Phase delays within visual cortex shape the response to steady-statevisual stimulation

Benoit Cottereau a,e,!, Jean Lorenceau a, Alexandre Gramfort b, Maureen Clerc b,Bertrand Thirion c,e, Sylvain Baillet a,d,ea COGIMAGE, Centre de Recherche de l'Institut du Cerveau et de la Moelle, CRICM, UPMC-UMRS 975 INSERM-UMR 7225 CNRS, Hôpital de la Salpêtrière, Paris, Franceb Odyssee Project Team, ENPC/ENS/INRIA Sophia-Antipolis, Francec INRIA Parietal, Neurospin, Saclay, Franced Departments of Neurology & Biophysics, Froedtert and the Medical College of Wisconsin, Milwaukee WI, USAe Federative Institute for Neuroimaging, IFR49, Saclay, France

a b s t r a c ta r t i c l e i n f o

Article history:Received 27 July 2010Revised 30 September 2010Accepted 2 October 2010Available online 21 October 2010

Keywords:VisionRetinotopyMagnetoencephalography (MEG)Steady-State Visual Evoked Response (SSVER)Source Imaging

Although the spatial organization of visual areas can be revealed by functional Magnetic Resonance Imaging(fMRI), the synoptic, non-invasive access to the temporal characteristics of the information !ow amongstdistributed visual processes remains a technical and methodological challenge. Using frequency-encodedsteady-state visual stimulation together with a combination of time-resolved functional magnetic sourceimaging from magnetoencephalography (MEG) and anatomical magnetic resonance imaging (MRI), thisstudy evidences maps of visuotopic sustained oscillatory neural responses distributed across the visual cortex.Our results further reveal relative phase delays across responding striate and extra-striate visual areas, whichthereby shape the chronometry of neural processes amongst these regions. The methodology developed inthis study points at further developments in time-resolved analyses of distributed visual processes in themillisecond range, and to new ways of exploring the dynamics of functional processes within the humanvisual cortex non-invasively.

© 2010 Elsevier Inc. All rights reserved.

Introduction

Investigation of the human visual system using functional brainimaging techniques has thrived over the past decade, with contribu-tions essentially from functional Magnetic Resonance Imaging (fMRI)studies. These latter complement advantageously animal electrophy-siogical studies, which are limited by the coverage of only a few brainregions at once. Amongst other signi"cant "ndings, neuroimagingstudies have reported on the retinotopic (also known as visuotopic)properties of the human visual system–whereby neighbouring neuralpopulations respond to neighbouring stimuli in the visual "eld(Sereno et al., 1995)–within an increasing number of brain structuresresponding selectively to a variety of visual attributes (Wandell et al.,2005, 2007). Indeed, sophisticated paradigm designs associated toprogresses in data analysis have improved the ability of fMRI to revealdetailed retinotopic maps within and beyond the striate cortex (seee.g., Saygin and Sereno, 2008). Accessing the timing properties ofneural events and capturing the !ow of visual information within andacross visual maps are crucial to understanding the integrativemechanisms ruling perception (Nowak and Bullier, 1997). In that

respect, the relatively slow hemodynamic response that rules thedynamics of fMRI measures remains a limiting factor. Non-invasiveelectrophysiological techniques such as electroencephalography(EEG) and magnetoencephalography (MEG) provide the necessary"ne temporal resolution, but are usually considered to lack thesuf"ciently "ne spatial resolution necessary to access retinotopicvisual processes. Recent studies however have shown that EEG andMEG responses coupled with sourcemodelling techniques are speci"cof the retinotopic organization of the visual cortex (e.g., Ales et al.,2009). For instance, Di Russo et al. have demonstrated that thelocation of equivalent current dipole (ECD) models of EEG visualcomponents properly localizes within the individual fMRI retinotopicmaps obtained in the same subjects (Di Russo et al., 2002). Therefore,source estimation of visual evoked potentials has been recentlyapproached by constraining the EEG source model to the individualfMRI retinotopic maps. Following this methodology, Vanni et al.(2004) were able to report on the respective timing of responses fromdistinct visual areas, although the anatomical information conveyedby ECD modelling remains limited. Some authors have compareddistributed EEG (Im et al., 2007), MEG (Moradi et al., 2003; Brookeset al., 2010) or combined MEG/EEG (Sharon et al., 2007) sourceimaging models with fMRI retinotopic maps, within and beyond V1.These studies however, did not address the relative timing ofresponses across visual areas. Magnetic source imaging (MSI) of

NeuroImage 54 (2011) 1919–1929

! Corresponding author. Centre de Recherche de l'Institut du Cerveau et de la Moelle,Equipe COGIMAGE 47, boulevard de l'hôpital 75651 Paris Cedex 13, France.

E-mail address: cottereau@ski.org (B. Cottereau).

1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved.doi:10.1016/j.neuroimage.2010.10.004

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MEG signals using distributed source models and statistical mappinghas contributed to reveal reproducible, dynamic propagation patternsof evoked responses within primary visual areas with (Yoshioka et al.,2008; Hagler et al., 2009) or without (Poghosyan and Ioannides,2007) fMRI weighting constraints.

The primary objective of the present study was to develop animaging methodology capable of revealing the relative chronometryof distributed neural responses to visual stimulation within andbeyond primary visual areas. Through the combination of temporally-encoded stimulation with time-resolved multimodal imaging–using apiecewise MEG source imaging approach constrained to MRIstructural data (Cottereau et al., 2007)–our results reveal both thespatial and temporal organization amongst multiple visual areas,which is essentially consistent with their retinotopic distribution.

Materials and methods

Subjects

Three healthy volunteers (S1, S2 and S3; 2males, 1 female, [26–29]age range) with normal or corrected-to-normal vision participated inthe study and provided their informed consent. The paradigm hadbeen previously reviewed and approved by the Internal Review Boardof La Salpêtrière Hospital, Paris, France.

Stimulation and paradigm design

Neurons in early visual areas are sharply tuned to local orientationand spatial frequency and are organized in columns, around pin-wheels (Bonhoeffer and Grinvald, 1991). The stimuli thereforeconsisted of radial patterns with multiple orientations at differentspatial frequencies to optimize the recruitment of a large ensemble ofthese neurons. The stimulus object was derived from a radial spatialfrequency pattern (4 to 5 cycles, Michelson contrast 96%) presented

against a dark grey background (103 cd m!2) and spatially convolvedwith a circular Gaussian "lter of increasing full-width at halfmaximum (FWHM) with stimulus eccentricity. In all conditions,stimuli were displayed through back-projection on a translucentscreen located 90 cm away from the subject, using a video projector(60 Hz refresh rate) located outside the magnetically-shielded room.

A "rst experimental run (RETINO) consisted in testing ourapproach to MEG source imaging for the resolution of the 4 visualquadrants and horizontal meridians. Six different experimentalconditions were designed with the presentation of a single stimulusobject located in the (1) upper left, (2) upper right, (3) lower left, (4)lower right visual quadrants and along the (5) left or (6) righthorizontal meridians. The width of the stimulus object was 2.66°withan eccentricity of 5.49° when presented along one of the fourquadrant diagonals. The left and right horizontal meridians were bothsampled using the simultaneous presentation of 4 pattern elements ofsizes (FWHM): 0.22, 0.35, 0.71 and 1.3°, at 0.62°, 1.52°, 3.3 and 6.64° ofeccentricities, respectively. These conditions were assembled intoacquisition blocks of trials as illustrated Fig. 1 and detailed hereafter.

A second experimental run (PROXIMATE) tested the ability of theimaging methodology to resolve between responses to stimuli ofeither different sizes, or presented with proximate eccentricities.Stimulus objects were presented along either one of the two diagonalsof the upper visual "eld. A PROXIMATE run consisted of fourexperimental conditions: a single stimulus object with a width of0.35° was presented at 3.14° of eccentricity in (1) the upper-left or (2)the upper-right part of the visual "eld; in the last two conditions, thestimulus object was 0.71° wide and was presented at 7.13° ofeccentricity in (3) the upper-left or (4) the upper-right visualquadrants. The sizes of the stimuli objects in conditions (3) and (4)of the PROXIMATE run could "t within the objects used to stimulatethe upper-left and upper-right visual "eld in conditions (1) and (2) ofthe RETINO run. The stimuli of PROXIMATE conditions (1) and (2) didnot overlapwith these latter andwere placed at smaller eccentricities.

Fig. 1. Protocol roundup: (A) Summary of the stimuli alternatively presented in the course of the study (RETINO run): visual quadrants, with variable eccentricities and horizontalmeridians; (B) Time line of stimulus presentation for one typical trial: 0.6 s "xation, 6 s stimulus presentation, followed by a 2 s inter-stimulus interval (ISI); (C) the RETINO runconsisted of 15 trials with each of the 6 experimental conditions (total duration of acquisition: about 15 min per subject). Note each horizontal meridian was sampled using 4patterns of sizes (FWHM): 0.22°, 0.35°, 0.71°, 1.33° simultaneously presented at eccentricities 0.62°, 1.52°, 3.3° and 6.64°, respectively.

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The positions of all stimuli used in the PROXIMATE run are shownFig. 4.

In both runs, all presentation trials started with the display of acoloured "xation disk at the center of the screen during 600 ms. Togenerate a steady-state visual evoked response (SSVER), the stimulusobject was then displayed and counter-phased (using patternreversal) at 7.5 Hz (i.e. the pattern being presented changed every67 ms) for a trial duration of 6 s. To focus the subjects' visual attentionand minimize eye movements, the "xation disk randomly changedcolour every second during stimulus presentation. After each trial,subjects were asked to report on which colour was the most frequentat the "xation point during stimulus presentation. All experimentalconditions were repeated and randomly interleaved through 15 trials.

Data acquisition and source modelling

MEG was recorded using a 151-channel whole-head MEG arrayconsisting of axial gradiometers (CTF/VSM MedTech Omega System,Coquitlam, BC, Canada) and time sampled at 1250 Hz. A surfacetessellation of each individual cortical surfaces (grey matter–cerebro-spinal !uid interface, 10,000-node triangulation) was obtained fromT1-weighted (high-contrast fast spoiled gradient-echo sequence, 124axial slices, 1.3 mm thickness, 240!240 mm "eld of view) MRI imagevolumes processed using the brainVISA segmentation pipeline(http://brainvisa.info) with subsequent hand editing, when neces-sary. Co-registration of MRI and MEG coordinate systems wasachieved by aligning 3 anatomical landmarks (nasion and pre-auricular points) that were manually detected in both modalities.The MEG source image support consisted of elementary ECD sourcesdistributed at each node of the tessellated cortical envelope, andoriented perpendicularly to its surface (Baillet et al., 2001). MEGsource analysis was performed using the BrainStorm softwareapplication (http://neuroimage.usc.edu/brainstorm). A best-"ttingspherical head volume conductormodelwas adjusted to the subjects'scalp envelope, and MEG source reconstruction was performedwith a multiresolution image model selection (MiMS; Cottereau etal., 2007) approach. Its principles are summarized as follows: thebuilding blocks of the MiMS source model are parcels of the corticalsurface, which are selected iteratively through a multiscale process.Compact parametric models of current !owswithin brain parcels areef"ciently modeled using current multipole expansions (Jerbi et al.,2002, 2004). Parcels are selected through the optimization of thegeneralized cross-validation error, which is closed-form for thebroad class of linear estimators of neural current amplitudes, suchas classical weighted minimum-norm estimators (WMNE, see Bailletet al., 2001 for an overview). The MiMS model selection speci"es theoptimal set of weights to be used in the "nal WMNE, to obtain thetime-courses at each node of the cortical tessellation and for eachexperimental trial.

Analysis of SSVER sensor data

Steady-state responses to pattern reversal presentations wereanalyzed from the original MEG sensor data, prior to source analysis.The data segments consisted of the last 5.6 s of each of the 15 trials.The "rst 400 ms of data following stimulus onset were discarded astransient episodes before the SSVER regime was established. Powerspectrum estimation was performed at each of the 151 MEG sensorsusing Welch's averaged modi"ed periodogram estimator computedover 1.6 s rectangular windows with 50% overlap (Marple, 1987). Thesteady-state power analysis was complemented with a time–frequency decomposition of selected sensors (Gröchenig, 2001):Morlet wavelets were designed with a 20 ms standard deviation of aGaussian kernel at the central frequency.

Analysis of SSVER source amplitudes

MEG source analysis provided a map of time-resolved distributedelementary cortical responses for each trial, condition and run. Thepower spectral density of each elementary source time series wasestimated using the same method as above. As detailed in the Resultssection, the analysis of SSVER sensor data revealed that powerestimation at each elementary cortical node could be restricted at thefrequency of the second harmonic (i.e., 15 Hz) of the fundamentalfrequency used for stimulus presentation. Baseline period powerspectral density estimation was conducted at 15 Hz across trialsduring the last 400 ms of the 600 ms time window where only a"xation disk was presented at the center of the screen.

For each condition, MSI amplitude maps were assessed forsigni"cance by contrasting responses to SSVERs with the restingbaseline using non-parametric permutation tests. False positives werediscarded by retaining only themain connected components of supra-threshold sources (pb0.001, Frackowiak et al., 2004). As typical inmost retinotopic neuroimaging investigations (e.g., Sereno et al.,1995; Wandell et al., 2005), topographical maps of responses topreferred stimulus locations were computed by labelling eachelementary cortical node with the stimulus location to which it wasresponding with greater, signi"cant amplitude with respect tobaseline. One subject (S2) performed the RETINO run also in fMRI.In supplement to the RETINO run, the fMRI session consisted of theclassical mapping of S2's visual areas. These data and the qualitativecorrespondence with MEG source image maps are shown inSupplementary Material 1.

Analysis of SSVER relative source phases

Elementary sources with signi"cant (pb0.001, see above) powermodulations were considered for phase estimation. The phase of theFourier transform of each of these signi"cant elementary corticalsource time series was estimated at 15 Hz during steady-stateresponse for each trial. Phase values were then averaged across trialsfor each experimental condition, which is equivalent to computingthe phase of the average of the normalized Fourier coef"cients at thefrequency of interest. Cortico-cortical phase differences were thenestimated relatively to the cortical source in the occipital cortexwhose average phase value was the smallest across trials for a givenexperimental condition (see Fig. 1). This source was located in theimmediate vicinity of the posterior cuneus. Phase values relatively tothe pattern-reversal stimulus onset for quadrant 1, 2, 3 and 4 were65°, 88°, 14° and 32° for subject 1, 62°, 31°, 12° and 40° for subject 2and 41°, 34°, 90° and 64° for subject 3, respectively. The exact phase ofthe stimulus was not considered as reference to avoid consideringuncontrolled delays in neural conductions across subjects (i.e. fromthe retina to the cortex). The correlation between phase-shifts and thegeodesic distance between cortical regions was also evaluated andtested for signi"cance.

Numerical simulations

Numerical simulations were performed to control for possibleconfounds in the measure of phase-shifts that could have been inducedby MEG source estimation. Two MEG datasets were synthesized usingthe cortical source time series obtained from the analysis of the bottom-right stimulation of subject S1's visual "eld in the RETINO run (seeFig. 3). In Dataset 1, the time series assigned to the elementary sourceswithin the regions detected as sustaining signi"cant power modulationat 15 Hz were those estimated from the original measurements. InDataset 2, these sources were assigned the same time serie: the onefrom the source serving as phase reference. Hence Dataset 2 wasgenerated using elementary source signals with no phase differencesand was used to test whether MSI would introduce spurious phase

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differences amongst reconstructed source time series. Dataset 1 wasused to evaluate whether MSI would be able to recover the true,simulated phase delays between cortical source time series. Realisticnoisy conditions were applied to both datasets by distributing randomsinusoidal time series across the cortical locations outside of theactivated regions. The frequency and phase of each confounding sinewave was randomly distributed within [0, 50] Hz and [0, 360] degrees,respectively. Their amplitudes were adjusted so that the signal-to-noiseratio (SNR) reached 14 dB. SNR was de"ned as the ratio between theroot-mean-square (RMS) power across the sensor array of the MEGsignals generated by the sources located in the activated regions and theRMS power of MEG traces generated by the random confoundingsignals. As in the actual experimental conditions, 15 trials weregenerated by randomly redistributing the frequency and relative phasesof the nuisance sinusoidal sources.

Results

MEG steady-state visual evoked responses

In both the RETINO and PROXIMATE runs, SSVERs elicited strongresponses at the second harmonic frequency (15 Hz) of the pattern

reversal stimulation. The sensor topography of these responses wastypical of major involvement of parieto-occipital cortices as illustratedFig. 2. These well-de"ned SSVER in the frequency domain served asfrequency-tagged targets for MSI. This approach had previously provenbeing a powerful experimental strategy to the detection of sustainedstimulus- or behaviour-related neural responses within and beyond thevisual system (e.g., Parkkonen et al., 2008; Jerbi et al., 2007).

Topographical maps of responses to preferred stimulus locations

Fig. 3 displays the individual sourcemaps of the RETINO responses at15 Hzon smoothed representationsof the individual cortical surfaces, tobetter reveal the source activity located inside sulci (Wandell et al.,2007; Dumoulin and Wandell, 2008; Levin et al., 2010).

In all three subjects, signi"cant activation was detected essentiallyalong the calcarine sulcus and matched the expected central occipitalsymmetry with respect to the position of the stimulus in the visual"eld. The size of these regions and the spatial continuum of activationpatterns suggest that they may encompass the V1 and V2 visual areas.

Additional regions about the intra-parietal sulcus were detected insubjects S1 and S3. The fMRI data from subject S2 revealed strongsimilarities with the MSI source maps (Supplementary Material 1).

Fig. 2. MEG surface topographies of SSVER: The steady pattern reversal stimulation elicited strong ongoing responses at the second harmonic of the stimulating frequency (15 Hz),starting about 400 ms after stimulus onset. (A) The "gure shows the average topography across the three subjects when the left horizontal meridian was stimulated. The topographyof the power spectrum of responses at three frequency bins (7.5 Hz, 15 Hz and 22.5 Hz) is shown, for comparison. The plots are on a !attened version of the original sensor array,nose pointing upwards. The signal power is expressed as a Z-score with respect to the power captured during the resting baseline at the same frequency bins. The topography ofmagnetic "elds at 15 Hz shows a clear visuotopic organization: strong responses are detected essentially over occipital and parietal sensor sents. (B) Group average sensortopographies in response to the stimulation conditions in the RETINO run.

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Table 1 displays the surface areas of the regions that were foundbeing activated in each experimental condition of the RETINO run.

Although these surface extents were found being in the samerange, these results indicated within and inter-subject differences,which are consistent with previous "ndings within the primary visualareas using fMRI (Dougherty et al., 2003).

Sensitivity to the size and location of the stimulus object

The spatial resolution of MSI mapping with respect to the size andeccentricity of the stimulus object was evaluated. Fig. 4 presents thesource maps obtained in conditions 1 and 2 of the RETINO run (i.e.

stimulation of the upper-left and upper-right visual "eld) and fromthe 4 conditions of the PROXIMATE run (i.e. stimulation of the samevisual quadrants but with different stimulus sizes and eccentricities).

The surface extents of the detected areas responding in theseexperimental conditions are summarized in Table 2.

These results corroborated the threefold prediction that (1)because of the cortical magni"cation factor (Rovamo and Virsu,1979), the surface extents of activated regions in response tostimulation at different eccentricities should be similar, (2) at "xedeccentricities, the primary area of cortical activation in response tosmaller stimulus objects would "t within those to larger stimulusobjects (RETINO run) and (3) primary cortical retinotopic projectionof stimulus objects with larger eccentricities would be more anteriorand along the calcarine fold, with respect to those in response to morefoveal stimulation within the same visual quadrant.

The ratio of surface overlap between the cortical areas respondingin the conditions of the PROXIMATE and to the stimulation of theupper visual quadrants in run RETINO is detailed in Table 3.

As hypothesized, the responses to smaller objects shown at largereccentricity values demonstrated greater overlap with the areasresponding to larger stimulation objects with similar eccentricity.Symmetrically, the responses to objects shown closer to the center ofthe visual "eld were found to have negligible surface overlap with

Fig. 3. MSI investigation of the retinotopic cortex: Rows S1, S2 and S3 report on data from each of the 3 subjects, respectively. Two leftmost columns: detection of neural signalchanges with respect to baseline rest. The dorso-medial views of the smoothed versions of the cortical surfaces are presented. Smoothed cortical surface representations help revealthe fundi inside cortical folds such as the parieto-occipital (PO) and calcarine (CA) "ssures. Colours on cortex encode for the position of the pattern-reversal stimulus object in thesubject's visual "eld as recalled at the top of each of the two leftmost columns. Typical retinotopic responses were detected on the surface of the left and right hemispheres of eachsubject. Four rightmost columns: cortical mapping of phase lags across retinotopic regions, for the 4 stimulus objects presented at the top of each of the 4 rightmost columns. Here,colours on cortex encode for phase lags across brain regions, pooled in 5 classes (see colour legend at the top of the rightmost inset). Systematic investigation of the phase differencesbetween the posterior occipital cortex and the rest of the brain regions responding with signi"cant amplitude change to stimulation with respect to resting baseline (pb0.001, asshown in the corresponding two leftmost columns) revealed a hierarchical topography of the phase lag maps. Phase lags increase along the dorsal and ventral parts of the occipitalcortex (see also Fig. 7) and reveal sharp discontinuities across both adjacent and more distant regions.

Table 1Surface areas (in cm") of activated brain regions in the conditions of the RETINO run, forall 3 subjects.

Subjects/conditions

Upperleftquadrant

Upperrightquadrant

Lowerleftquadrant

Lowerrightquadrant

Leftmeridian

Rightmeridian

S1 11 7.6 8.4 11 9.7 5S2 12 21 14 5.2 18 15S3 8.8 4 7.4 4.5 2.5 3.5

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responses from stimulation at larger eccentricities in the same visualquadrant. These effects were signi"cant (binomial test p=0.016).

Maps of phase lags across the visual cortex

Fig. 5 displays the time courses of four cortical sources during thestimulation of the lower-right visual-"eld of subject S1. The corticalregion with smallest average phase across trials was located in themost posterior part of the cuneus. Phase differences betweenresponding cortical areas sustaining oscillatory responses werede"ned with respect to this location. The time–frequency decompo-sition of source time series in responding cortical areas revealed thatthe SSVER established about 400 ms after the onset of the patternreversal stimulation, following a short burst between 8 and 10 Hzimmediately after stimulus onset. This "gure also illustrates how timeseries of cortical responses along the dorso-medial occipital andparietal cortex can be extracted and compared for phase differences.Maps of steady-state phase differences were computed for eachsubject and the RETINO run; they are displayed in Fig. 3. The regionaldistribution of these phase lags revealed an organized topographicdistribution along the dorsal (ventral respectively) pathway forstimulus objects presented in the lower (upper respectively) visualhemi"elds. Respectively to the stimulus location in the lower or upperhemi"elds, phase lags across the MSI maps gradually increased up to60° with distance along the dorsal (ventral, respectively) occipital

cortex. Two classes of sharp leaps reaching up to 80–110° and 170°phase delays were further evidenced within the immediate contin-uation of the primary visual maps and the posterior bank of theparieto-occipital sulcus, respectively (rightmost columns in Fig. 3).

A systematic analysis of how phase lags were found increasingwith the geodesic distance to the phase-reference cortical location inthe posterior cuneus is shown Fig. 6. In responses to stimulationduring the RETINO run within the lower visual hemi"elds, a strongeffect of the cortical geodesic distance was found using linearregression analysis. Regression coef"cients were found takingstrikingly similar values within and across subjects (26±5.4°/cm).Similar, though milder, effects were found for responses to stimula-tion within the upper visual hemi"eld.

The (uncorrected) p-values associated to the linear regressionanalysis are provided in Table 4.

The effect of phase delaywith increasing cortical distancewas foundbeing signi"cant (pb0.05) in 9 out of 12 experimental conditions.Whenall conditions were pooled together (i.e. the respective responses tostimulationwithin each of the four quadrants , for all three subjects), theeffectwas highly signi"cant (p=1.4e–19). To testwhether these effectswere not consequences of an amplitude-latency confound the linearregression analyses of source amplitudes versus geodesic distances andamplitude versus phase lags were performed (see SupplementaryMaterial 2). No signi"cant correlation between these factors was found,therefore there exists a unique correlation of increasing phase delays

Fig. 4.MSI sensitivity to variations of stimulus eccentricity: Results obtainedwith the 3 subjects (S1–3) are shown in response to 3 categories of stimulus objects. The"rst category is givenby conditions 1 and 2 of the RETINO run and the two others are provided by the four conditions of the PROXIMATE run (2 different eccentricities in the left and right upper-visual "elds intotal). These objects were presented in the left (inset (A)) or right (inset (B)) visual "eld. Views are identical than those de"ned for Fig. 3. Colours used tomap cortical activations matchthose of the stimulus objects represented on the left hand side of this"gure. Coloured contour lines are used to delineate theprojections of corticalMSI activation in response to the smallerstimulus objects shown to the left. Cortical areas sustainingMSI activation in response to the largest stimuli are tiledwith also the respective corresponding colours. Qualitative assessmentof the results indicate that the relative topography ofMSI cortical activationsmatches predictions: (1) responses to smaller stimuli objects "t within the responses to larger!ashed objectswith similar eccentricity, while (2) responses to objects with more central eccentricities are more posterior to those to objects with larger eccentricities.

Table 2Surface of cortical areas (in cm") responding to stimulation at two differenteccentricities in the left or right upper visual quadrants. Data from all 3 subjects.

Subject/conditions 3.14°Eccentricity(upper leftquadrant)

3.14°Eccentricity(upper rightquadrant)

7.13°Eccentricity(upper leftquadrant)

7.13°Eccentricity(upper rightquadrant)

S1 4.3 2.5 2 7.5S2 3 7.6 4.1 10S3 3.2 3 10 6.9

Table 3Percentage overlap between the cortical areas responding in the conditions of thePROXIMATE and to the stimulation of the upper visual quadrants in run RETINO.

Subject/conditions

3.14°Eccentricity(upper leftquadrant)

3.14°Eccentricity(upper rightquadrant)

7.13°Eccentricity(upper leftquadrant)

7.13°Eccentricity(upper rightquadrant)

S1 0% 5.1% 13% 94%S2 5.8% 0 % 17% 61%S3 3.2% 3.5% 25% 77.8%

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with increasing geodesic distance on the cortex from the posterioraspects of the cuneus.

Evaluation through synthetic data

We sought to refute through numerical simulations the possibilitythat the observed phase differences would pertain to cross-talk mixingeffects between brain areas induced by the MEG source imagingprocedure. Fig. 7 displays the average time series estimated across the15 trials for each of the 4 regions of interest de"ned in Fig. 5 andresulting for the analysis of the synthetic datasets. Results from Dataset1 are similar to the ones retrieved from the original experimental data.Results from Dataset 2 indicate that the simulated synchronous sourcesare correctly retrieved by the MSI technique as well. Hence the MSItechnique did not introduce spurious phase delays between source timeseries (see Fig. 7). Further, the phase delays between the elementarysource that served as phase reference in the original experimental dataand the rest of the sources in the region of activation were computedacross the 15 trials of each of the two datasets. Comparison betweensimulated and reconstructed phase lags revealed that the estimationerror reached 6.9±4.3° of phase angle on average for Dataset 1, and3.31±3.8° for Dataset 2. These values are small with respect to thephase lags estimated from the experimental data (see Fig. 3).

Discussion

Spatial resolution of MSI maps

Early investigations of the retinotopic cortex with MEG consideredthe simple ECD model to account for the generation of primary MEG

visual evoked "elds (VEF) (Ikeda et al., 1998). Without the explicitcontribution of structural MRI data, the resulting MEG activationmodel remains anatomically crude and is challenged when multipleextended brain areas contributed to the response to stimulation.Some authors have interestingly considered ECD models constrainedto fMRI contrast maps (Vanni et al., 2004) to report on time delaysbetween retinotopically-organized brain regions. Though it is tempt-ing to enforce some correspondence between fMRI and MEGactivation maps (Liu et al., 1998), multiple conceptual (Logothetisand Wandell, 2004) and technical (Liu et al., 2006) challenges remainoutstanding. Distributed source models for MSI explicitly integrateanatomical data within the MEG source estimation process to suggestgenuine imaging models of cortical current !ows (Baillet et al., 2001).This approach extends the possibilities of MSI to the statisticalmapping of e.g., amplitude and size effects within source activationmaps (Cottereau et al., 2007). Fine spatial features of the primaryvisual system may further be evidenced by identifying somedynamical properties of distributed cortical current !ows, asexempli"ed with the effects of cortical magni"cation revealed in theprimary visual cortex (Lefèvre and Baillet, 2009). This, with otherrecent studies, builds a growing body of evidence that MSI offers amethodological trade-off in terms of excellent temporal and adequatespatial resolutions to access dynamical features of functional systems.Indeed, the present analysis of the RETINO run demonstrated that theexpected basic retinotopic organization about the calcarine fold couldbe retrieved using MSI and SSVERs. Further, we demonstrated thatthis approach could reveal predictable effects of the eccentricity andsize of the stimulus object on the topography of cortical currents, asillustrated with the analysis of the PROXIMATE run in this study. Thecorrespondence with the fMRI retinotopic maps was shown in onesubject (see Supplementary Material 1) and strengthens the signif-icance of these results, even though the comparison remainsqualitative at this point (Auranen et al., 2009).

The spatial accuracy of MSI mapping relies on the propergeometrical registration of MRI with MEG. We brought carefulattention to this issue: consistency between the digitized positionsof head coils used for head-positioning in the MEG and theirlocalization during the actual MEG recording was checked by theMEG system positioning system. A discrepancy of less than 5 mmwasrequired. Another source of misalignment is in the detection ofanatomical landmarks (nasion and periauricular points) in the MRIvolume, which is also on the range of about 5 mm as discussed inSchwartz et al. (1996). Overall, though misregistration is a source ofabsolute localization error for MEG sources, we claim it has limitedin!uence on the results presented here, which are essentially basedon comparing source maps across experimental conditions withinindividuals: no absolute position values expressed in a geometricalreferential are provided and discussed.

We are also con"dent that the relative inaccuracy of MEG-MRIregistration has limited impact because of the expected, retinotopicdistribution of MEG source maps that were indeed obtained throughour data analysis. Severe misregistration errors would have spoiledand distorted these maps and would have been detrimental to theirconsistency–in terms of the retinotopic distribution of regionalresponses and their relative phase delays–within and across subjects.

The cortical tessellations that were used for source imagingconsisted of 10,000 triangle vertices, with an inter-node distancebelow 5 mm on average. In (Lin et al., 2006), it was shown thatapplying a loose-orientation constraint to 7,500-dipole image modelsimproved by only a few millimetres the performances in localizationof a variety of distributed source imaging approaches when comparedwith models where the orientations of elementary sources are strictlyorthogonal to the cortical surface. Here, a denser image grid was used(10,000 vs 7,500, as in e.g., Jerbi et al, 2007; Del Cul et al., 2007;Sergent et al., 2005). The approximations in MEG head modeling arecertainly detrimental–though less than in EEG–to the accuracy of

Fig. 5. Regional dynamics of SSVERs (Subject S1): Inset (A) shows the typical time coursesof cortical responses in the areas marked as coloured disks along the dorsal occipito-parietal cortex (colours of the disks in inset legendmatch the colours of the correspondingtime series displayed). Note that these regions of interest are also used in Fig. 7. Thestimulation time line is shown at the top of the plot, with the actual stimulus objects beingpresented. Time t=0ms is stimulus onset. After a period of transition, sustainedoscillatory responses are readily identi"ed in these regions. The outcome of the analysisof the phase lags is summarized in Fig. 3 for all 3 subjects and all experimental conditions.Inset (B) displays the time–frequency decomposition using Morlet wavelets, of theresponsedetected in theposterior part of the cuneus (dark bluedot in cortical legend). Thetime–frequencymap reveals a short burst between 8 and 10 Hz during a 400 ms period oftransition before the responses locks to its second harmonic (15 Hz).

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source estimation. Buchner et al. (1995) have shown however thaterrors due to such approximations were more prominent withindeeper brain regions that those revealed by our study.

On the usage of steady-state visual evoked responses

Steady-state evoked brain responses havemarked a long history inelectrophysiology, and studies of the visual system in particular (seee.g., Narici et al., 1998; Appelbaum et al., 2006; Ales and Norcia, 2009).The accompanying hypothesis is that the brain systems driven by theongoing stimulation are entrained to stationarity– after a relativelyshort period of transition– at the fundamental frequency of thestimulation and/or some of its higher-order harmonics (Vialatte et al.,2009). This approach de"nes well-identi"ed, tagged evoked brainresponses which can readily be extracted from ongoing MEG/EEGbrain signals using spectral analysis. The SNR in the data is enhancedef"ciently by multiplying the presentation of the visual motifs within

a compact time frame. Here, a stimulus presentation rate of 7.5 Hzduring the last 5.6 s–i.e., after having discarded the "rst 0.4 s to allowfor steady-state regime to establish–of each of the 15 trials yielded15!5.6!7.5=630 repetitions for each experimental condition.

As illustrated Fig. 5, the initial transient components from theevoked visual response seem to occur with latencies within the sametime range (about 100 ms) across a rather large region of the occipitaland inferior dorsal parietal cortices. This latency was expected, as it isa typical early component of event-related visual responses. Asalready discussed above, the dynamics of visual responses varyamongst visual areas and little is known about the way they areorchestrated: single areas are usually studied at once and in a givenexperimental context (e.g., static vs. moving gratings). There is verylimited knowledge of how they organize in space and time fromtransient to steady-state and our data cannot address this keyquestion yet. Our study is a report on a technical advance on how touse MEG source imaging to reveal the temporal organization withindistributed visual areas. We hope this will inspire future studies toinvestigate more speci"c dynamical properties of the information!ow with the human visual cortex.

SSVERs at the second harmonic of the visual stimulation rate

We report on SSVERs peaking dominantly at the second harmonicof the stimulation frequency, which is concordant with former studies(Fawcett et al., 2004; Di Russo et al., 2007). The physiological originsto this temporal frequency-doubling effect has been assumed to

Fig. 6. Phase shifts vs. geodesic distance to phase-reference location for all RETINO conditions, except stimulation of horizontal meridians (not shown). Results are shown for all threesubjects (S1, S2 and S3) and the stimulus presentations depicted along the top row of the "gure. Geodesic distances were computed along the cortical surface tessellation used forMSI, between the phase-reference source and the cortical locations which preferred SSVER corresponds to the stimulation location indicated at the top of each column in the "gure.The respective linear regression models are shown with dotted-grey lines and their corresponding equation.

Table 4(Uncorrected) p-values associated to the linear regression analysis of the phase lagsversus geodesic visual quadrants in run RETINO.

Subject/conditions Upper leftquadrant

Upper rightquadrant

Lower leftquadrant

Lower rightquadrant

S1 0.04 1.16e–4 4.6e–6 2.4e–8S2 3.8e–6 6.9e–3 1.4e–4 0.1S3 8.1e–4 0.34 7.2e–6 0.06

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originate from (1) a non-linear transfer function that may involve–amongst other possible phenomena–cortical contrast gain control inthe presence of strong photic stimulation, and/or (2) recti"ed neuralresponses to reversing visual patterns (Zemon and Gordon, 2006).Indeed, brain responses to !ashing stimuli without contrast reversalhave been shown to be dominant at the stimulation fundamentalfrequency (Moratti et al., 2007). Phase alignment of oscillatory neuralresponses within local cell assemblies and across thalamocorticalcircuits are necessary to generate SSVERs, as investigated in recentcomputational models (Robinson et al., 2008). The precise, physio-logical origins of the readily observed frequency-doubling effectremain speculative at this stage, and may require a more completeunderstanding of the selective role played by the magno- and parvo-cellular visual pathways, their ON and OFF-cell subsystems, andfeedback connections.

Completeness of SSVER MSI mapping

We have found that the spatial sensitivity achieved by the analysisof SSVER was satisfactory in occipital areas. The cortical mapping ofthe horizontal meridians of the visual "elds is considered aschallenging for EEG or MEG source imaging, because neural currentgenerators along the superior and inferior banks of the calcarine foldare supposed to have opposite orientations. The associated “cruciformmodel” predicts that activity from V1 would be cancelled out andtherefore would not be detectable in EEG or MEG, whencorresponding parts of the upper and lower visual "elds are bothstimulated (Jeffreys and Axford, 1972). We show however in thisstudy reliable activationmaps along the calcarine sulcus. These resultsare in agreement with those from Ales et al. (2010), who

demonstrated that the electric "elds generated from within V1would not cancel out when the full visual "eld is stimulated.

In addition to responses within the occipital poles, which areassociated to primary visual areas (V1, V2 and V3), some of theexperimental conditions in the present study also led to signi"cantresponses in the parieto-occipital sulcus region (subjects S1 and S3and in the right hemisphere of subject S2). These regions aresometimes labelled either as V6 (Pitzalis et al., 2006) or IPS (Wandellet al., 2007), and studies have shown they demonstrate someretinotopic properties. At some other occasions in the present study,(subjects S3, left hemisphere of S1 and right hemisphere of S2),responses were detected along the transverse occipital sulcus in theregion of the V3A/V3B complex, which contains a full retinotopic mapand a fovea representation (Larsson and Heeger, 2006).

The question may arise however whether the visual mapsobtained are biased by the frequency-tagging experimental proce-dure. In that respect, we may question whether the brain areas driventhrough ongoing stimulation represent a limited subset of visual areasthat would otherwise be detected using more conventional stimula-tion. This point may be more speci"cally addressed in the context ofretinotopic mapping. Recent fMRI investigations have indeedreported on a growing number of cortical (Silver et al., 2005; Sayginand Sereno, 2008) and sub-cortical (Schneider and Kastner, 2005)retinotopic areas that were not retrieved in our study for reasons weshall now discuss. At the cortical level, higher-order retinotopic areas(e.g., in motion-sensitive temporal cortex and frontal regions) havebeen revealed using stimuli constructed from biological motion"gures (Saygin and Sereno, 2008). Though we report on activationsextending to nonstriate visual cortex, the identi"cation of high-levelretinotopic maps in humans using MSI reaches beyond the scope ofthe present investigation, which focus was to prove the concept ofextracting temporal dynamics across retinotopic cortical maps usingMSI. The use of stimuli tailored to known functional properties ofextrastriate areas is likely to elicit robust and signi"cant responsesthat MSI might identify, depending on the adequacy of their localneuronal integration time constants and the frequency of stimulation.Further, access to sub-cortical responses using MEG has long beenconsidered as a challenge, because of the rapid drop in SNR withincreasing distance from the neuronal sources to the magneticsensors. Some studies however have recently con"rmed and extendedearlier evidence of the detection of non cortical regions as deep as thebrainstem (Parkkonen et al., 2009) usingMEG. Attal et al. (2009) haverecently designed a complete anatomical and physiological MSIsource model that includes subcortical structures, which may helpreveal the retinotopic organization of some subcortical regions infuture studies.

Phase lags across visual responses

Beyond the demonstration of retinotopic mapping using MSI, thisstudy emphasizes the possible access to relative timingmeasures acrossvisual brain areas. The measure of systematic phase differences acrossbrain regions may be an entry point to the characterization of theircomputational properties and functional connections. A 90° phase lagmay for instance re!ect time derivative or integrative properties ofvisual areas, taking earlier responses to later stages. This technical abilitymay extend to the identi"cation of visual areas based on their relativephase chronometry (Nowak and Bullier, 1997). This is exempli"ed bythe sharp discontinuities found in the spatial distribution of phase lagsacross the occipital cortex and along the dorsal (ventral respectively)pathway for stimulus presentation in the lower (upper respectively)visual hemi"elds. As a general "nding, we showed how phase delaysincreased with the geodesic distance to the posterior cuneus. As animportant caveat though, the phase differences across these visual areaswere measured for a single fundamental frequency of stimulation andtherefore cannot be extrapolated to general dynamics of visual

Fig. 7. Synthetic data and the recovery of original phase lags: The colours of the timeseries correspond to the estimated source activation at the points of interest as de"nedon the legend inset to the right, which are identical to those used in Fig. 5 in the realexperimental data. (A) For reference, the original source time series used as generatorsof dataset 1, as reproduced from Fig. 5. (B) Time series in the 4 regions of interestestimated from dataset 1; the source time series show great similarity with the originalsin (A). (C) Time series estimated from dataset 2 (identical simulated sources, hencewith no phase lag); no spurious phase delay was introduced by MSI estimation.

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responses, which can only be accessed through the identi"cation of afull, wideband transfer function. Systematic measures of phase delayswith respect to a wider range of stimulus-tagging fundamentalfrequencies shall be addressed in forthcoming studies. For the sake ofillustrating this important point however, the 90° phase discontinuityfound immediately anterior to themost posterior occipital areasmay beinterpreted as a 16 ms phase delay (at a 15 Hz response frequency),which is compatiblewith the 11 ms timing delays betweenV1 andV2 inthe macaque brain (Nowak and Bullier, 1997). Larger phase lags–reaching up to 190°–were further identi"ed in all subjects in the vicinityof the parieto-occipital fold. These "ndings are consistentwith previousevidence from fMRI that dorsomedial (DM) parietal areas are alsovisuotopically-organized visual cortices (Wandell et al., 2005). A 170°phase delay would account for a time difference of DM steady-stateresponses in the range of 30 ms with respect to V1's. Recent evidencefrom primate studies has shown however that DM areas receive directfeedforward projections principally from V1 and V2 areas (Rosa et al.,2009). Though this would make 30 ms a relatively long time delay formonosynaptic connections between these two sets of areas, thismeasure would be compatible with multisynaptic stages within DMitself (Nowak and Bullier, 1997). A competitive assumption wouldconsider that phase delays close to 180° might be due to imagereconstruction artefacts of MEG sources. This hypothesis is discussedand ruled out by the analysis of synthetic data conducted in the presentstudy. Whether such phase lags are due to propagation and/or to adistinctive functional competence can be further investigated bycomparing the MEG phase-lag maps with the visual regions identi"edfrom a more standard fMRI approach. This again can be illustrated anddiscussed for subject S2: the borders between visual areas as revealedfrom fMRI andMSI, were found to be very similar. In particular, a strongphase discontinuitywas found between the borders of V1 and V2 in thissubject.

Altogether, these "ndings demonstrate that a steady-state para-digm coupled with MRI-informed MSI analysis provides the "netemporal, and fair spatial resolutions that are essential to reveal thetemporal organization and dynamic interplay between cortical areasat the individual level. Additional investigations to identify thetransfer of temporal processes on a broader spectrum of stimulationfrequencies would be a natural extension to this initial study.

Conclusion

Characterizing the temporal dynamics of neural activity withinand between human cortical areas can drastically improve ourunderstanding of how visual areas are recruited or coupled to yielda coherent visual perception of the environment. Jointly using powerand phase information elicited by frequency-tagged focal stimula-tions, we characterized and differentiated visual regions respondingwith a variety of phase lags to the stimuli. Altogether, our resultsprovide strong evidence that MEG source imaging can ef"ciently anddistinctively map visual areas, opening the way to further explora-tions of the visual system aiming at characterizing the temporal !owof visual processing in healthy humans or patients.

Supplementarymaterials related to this article can be found onlineat doi:10.1016/j.neuroimage.2010.10.004.

Acknowledgments

The authors are grateful to Antoine Ducorps and Denis Schwartzfrom the Center for Neuroimaging Imaging Research (CENIR) in Paris,France and Rey R. Ramirez from the Department of Neurology,Medical College of Wisconsin in Milwaukee, USA for precioustechnical assistance during data acquisition and/or analysis.

This research was supported by the French National ResearchAgency (Agence Nationale pour la Recherche) through the ViMAGINEproject (ANR-08-BLAN-0250).

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