole. This section outlines limitations that arise when interpreting scalp-recorded

VEPs in terms of contributions from the subcortical afferent pathways, and provides

suggestions for future research in this area.

8.4.1 Relating primate physiology to human behaviour

Much of what we know about the M, P and K pathways is based on physiological

studies in primates, in particular macaques. Therefore, it is worthwhile considering

whether the properties of cells in the afferent pathways are comparable in macaques and

humans. Studies have shown broad similarities between the human and macaque midget

(i.e., P) and parasol (i.e., M) retinal ganglion cells, (Curcio & Allen, 1990; Dacey, 1993;

Dacey & Petersen, 1992; Goodchild, Ghosh, & Martin, 1996). Likewise, anatomical

studies of the human LGN show distinctions between cells in the ventral (M), dorsal (P)

and interlaminar (K) layers (Hickey & Guillery, 1981). However, there are some

relevant interspecies differences. For instance, human parasol retinal ganglion cells tend

to have larger dendritic fields (Dacey & Petersen, 1992). This may explain some

discrepancies in M psychophysics for humans and macaques (Cavonius & Robbins,

1973). Furthermore, without single cell physiological investigations, one cannot say for

certain whether there is a distinction between Type III and Type IV M cells in the

human retina and LGN.

Studies in macaques that have compared VEPs, current source density, and

multiunit activity have been particularly useful in supporting physiological

interpretations of scalp recorded VEPs in humans (Givre et al., 1995; Schroeder, Mehta,

& Givre, 1998; Schroeder, Tenke, Givre, Arezzo, & Vaughan Jr, 1991). Comparisons of

VEPs recorded from V1 layers 2/3, 4Cα and 4Cβ, as well as M and P layers of the

LGN, have been particularly useful in understanding how input from the afferent

pathways, and intracortical mechanisms contribute to the generation of scalp recorded

signals (Givre et al., 1995; Schroeder et al., 1991). Using these recording techniques in

combination with fast m-sequence/multifocal stimulation would help to clarify

contributions from the M, P and K pathways to the K1, K2.1 and K2.2 VEP waveforms.

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8.4.2 Separation of K signals from M and P signals

It is also important to acknowledge the potential for overlapping effects from the

different afferent pathways. The putative M and P signals that we measured with non-

linear VEP analyses and paired and pulsed psychophysics are broadly consistent with

the properties of the primate M and P systems (Klistorner et al., 1997; Pokorny &

Smith, 1997). Yet, such distinctions are complicated by subpopulations of K cells with

similar contrast response functions to those of the M and P pathways (Hendry & Reid,

2000). However, due to sluggish temporal responses (Irvin, Casagrande, & Norton,

1993), it is unlikely that K cells produce non-linear temporal signatures that overlap

with those generated within the M pathway.

8.4.3 Updating the three-pathway hypothesis

It is convenient to describe the afferent visual pathways from the retina to the

LGN and V1 in terms of segregated M, P and K streams that subserve different

functions. However, current evidence suggests that this view is far too simplistic, and

that the three stream hypothesis should be replaced by either a multi-stream hypothesis

or a network model, in which most cell types contribute to the perception of most

attributes (Jazayeri & Movshon, 2006; Kaplan, 2014). These considerations call for

cognitive neuroscientists to develop more sophisticated techniques for teasing apart

contributions from multiple, overlapping afferent pathways.

8.4.4 Other M-driven subcortical visual pathways

Due to the nature of fast-m sequence stimulation, non-linear VEP kernel

waveforms are strongly localised to V1, without much contribution from extrastriate

sources (Fortune & Hood, 2003). This makes the non-linear VEP technique well suited

for studying M and P contributions to processing within V1. However, this technique is

not well suited to studying M projections via other subcortical pathways.

Object recognition was once viewed as a hierarchical, bottom up neural process,

but current models indicate that rapid projection of M input to the orbitofrontal cortex

facilitates bottom-up object perception (Kveraga et al., 2007). In addition, current

models of emotional processing suggest that visual input is projected from the retina to

the amygdala within several M-driven subcortical routes via the superior colliculus and

pulvinar (Yoshor, Bosking, Ghose, & Maunsell, 2007). Human studies of M-driven

subcortical routes to the cortex have mostly relied on creating stimuli that bias visual

181

processing towards the M or P pathways. For example, low luminance contrast versus

isoluminant chromatic stimulation, or low and high spatial frequency stimulation

(Kveraga et al., 2007; Vlamings et al., 2009; Vuilleumier et al., 2003). A challenge for

this area of research will be to develop more robust, non-invasive methods for probing

these alternate M pathways.

8.4.5 Individual differences

Comparisons of scalp-recorded VEPs across individuals are complicated by a

large degree of inter-subject variability in the waveforms. This is likely to reflect

individual differences both in cortical folding patterns, and retinotopic maps of V1

(DeYoe et al., 1996; Engel, 2012). When singular value decomposition (SVD) and

principle components analysis (PCA) are applied to multifocal VEPs, two components

account for a high percentage of intra- and inter-subject variance in the data (Dandekar,

Ales, Carney, & Klein, 2007; Zhang & Hood, 2004). This makes it possible to resolve

VEPs that arise from very close cortical sources, such as V1 and V2 (Ales, Carney, &

Klein, 2010; Carney, Ales, & Klein, 2008). Future studies that apply these techniques

would enable more accurate comparisons of K2.1 and K2.2 waveforms across groups

with low and high autistic tendency, dyslexia and dyscalculia (see Chapter 2).

8.5 Conclusions In conclusion, non-linear analysis of multifocal VEPs has enabled researchers to

study scalp-recorded signals that appear to arise from the M and P visual pathways. This

has led to a great deal of research into the ways in which input from the M and P

pathways contributes to visual processing.

This thesis used non-linear VEP, in combination with other techniques to

provide novel contributions to three main areas of research. The results demonstrate that

it would be an oversimplification to attribute the effects of a red background on

stimulus processing to suppression of the subcortical M pathway. Furthermore, it would

be an oversimplification to attribute all aspects of chromatic processing to signals

arising from the P and K pathways. On the contrary, the work presented here implies

that the effects of blue chromatic saturation on cortical evoked responses are generated

by a mechanism with rapid recovery from stimulation, which may reflect M input to the

cytochrome oxidase blob centres in V1. Finally, evidence presented within this thesis

182

suggests that the effects of oxytocin on the very early stages of visual processing vary,

depending on the presence of emotionally relevant stimuli.

This thesis highlights difficulties in linking human behaviour and scalp-recorded

VEPs with M and P physiology, and speaks to the advantages of using a variety of

complementary techniques to investigate the earliest stages of visual processing. Future

research would benefit from the development of non-invasive techniques for studying

the multiple parallel pathways from the retina to the cortex.

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Appendix A: Certificates of ethics approval

A.1 SUHREC Project 2015/064: Transformations in Visual Cortex: From neural

input to recognition

The experiments presented in Chapters 4 and 5 were part of the above-

mentioned project. The procedures were approved by the Swinburne University Human

Research Ethics Committee, and were conducted in accordance with the Declaration of

Helsinki

190

01/03/2018 Mail - lhugrass@swin.edu.au

https://outlook.office.com/owa/?realm=swin.edu.au&exch=1&path=/mail/inbox 1/1

FW: Acknowledgement of Report for SUHREC Project ‐ 2015/064

FYI again Laila, Sally 

‐‐‐‐‐Original Message‐‐‐‐‐ From: resethics@swin.edu.au [mailto:resethics@swin.edu.au]  Sent: Thursday, 1 March 2018 8:18 AM To: David Crewther <dcrewther@swin.edu.au> Cc: RES Ethics <resethics@swin.edu.au> Subject: Acknowledgement of Report for SUHREC Project ‐ 2015/064 

Dear David, 

Re: End of Student Involvement  Report  for the project 2015/064 

'Transformation in Human Visual Cortex ‐ from Neural Input to Recognition' ﴾Report Date: 28‐02‐2018﴿ 

The End of Student Involvement  report  for the above project has been processed and satisfies the reportingrequirements set under the terms of ethics clearance. 

Thank you for your attention to this matter. 

Regards Research Ethics Team 

Swinburne Research ﴾H68﴿ Swinburne University of Technology PO Box 218 HAWTHORN VIC 3122 Tel: 03 9214 3845 Fax: 03 9214 5267 Email: resethics@swin.edu.au 

Sally Fried on behalf of RES EthicsThu 1/03/2018 8:18 AM

To:Laila Hugrass <lhugrass@swin.edu.au>; RES Ethics <resethics@swin.edu.au>;

191

A.2 SUHREC Project 2017/027: Transformations in Visual Cortex: From neural

input to recognition

The experiments presented in Chapters 6 and 7 were part of the above-

mentioned project. The procedures were approved by the Swinburne University Human

Research Ethics Committee, and were conducted in accordance with the Declaration of

Helsinki

192

06/03/2018 Mail - lhugrass@swin.edu.au

https://outlook.office.com/owa/?realm=swin.edu.au&exch=1&path=/mail/search 1/1

FW: Acknowledgement of Report for SUHREC Project ‐ 2017/027

FYI again Laila...Sally 

‐‐‐‐‐Original Message‐‐‐‐‐ From: resethics@swin.edu.au [mailto:resethics@swin.edu.au]  Sent: Wednesday, 28 February 2018 2:05 PM To: David Crewther <dcrewther@swin.edu.au> Cc: RES Ethics <resethics@swin.edu.au> Subject: Acknowledgement of Report for SUHREC Project ‐ 2017/027 

Dear David, 

Re: End of Student Involvement  Report  for the project 2017/027 

'A brain study of oxytocin's effects on social cognition on ageing' ﴾Report Date: 28‐02‐2018﴿ 

The End of Student Involvement  report  for the above project has been processed and satisfies the reportingrequirements set under the terms of ethics clearance. 

Thank you for your attention to this matter. 

Regards Research Ethics Team 

Swinburne Research ﴾H68﴿ Swinburne University of Technology PO Box 218 HAWTHORN VIC 3122 Tel: 03 9214 3845 Fax: 03 9214 5267 Email: resethics@swin.edu.au 

Sally Fried on behalf of RES EthicsWed 28/02/2018 2:06 PM

To:Laila Hugrass <lhugrass@swin.edu.au>; RES Ethics <resethics@swin.edu.au>;

193

Appendix B: Authorship Indication Forms

B.1 Authorship indication for the paper presented in Chapter 2

194

B.2 Authorship indication for the paper presented in Chapter 3

195

B.3 Authorship indication for the paper presented in Chapter 4

196

197

B.4 Authorship indication for the paper presented in Chapter 5

198

B.5 Authorship indication for the paper presented in Chapter 6

199

B.6 Authorship indication for the paper presented in Chapter 7

200

Appendix C: Summary of Journals in which paper are/are to be

published

Papers In Press

Hugrass, L., Verhellen, T., Morrall-Earney, E, Mallon, C & Crewther, D.P. (In Press).

The effects of red surrounds on visual magnocellular and parvocellular cortical

processing and perception. Journal of Vision

201

Papers In Submission Hugrass, L., & Crewther, D. (In Submission). The afferent pathway origins of scalp

recorded visual evoked potentials - A review

• This work has been submitted for publication as a review in Experimental Brain

Research. It is currently with the reviews editor.

Hugrass, L., & Crewther, D. (In Submission). A review of non-linear visual evoked

potential research into contributions from the human M and P pathways to

cortical vision.

• This work has been submitted for publication as a review in the European

Journal of Neuroscience

Hugrass, L., Labuschagne, I., Price, A., & Crewther, D. (In Submission). Part 1:

Intranasal oxytocin modulates very early visual processing of emotional faces.

• This paper has been submitted for publication as an original research article in

Hormones and Behaviour. It is currently under review.

Hugrass, L., & Crewther, D. (In Submission). Part 2: Acute intranasal oxytocin does not

influence the non-linear temporal structure of cortical visual evoked potentials.

• This paper has been submitted for publication as an original research article in

Hormones and Behaviour. It is currently under review.

Papers In Preparation

Hugrass, L., & Crewther, D. (In Preparation). The temporal structure of evoked MEG

responses: Effects of chromatic saturation

• This work is currently in preparation for submission as an original research

article for the journal, Frontiers in Neuroscience.