Core dysfunction in schizophrenia:electrophysiology trait biomarkers

Koychev I, El-Deredy W, Mukherjee T, Haenschel C, Deakin JFW.Core dysfunction in schizophrenia: electrophysiology trait biomarkers.

Objective: Core symptoms of schizophrenia, particularly in thecognitive domain are hypothesized to be due to an abnormality inneural connectivity. Biomarkers of connectivity may therefore be apromising tool in exploring the aetiology of schizophrenia. We usedelectrophysiological methods to demonstrate abnormal visualinformation processing during in patients performing a simplecognitive task.Method: Electrophysiological recordings were acquired from 20chronically ill, medicated patients diagnosed with either schizophreniaor schizo-affective disorder and 20 healthy volunteers while theyconducted a working memory (WM) task.Results: The patient group had significantly lower accuracy on theWM task and a trend for slower responses. An early visual evokedresponse potential was reduced in patients. Analysis of theelectroencephalographic oscillations showed a decreased phase-lockingfactor (in the theta, beta and gamma bands) and signal power (thetafrequency band). The beta and gamma oscillatory abnormalities wereconfined to two sets of correlated fronto and occipital electrodes.Conclusion: The findings of event-related potential and oscillatoryabnormalities in patients with schizophrenia confirm the sensitivity ofearly visual information processing measurements for identification ofschizophrenia phenotype. The fronto-occipital distribution of theoscillatory abnormalities replicates our findings from a schizotypalsample and implicates a possible top-down dysfunction as avulnerability trait.

I. Koychev1, W. El-Deredy2,T. Mukherjee1, C. Haenschel3,J. F. W. Deakin1

1Neuroscience and Psychiatry Unit, The University ofManchester, Manchester, 2School of Psychology, TheUniversity of Manchester, Manchester and 3Departmentof Psychology, City University, London, UK

Key words: schizophrenia; information processing; P1;oscillations; top-down

Ivan Koychev, Neuroscience and Psychiatry Unit, TheUniversity of Manchester, G.800, Stopford Building,Oxford Road, Manchester M13 9PT, UK.E-mail: ivan.koychev@doctors.org.uk; ivan.koychev@doctors.org.uk

Accepted for publication January 26, 2012

Significant outcomes

• Patients with schizophrenia are characterized by abnormalities in the early event-related P1 potentialand its corresponding low-frequency (theta band) evoked oscillations.

• A distributed fronto-central network in higher-frequency (beta and gamma band) oscillations in isalso abnormal in the patient group, possibly representing a top-down deficit.

• The early visual processing deficits in patients with schizophrenia replicate results from a previouslypublished schizotypy study from our group, indicating that these are trait characteristics of theschizophrenia spectrum disorder.

Limitations

• The finding of correlated fronto-central occipital activity could also represent the same response thatis volume conducted rather than the activation of separate neural populations.

• The study did not test the specificity of the early information processing abnormalities to theschizophrenia spectrum by comparing the study groups with other psychiatric entities (e.g. bipolardisorder).

Acta Psychiatr Scand 2012: 126: 59–71All rights reservedDOI: 10.1111/j.1600-0447.2012.01849.x

� 2012 John Wiley & Sons A/S

ACTA PSYCHIATRICASCANDINAVICA

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Introduction

Schizophrenia is currently diagnosed on the basisof psychotic symptoms (e.g. hallucinations anddelusions) present for most of the time in a 4-weekperiod along with social or occupational dysfunc-tion. The lack of specificity and longitudinalstability of psychotic features (1) leads to opera-tional criteria accepting the risk that aetiologically,prognostically and clinically heterogeneous dis-orders may be lumped together in one condition(2). This complicates within- and inter-sampleanalysis and limits the relevance of fundamentalresearch. With respect to genetic studies for exam-ple, none of the identified risk genes are specific toschizophrenia but rather indicate a general vulner-ability to mental health disorders or personalitypsychopathology (3).One way of decreasing the extreme clinical and

probably aetiological heterogeneity of schizophre-nia as defined using the 4th revision Diagnosticand Statistical Manual of Mental Disorders (4)(DSM-IV) is to decompose its manifestations intodistinct clinical dimensions (e.g. positive, negative,cognitive, disorganization, mood, motor symptomdomains) (5). These dimensions have been broadlyreplicated (5) and discriminate with regard totreatment response and course (6–8). The dimen-sions may in turn reflect separate etio-pathogenicprocesses that are associated with measureablebiological correlates (biomarkers or intermediatephenotypes) (9–11). The successful development ofsuch biomarkers would validate the dimensionsaetiologically and may ultimately be better atdefining specific drug targets (12).A dimension that may be particularly relevant

to the vulnerability to schizophrenia and thereforeits core pathophysiology is the cognitive symptomdomain. Cognitive dysfunction is highly prevalentin schizophrenia (13–15) and at-risk populations(16, 17), is present in the premorbid stages (18),persists over time (19, 20) and is linked tomorphological cortical abnormalities (21). Impor-tantly, it independently predicts functional out-come (22), an effect that may be due tobreakdown of self-monitoring processes affectingsocial interactions (23). It has been hypothesizedthat this symptom domain may be at leastpartially due to a break-up in the fine coordina-tion of neural activity (coined �dysconnectivity�)(24, 25). Therefore, defining reliable measures ofneural coordination is a prominent direction ofbiomarker development aimed at exploring cogni-tion and its abnormalities.Neural connectivity patterns in schizophrenia

have been explored while patients perform working

memory (WM) tasks, a cognitive function consid-ered to be a cardinal neuropsychological feature ofthe disease (26, 27). Working memory is proposedto be underpinned by the prefrontal cortex (28),and research into connectivity using event-relatedfMRI (29) or electroencephalography (30) hastherefore focused on activity within this region.Recent evidence has indicated that WM deficit inschizophrenia may also be at least partly beaccounted for by dysfunction in the processing ofsensory information (31). Event-related potentials(an encephalographic technique based on theaveraging of data following repetitive sensorystimulation) revealed that the primary visualcortex response (P1 event-related potential) ofpatients and at-risk individuals is of diminishedmagnitude (32–36). This P1 amplitude reductionhas been shown to predict WM performance (32)and other executive functions (37, 38). Event-related potentials are summations of the ongoingneural oscillatory activity (39), which in turn hasbeen proposed to be a key mechanism that allowscoordination of distributed neural networks (40).Accordingly, neural oscillations generated by thesensory cortex in the earliest stages of informationencoding are also abnormal in schizophrenia (41–46), and this oscillatory aberration has also beenshown to predict cognitive performance (41, 42).Therefore, ERPs and neural oscillations recordedduring perception may be reliable in determiningthe presence of disturbed neural connectivity andmay act as �footholds� linking the cognitive dys-function to its aetiology.One key limitation of using information pro-

cessing biomarkers to aid the diagnostics ofschizophrenia is their lack of specificity. Both P1and evoked oscillation abnormalities have beenreported in bipolar disorder (47–49) and disordersthat involve sensory or higher-order corticaldamage (50–53). One possible way of dealingwith this is to obtain further temporal and spatialcharacteristics of the sensory cortex signals inschizophrenia. These may help define a patternthat is relatively specific to the disorder. Forexample, the oscillatory abnormalities in patientswith schizophrenia and at-risk individuals appearwithin the first 150 ms and are present in distrib-uted networks encompassing sensory and prefron-tal cortex (44). Such long-range communicationsunderlie the naturally occurring modulation ofperception by higher-order structures (54) and itsbreakdown may explain the sensory deficits inschizophrenia (55, 56). Functional biomarkers ofthis �top-down� dysfunction might be more specificto the schizophrenia phenotype and perhaps infor-mative of its pathophysiology.

Koychev et al.

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Aims of the study

In this study, we aimed to confirm the presence andcharacteristics of sensory information processingabnormalities in schizophrenia. We hypothesizedthat these are vulnerability traits and therefore willreplicate the patterns observed in a previous studyin schizotypal personality.

Material and methods

Subjects

Twenty-eight patients diagnosed according toDSM-IV (4) with schizophrenia or schizoaffectivedisorder were recruited from mental health careteams and clozapine clinics in Greater Manchesterand Merseyside areas of the United Kingdom.Patients were included if they: i) had been diag-nosed at least 1 year prior to the appointment; ii)had been clinically stable for at least 6 monthsprior to the appointment; iii) did not suffer fromany neurological disorders; iv) had normal orcorrected-to-normal vision. Seven patients wereexcluded because of poor performance on the WMtask: three patients did not complete it while fourother patients had a below chance performance(<50% correct responses). Also, the data from onepatient was not included in the final analysisbecause of unacceptable signal-to-noise ratio.Twenty-six controls were recruited from a data-

base of responders to an online version of theSchizotypal Personality Questionnaire (SPQ) (57).Participants who scored in the average SPQ range(9–36) were invited for an appointment where theycompleted the questionnaire again and wereincluded in the study if the score remained in the9–36 range. The SPQ cut-off scores were based onthe results from a recently completed study usingthe SPQ on the same population (58) and the SPQmanual. All controls were free from prescribedmedication (apart from the contraceptive pill) andhad no history of mental health disorders asconfirmed by the Mini-International Neuropsychi-atric Interview. All controls reported normal orcorrected-to-normal vision and had no history ofneurological conditions. Six controls were excludedbefore any data were recorded because of SPQscores out of the 9–36 range.The final groups used in the analysis consisted of

20 (five female) patients and 20 (seven female)controls. They were well matched in terms of age,but the controls had significantly higher IQ(P < 0.001). All included patients were takingantipsychotic medication (13 clozapine only, oneclozapine and flupentixol, one clozapine and que-

tiapine, one clozapine and amisulpride, two ris-peridone depot, one risperidone depot andaripiprazole, and one aripiprazole only). Nine ofthe patients were also taking selective serotoninreuptake inhibitors for mood and anxiety symp-toms. The demographic data of the includedsample, the mean SPQ score of the controls, aswell as the mean duration of disease, Brief Psychi-atric Rating Scale (BPRS) score and daily anti-psychotic dosage of the patient group are presentedin Table 1.

Stimuli and task – WM experiment

A delayed discrimination task [minor modificationsfrom a previously published study (32)] that probesthe effect of WM load on visual informationprocessing was presented on a personal computerusing E-Prime software (http://www.pstnet.com/).Thirty-six non-natural objects were presented inthe centre of a black screen (0.6 · 0.6 visualangles). The participants were instructed toencode one, two or three subsequently presentedimages into WM. Each trial began with a screensaying �New trial� after which the encoding stimuliwere presented for 400 ms each. The encodingimages were separated by an interstimulus intervalof 600 ms during which a fixation cross appearedon the screen. After a delay (maintenance) periodof 6 s, a target probe appeared on the screen(presentation time 3 s) and the participants had toindicate whether it was part of the initial sampleset. The participants pressed �Y� if the target imagewas part of the encoding sequence and �N� if it wasnot. An interstimulus interval of 1 s separated thetrials. Each block consisted of 60 trials, 20 of eachWM load. The trials were intermixed pseudo-randomly. All subjects included in the analysiscompleted four blocks.

Table 1. Subject characteristics

Patients withschizophrenia Controls

Significancevalue

Age 36.4 € 9.0 34.7 € 7.8 P = 0.54Sex 5 female 7 female v2 = 0.496IQ (NART) 105.9 € 9.0 114.6 € 4.9 P = 0.001SPQ total score NA 21.0 € 7.8BPRS total score 28.4 € 8.4 NADDD 1.53 € 0.51 NADuration of disease 14.5 € 7.2 NAFamily history of schizophrenia

First degree 2 0Second degree 3 1Third degree 4 0No history 11 19

BPRS, Brief Psychiatric Rating Scale; DDD, defined daily dose; SPQ, SchizotypalPersonality Questionnaire.

Information processing in schizophrenia: an EEG study

61

ERP data acquisition, processing and analysis

Continuous EEG recording was obtained usingActiveTwo BioSemi electrode system (BioSemi,Amsterdam, the Netherlands) from 72 electrodesdigitized at 512 Hz with an open passband fromDC to 150 Hz. In the BioSemi system, the classical�ground� electrodes are replaced with two separateones: Common Mode Sense active electrode andDriven Right Leg passive electrode. These twoelectrodes form a feedback loop, which drives theaverage potential of the subject (the CommonMode voltage) as close as possible to the ADCreference voltage in the AD-box (the ADC refer-ence can be considered as the amplifier �zero�).A detailed description of the BioSemi electrodereferencing and grounding convention can be foundat http://www.biosemi.com/faq/cms&drl.htm.Data were analysed using besa version 5.2 (Brain

Electric Source Analysis, Grafelfing, Germany).For the purpose of the analysis an averagedreference was employed. Only trials where theparticipants responded correctly were included inthe analysis. The epoch within each trial wasdefined as the period starting at 400 ms prestimu-lus and ending at 1000 ms poststimulus. For theencoding phase, the stimulus was defined as the lastobject to appear within the encoding series (object1 in load 1, object 2 in load 2 and object 3 in load3). For the retrieval phase, the stimulus was thetarget image. Baseline was defined as the period)100 to 0 ms. All electrode channels were sub-jected to automatic artefact rejection in the periodstarting at 400 ms prestimulus to 1000 ms poststi-mulus to correct for blinks and saccades (thethresholds for exclusion of vertical and horizontalmovements were ±250 and ±150 lV respec-tively). The continuous data was then examinedfor outstanding blink artefacts and those wereremoved manually. Trials that survived artefactcorrection were averaged together with a low-passfilter of forward-phase shift of 0.3 Hz (6 dB ⁄oc-tave) applied before the procedure. A high-passfilter of 0-phase shift of 30 Hz (24 dB ⁄octave) wasused after averaging. The mean percentage accep-tance rate and standard deviation for the twogroups is as follows: patients with schizophrenia –encoding 91.2 ± 6.7% and retrieval 91.8 ±10.1%; controls – encoding 97.8 ± 2.7% andretrieval 98.5 ± 1.7%. This difference was statis-tically significant for both encoding and retrieval(P < 0.001 and P < 0.01 respectively).Because the task was designed to probe the early

visual components (P1, N1) averaged mean ampli-tude from eight occipital electrodes was used (PO8,PO4, 02, O1, PO3, PO7, Oz and POZ). P1 was

calculated by extracting the mean amplitude of the20-ms window centred on the mean latency of thecomponent. Using the same approach the meanamplitude of a 30 ms window was used to identifythe N1 values. The choice of duration of window ofinterests was based on observations that the P1peak is of shorter duration (peaking within 100–130 ms) while N1 peaks over a longer period (150–200 ms) (59). The latency of the two ERPcomponents was established in each participantby examining the Global Field Power and extract-ing the latency of the increases in activity corre-sponding to the P1 and N1 peaks. The latencyvalues for the P1 and N1 peak were then enteredinto a repeated measures anova with within-sub-jects factors of WM load, condition (encoding andretrieval) and electrode and a between-subjectfactor of group (patients and healthy volunteers).In case of main effect of group or condition, themean amplitude of the ERP component would becalculated at the mean latency for each group orcondition. The mean latency of the P1 componentin encoding was 125.5 ± 2.3 ms for the patientsand 127.8 ± 2.3 ms for the controls. In retrieval,the P1 latency was 119.4 ± 2.5 and 118.0 ±2.5 ms for patients and controls respectively. Thedifference in P1 latency between encoding andretrieval was significant (main effect of condition,F1,38 = 49.700, P < 0.001). There was no effect ofgroup or WM load on latency, nor did theyinteract. The P1 analysis was therefore performedat 118–138 in encoding and 108–128 in retrieval.The mean latency of the N1 component in encod-ing was 177.0 ± 3.8 ms for the patients and183.0 ± 3.8 ms for the controls. In retrieval, theN1 latency was 175.0 ± 3.4 and 180.0 ± 3.4 msfor patients and controls respectively. The differ-ence in N1 latency between encoding and retrievalwas not significant. There was no effect of group orWM load on latency, nor did they interact. The N1analysis was therefore performed at 163–193 msfor both conditions.The mean amplitudes of the P1 and N1 peaks

were entered into repeated measures ANOVAswith within-subjects factors of WM load andelectrode and a between-subject factor of group(patients and healthy volunteers) in two separateanalyses (encoding and retrieval respectively). Incase of a significant main effect of WM load, weused polynomial contrast to determine the charac-ter of the effect.

Time-frequency analysis

The retained trials were subjected to a complexMorlet wavelet transformation, and signal power

Koychev et al.

62

and phase-locking factor (PLF) values were calcu-lated for each time point according to Tallon-Baudry et al. (60) in frequency steps of 1 Hz. Thewavelet transformation results in complex coeffi-cients of the decomposition of EEG signal as aweighted sum of the wavelet functions. It can beseen as a convolution of signal with waveletfunctions: windowed, complex sinusoids, with thedistinctive property that the width of the Gaussianwindow is coupled to the sinusoid frequency.Therefore, as the frequency increases the timeresolution increases, whereas the frequency resolu-tion decreases (60). Signal power is a measure ofthe magnitude, that is, the absolute value of thewavelet coefficients. The PLF is a quantitativeapproach for measuring the phase synchronizationthat tests the consistency of phases across trials inthe EEG spectrum at particular frequencies. ThePLF at frequency f and time point t is defined as

¼ 1

L

XL

i¼ 1

ejuift

�����

����� ¼1

L

XL

i¼ 1

xift

xift�� ��

�����

�����

where uift is the phase of the complex waveletcoefficients xift and L is the number of trials. Thiscorresponds to projecting of wavelet coefficientsonto a unit circle in the complex plane andtherefore is independent of the signal power.Phase-locking factor is a measure of the phase

variability across trials within each electrode. Itprovides information regarding the degree ofsynchronization between the oscillations and theevent onset, separate from its signal power. PLFranges between 0 and 1, 0 indicating completelyrandom-phase distribution across trials and 1perfectly synchronized phase angles across trials(61). Based on the findings from our previous studyin high schizotypy using the same task and set-up,the measures were calculated for three frequencybands (5–8, 14–28 and 30–50 Hz) in the 100- to250-ms poststimulus interval. Topography plotsshowed that the activity was confined to twoseparate sets of electrodes: fronto-central (FC1,FC2, FCz, C1, C2 and Cz) and occipital (PO8,PO4, 02, O1, PO3, PO7, Oz and POZ). The meanpower and PLF values of these two sets ofelectrodes were extracted for each level of thetask for encoding and retrieval separately. Therepeated measures anova therefore included twowithin-subject factors (WM load and electroderegion: occipital and fronto-central) and onebetween-subject factor (group: patients andhealthy volunteers). Spearman�s correlations wereperformed to determine whether the activity in thetwo sets of electrodes is correlated.

Covariate analysis

The potential influence of disease duration, symp-tom severity and medication dose on the electro-physiological measures was investigated byincluding them as covariates in a secondary anal-ysis on the patient group only. Disease chronicitywas expressed in years of illness; symptom severitywas represented by the total score on the BPRS(62); medication dose was described using defineddaily dose (DDD). DDD is a statistical measure ofdrug consumption developed by the World HealthOrganization (http://www.whocc.no/ddd/defini-tion_and_general_considera/). It is defined as theassumed average maintenance dose per day for adrug used for its main indication in adults. DDDwas preferred to the conventionally used chlor-promazine equivalents because of its good reliabil-ity (63) and the lack of agreement on thechlorpromazine equivalence of clozapine (64, 65).Significant effects were followed by correlationsbetween the outcome variable and the covariate todetermine the direction of the effect.

Ethical approval

The study has been approved by 11th RegionalEthics Committee, Preston (reference number09 ⁄H175 ⁄90) and has been carried out in accor-dance with the ethical standards laid down in the1964 Declaration of Helsinki.

Results

Behavioural data

Data on reaction time and accuracy of responses onthe task are presented on Fig. 1. In terms of reactiontimes, there was no main effect of group. There wasa main effect of WM load (F2,76 = 84.526, P <0.001; partial g2 = 0.69), the reaction time increas-ing linearly with demand (F1,38 = 130.029, P <0.001; partial g2 = 0.75) in both groups.The patient group also had significantly lower

accuracy (F1,38 = 46.424, P < 0.001; partial g2 =0.55) compared with controls. The effect of WMload on the percentage correct variable was signif-icant (F2,76 = 32.809, P < 0.001; partial g2 =0.46) because of linear decrease in accuracy withdemand ⁄or load (F1,38 = 726.097, P < 0.001; par-tial g2 = 0.67) in both groups.

ERP data

P1 component. The patient group had a signifi-cantly reduced P1 amplitude during encoding

Information processing in schizophrenia: an EEG study

63

1500

Reaction times ERP task

100

Percentage correct ERP task

**(b)(a)

800900

10001100120013001400

70

75

80

85

90

95 *

WM load 1 WM load 2 WM load 3

Schizophrenia patients 1024 ± 215 1157 ± 200 1214 ± 236Controls 887 ± 165 1049 ± 166 1140 ± 170

600700R

eact

ion

times

(ms)

WM load 1 WM load 2 WM load 3

Schizophrenia patients 78.5 ± 10.5 75.5 ± 10.7 71.1 ± 9.9Controls 96.1 ± 3.7 92.1 ± 7.1 85.6 ± 7.3

60

65Perc

enta

ge c

orre

ct

Fig. 1. Reaction times (a) and accuracy (b) of the working memory task in both study groups. (a) Reaction time in ms on the verticalaxis, working memory load on the horizontal axis. (b) Accuracy in percentage correct on the vertical axis, working memory load onthe horizontal axis. Error bars represent the standard deviation; asterisk denotes p < 0.05 for the main effect of group.

1

2

3

4Control groupPatient group

2

3

4Control groupPatient group

Event-related potentials at PO7: retrievalEvent-related potentials at PO7: encoding

P1 P1

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–200 0 200 400 600 800 1000–6

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Time (ms)–200 0 200 400 600 800 1000

–6

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Time (ms)

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1.52.02.53.03.5

P1 amplitude retrieval

1.52.02.53.03.5 P1 amplitude encoding

Control groupPatient group

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Working memory loadLoad 1 Load 2 Load 3

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Control groupPatient group

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–0.5

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Working memory loadLoad 1 Load 2 Load 3

Mea

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plitu

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Fig. 2. Event-related potential results for patients (black lines) and controls (grey lines). (a) An event-related potentials plot forencoding (left hand side) and retrieval (right hand side) for the PO7 electrode. Amplitude in microvolts on the horizontal axis, time inmilliseconds on the horizontal axis. (b) P1 amplitude in response to encoding (left hand side) and retrieval stimuli (right hand side).Mean amplitude in microvolts on the horizontal axis, working memory load on the horizontal axis; (c) N1 amplitude in response toencoding (left hand side) and retrieval stimuli (right hand side). Mean amplitude in microvolts on the horizontal axis, workingmemory load on the horizontal axis. Error bars represent standard deviation; asterisk denotes p < 0.05 for the main effect of group.

Koychev et al.

64

(F1,39 = 5.763, P = 0.02; partial g2 = 0.13) andretrieval (F1,39 = 7.138, P = 0.01; partial g2 =0.15) compared with controls (Fig. 2a). There wasno main effect of WM load on amplitude, and itdid not interact with diagnosis.

N1 component. The amplitude of the N1 poten-tial was significantly reduced in the patientgroup during retrieval (F1,38 = 5.830, P = 0.02,

partial g2 = 0.13) but not encoding (Fig. 2b).There was no main effect of WM load, but itinteracted with group during retrieval(F2,76 = 4.354, P = 0.02; partial g2 = 0.1). Theeffect was attributed to reduction in N1 ampli-tude with load in patients but a reversed patternin controls (F1,38 = 5.608, P = 0.02; partialg2 = 0.13).

Table 2. Summary of phase-locking factor (PLF) and power results

Encoding Retrieval

5–8 Hz 16–28 Hz 30–50 Hz 5–8 Hz 16–28 Hz 30–50 Hz

PLFANOVAs: P-values

Group <0.001 0.02 0.01 <0.001 0.04 NSLoad NS NS 0.07 0.04 NS NSGroup-by-load NS NS NS NS NS NS

Clinical correlates: P-valuesSeverity NS NS NS NS NS NSDuration 0.05 NS NS 0.08 NS NSDrug dose NS NS NS 0.06 0.07 NS

Signal powerANOVAs: P-values

Group 0.01 NS NS 0.008 NS NSLoad NS NS NS NS NS NSGroup-by-load NS 0.04 NS NS NS 0.06

Clinical correlates: P-valuesSeverity NS NS NS NS NS NSDuration NS NS NS NS NS NSDrug dose NS 0.04 NS 0.02 NS NS

Controls Patients

Phase-locking factor

–0.5

–0.4

–0.3

–0.2

–0.1

0

0.1

0.2

0.3

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0.5

Phase- locking factor

–0.1

–0.08

–0.06

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Phase- locking factor

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–0.06

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–0.02

0

0.02

0.04

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Signal power

Controls Patients

Controls PatientsControls Patients

(a) (b)

(c) (d)

Fig. 3. Topography plot of the time-frequency values of the evoked oscillations in the 100-250 ms post-stimulus period in both studygroups. View from above; frontal electrodes facing upwards and occipital electrodes facing downwards. (a) Phase-locking factorvalues of 5-8 Hz evoked oscillations; (b) Phase-locking factor values of 14-28 Hz evoked oscillations; (c) Phase-locking factor valuesof 30-50 Hz evoked oscillations; (d) Signal power values of the 5-8 Hz evoked oscillations.

Information processing in schizophrenia: an EEG study

65

Time-frequency analysis

Phase-locking factor data. Table 2 presents a sum-mary of the results concerning PLF.

Phase-locking factor: theta band range. In the5–8 Hz range (Fig. 3a), the patient group hadsignificantly lower PLF values relative to the con-trols in both encoding (F1,38 = 14.730, P < 0.001;partial g2 = 0.28) and retrieval (F1,38 = 12.559,P = 0.001; partial g2 = 0.25). There was a maineffect of WM load only during encoding (F2,76 =19.72, P < 0.001; partial g2 = 0.34), because of alinear decrease of PLF with load. The factorelectrode region was highly significant in encoding(F1,38 = 33.862, P < 0.001; partial g2 = 0.47).This was attributed to greater PLF values in theoccipital electrodes when compared with the fronto-central electrodes. The two sets of electrodes,however, were highly correlated in all WM loads inboth encoding [WM load 1 (r = 0.59, P < 0.001),2 (r = 0.61, P < 0.001) and 3 (r = 0.76, P <0.001)] and retrieval [WM load 1 (r = 0.58, P <0.001), 2 (r = 0.62, P < 0.001) and 3 (r = 0.57,P <0.001)]. There was no interaction betweenWMload and group.

Phase-locking factor: beta band range. In the16–28 Hz range (Fig. 3b), the main effect of groupwas significant in both encoding (F1,38 = 0.6.962,P = 0.02; partial g2 = 0.14) and retrieval(F1,38 = 4.491, P = 0.04; partial g2 = 0.1). Thiswas attributed to higher PLF values in healthycontrols relative to patients. The factor of electroderegion was significant during encoding (F1,38 =30.616, P < 0.001; partial g2 = 0.45) and retrieval(F1,38 = 4.407, P = 0.04; partial g2 = 0.10). Thiswas attributed to higher values in the occipitalregion relative to the fronto-central region. The twosets of electrodes were correlated in encoding [WMload 1 (r = 0.62, P < 0.001), 2 (r = 0.58, P <0.001) and 3 (r = 0.34, P = 0.03)] and in retrieval(WM load 2 (r = 0.36, P = 0.02) and 3 (r = 0.44,P < 0.01), but not WM load 1). There was nomain effect of WM load nor did it interact withgroup.

Phase-locking factor: gamma band range. In the30–50 Hz range (Fig. 3c), the patient group hadsignificantly lower PLF values during encoding only(F1,38 = 6.952.276, P = 0.01; partial g2 = 0.16).There was nomain effect of region orWM load, nordidWM load interact with group. The two electroderegions were correlated during encoding (at trend inWM load 1 (r = 0.29, P = 0.07) and significantlyat WM load 3 (r = 0.62, P < 0.01) and retrieval

[WM load 1 (r = 0.63, P < 0.001), load 2(r = 0.36, P = 0.02) and 3 (r = 0.54, P < 0.01)].

Signal power

Table 2 presents a summary of the resultsconcerning signal power.The only significant effects of interest occurred in

the 5–8 Hz range (Fig. 3d). The controls had signif-icantly greater signal power than the patientsin encoding (F1,38 = 6.611, P = 0.01; partialg2 = 0.15) and retrieval (F1,38 = 7.959, P < 0.01;partial g2 = 0.17). The main effect of region wasalso significant because of the greater activation inthe occipital electrodes when compared with thefronto-central ones (F1,38 = 9.493, P < 0.01; par-tial g2 = 0.2 and F1,38 = 9.901, P < 0.01; partialg2 = 0.21 for encoding and retrieval respectively).This effect of region was significantly more pro-nounced in the controls relative to the patients(F1,38 = 4.290,P = 0.045; partial g2 = 0.1) duringencoding. The occipital and fronto-central elec-trodes were nonetheless significantly correlated inencoding [WMload1 (r = 0.58,P < 0.001), 2 (r =0.58, P < 0.001) and 3 (r = 0.67, P < 0.001)] andretrieval [WM load 1 (r = 0.78, P < 0.001), 2(r = 0.78, P < 0.001) and 3 (r = 0.87, P <0.001)]. WM load exerted no main effect and didnot interact with the other factors.

Effects of symptom severity, medication and chronicity

There were no significant covariates in the ERPanalysis. Symptom severity and duration of illnesswere not a significant confounding factor in any ofthe analyses.Defined daily dose was a significant covariate in

the 5–8 Hz band signal power analysis in retrieval(F1,16 = 6.860, P = 0.02) but not in encoding.The effect was attributed to a decrease in powerwith increasing medication dose.

Discussion

In this study, we sought to clarify the electrophys-iological patterns that underlie early visual infor-mation processing abnormalities in schizophrenia.In a sample of medicated patients with chronicschizophrenia, we found that the widely replicatedreductions in the P1 visual evoked potential couldbe associated with reduction in the PLF values oftheta, beta and gamma neural oscillations, aswell as reductions in the signal power of thetaoscillations. Importantly, these oscillatory abnor-malities were confined to a fronto-occipitalnetwork that is similar to the one we described in

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non-medicated high schizotypy individuals usingidentical experiment settings (66).

ERP results

Reduction in the amplitude of the early P1 visualevoked potential is a well-replicated finding inschizophrenia (35, 67, 68). This abnormality hasbeen shown to be unaffected by disease chronicityor medication dosage (35) and was demonstratedin a sample of non-medicated first-episode patients(68). Diminished P1 was also reported both inunaffected relatives of patients with schizophrenia(36) and in schizotypal individuals (69). Thisevidence argues that the reduction in P1 amplitudeis a trait biomarker of schizophrenia: it is associ-ated with the vulnerability to the disease and notthe overt clinical phenotype. In support of this, wedid not find a correlation between symptomseverity, disease duration or neuroleptic dosageand the extent of P1 amplitude reduction.The literature concerning N1 in schizophrenia is

so far inconclusive with some but not all studiesreporting normal amplitude (37, 70–72). We foundthat the schizophrenia group had reduced N1, aneffect which reached significance only with respectto the retrieval conditions. On the basis of datashowing that N1 represents discriminatory visualprocesses (73), it could be argued that the require-ments for object discrimination in the retrievalcondition were higher than in the encoding condi-tion, a demand that could not be sustained by thepatients with schizophrenia. From this interpreta-tion it would follow that that N1 impairment inschizophrenia can be elicited preferentially bycognitively demanding paradigms. This interpreta-tion fits with the available literature, as thenegative (70–72) and positive (37) studies haveused purely perceptual and cognitively demandingtasks respectively.In summary, the current results lend further

support to the usefulness of early visual ERPs, andP1 in particular, in detecting vulnerability to theschizophrenia spectrum disorders. In addition,these biomarkers are easily transferable to animalmodels, which could aid drug development (74,75). However, it should be noted that their role inaddressing the diagnostic and classification issuesof schizophrenia may be limited by its lack ofspecificity (47, 50).

Oscillatory results

The finding of oscillatory abnormalities in patientswith schizophrenia is also in line with the existingevidence. Studies commonly report reductions in

evoked oscillations PLF indicating higher variabil-ity of the neuronal response [coined �cortical noise�(76)]. Abnormal evoked signal power is also afrequent finding but some authors find increases inpower, while others report diminished values (46).This raises suggestions that while abnormalities inboth measures may reflect the proposed state ofneuronal dysconnectivity in schizophrenia, withPLF being the more consistent measure. Thefindings from our study support this conclusion:PLF reductions were evident across the theta, betaand gamma bands, while signal power was dimin-ished in the theta band. Perhaps, PLF is the moresensitive measure with respect to activity in thehigher-frequency bands where the magnitude ofthe signal is not strong enough to produce reliableeffects in signal power. Similarly to P1, theoscillatory abnormalities appear to be state inde-pendent, as both our study and previous research(46) failed to establish significant effect of medica-tion, duration of disease or state. Instead, theoscillatory deficits are likely to represent a vulner-ability trait for schizophrenia. In demonstration ofthis, one study reported evoked PLF reductions inultra-high-risk individuals (44), a finding we repli-cated in a study in high schizotypy (66).In conclusion, the evoked oscillatory abnormal-

ities appear to be sensitive in probing vulnerabilityto schizophrenia. Their advantage over ERPs isthat they provide information not only regardingthe strength of the neural response but also itsvariability (PLF). This may be particularly relevantwhen the probed abnormality is not the result offrank neuronal damage but inefficiency in sustain-ing neuronal networks. The data on the specificityof oscillatory abnormalities to schizophrenia spec-trum disorders are so far limited with someevidence for disruptions in bipolar disorder (48,49). However, given the proposed central role ofneural oscillations in coordinating brain activity(77), it appears unlikely that oscillatory abnormal-ities will be restricted to schizophrenia and will alsofeature in other conditions that involve disruptionof normal neural function. Defining relationshipsbetween cognitive and neural oscillatory deficitsmay therefore be more appropriate to define andsegregate schizophrenia.

Top-down dysfunction pattern

A finding that furthered the case of a dysconnec-tivity phenomenon in our patient group was thatthe oscillatory abnormalities were confined to twosets of correlated fronto-central and occipital elec-trodes. In controls, perception of stimuli leads toincrease of theta, beta and gamma band PLF and

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theta power in these two regions. In patients thesame pattern was evident, but the degree ofactivation was significantly lower than in controls.Importantly, this replicates the disturbance weobserved in a high schizotypy sample where wefound decreased PLF in the same frequency bandsand in the same two sets of electrodes. This set offindings is in agreement with two studies reportingaltered oscillatory activity in both posterior andhigher cortical regions during perception in patientsand at-risk individuals (44, 78). These results arguethat under normal conditions a network connectingsensory and higher-order cortical structures isactive in the earliest stages of perception. Extensiveresearch has highlighted that at least a part of theseconnections underpin top-down informationexchange (54). Top-down modulation biases thesensory cortex to preferentially process behaviour-ally relevant data. This control mechanismmight beparticularly enhanced in tasks such as ours, wherethe interstimulus interval is constant and theparticipant anticipates the appearance of theimage. Therefore, the findings in our patient andhigh schizotypy samples can be interpreted asevidence for disturbed top-down regulation ofperception across the schizophrenia spectrum.Such top-down regulation deficit could be at theheart of the schizophrenia pathophysiology – aris-ing from long-range and local dysconnectivity andmanifesting as basic perceptual and electrophysio-logical deficits (such as P1 amplitude reduction).The case for top-down dysfunction in schizo-

phrenia has already been supported by direct andindirect evidence (44, 55, 56). First, a MEG studyreported decreased alpha band PLF and power in aparieto-occipital network in both patients and at-risk individuals when compared with controls (44).The degree of PLF abnormality was higher inpatients than in the ultra-high-risk group, whichwas interpreted as an indication that the extent oftop-down dysfunction reflects the vulnerability toschizophrenia. Also, Dima et al. (55, 56) used bothERP and fMRI techniques to directly demonstratethat perception in patients is less restrained by top-down modulation than in controls. Finally, in a setof behavioural experiments, Dakin et al. (79) haveshown that patients are less responsive to anoptical illusion that depends on global featureintegration. Therefore, these data indicate that top-down abnormalities might be a highly replicable inthe schizophrenia spectrum. Narrowing down ofthe temporal and spatial characteristics of thisphenomenon could identify a pattern specific to thecondition and may in addition provide a theoret-ical framework to understand the pathogenesis ofcognitive abnormalities in schizophrenia.

Limitations

One limitation of this study is that it does notdirectly test the specificity to the schizophreniaspectrum of the oscillatory and top-down abnor-malities by including a group from a differentdiagnostic entity (e.g. bipolar disorder). Findingsof reduced P1 in patients with bipolar disorder (47)and frontal cortex lesions (50) suggest that earlyvisual processing deficits may be a shared abnor-mality across mental health and neurologicaldisorder. The aetiology of these perceptualabnormalities, however, may differ between disor-ders, as others have argued earlier on the basis ofpsychophysical experiments (80, 81).It should also be noted that despite the consid-

erable evidence suggesting an early dysfunctionaltop-down network in the schizophrenia spectrum,our finding of correlated fronto-central occipitalactivity could equally represent the same responsethat is volume conducted rather than the activationof separate neural populations. This limitation isinherent to voltage data subjected to laplaciantransformation. Also, the effects could be explainedby a bottom-up deficit consistent with the cascademodel of perception (82). It has been argued that theearly visual processing deficits in schizophrenia maybe due to a breakdown in the basic perceptualprocesses at the level of primary sensory cortex (83).According to this theory, a deficit localized to one ofthe visual pathways (magnocellular) and the corre-sponding dorsal visual stream drives higher-ordercognitive deficits through inadequate informationencoding (34, 70, 84), but see also Skottun et al.(85–87) for a challenge of the model. The currentanalysis cannot exclude the possibility of a bottom-up genesis of the observed deficits and future studiescould include a more in-depth independent compo-nent analysis or dynamic causal modelling to makethis distinction (88).It should also be noted that most of the patients

in our study were treated with clozapine which isused primarily in chronic, treatment-resistant formof schizophrenia (89). While it could be argued thatthe results are specific to a subset of chronically illpatients, we find that the characteristics of thestudy group are especially valuable in the contextof the research question. Namely, we hypothesizedthat visual processing deficits are trait functionalbiomarkers of schizophrenia and therefore shouldbe uniform across the disease spectrum. Havingdemonstrated them in a non-clinical sample of highschizotypy individuals, the hypothesis would bemost appropriately addressed by testing a groupfrom the severe end of the schizophrenia spectrum.We therefore argue that the demonstration of

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similarities between these two groups and the lackof significant covariate effects (dose, disease dura-tion and BPRS score) make a stronger case for thetrait status of these deficits in comparison with adesign that would have involved less severe cases ofschizophrenia.A potential problem is that we included both

schizophrenia and schizoaffective patients giventhe evidence of difference in cognitive performancebetween these two groups (90). Our reasoning forthis was that when exploring potential traitmarkers of psychosis, one would expect the deficitto be demonstrable in both affective and non-affective psychosis. Furthermore, a meta-analysisshowed that while there is some evidence of worsecognitive performance in patients with schizophre-nia, the difference between the two groups is smallwith heterogeneous effect sizes (91).In conclusion, this study provides evidence that

the widely replicated P1 potential abnormality inschizophrenia is associated with evoked oscillatoryabnormalities that are consistent with a dysfunc-tional top-down network. The lack of consistentrelationship between these abnormalities and stateindicators (symptom severity, disease duration andmedication dose) along with similar findings inschizotypal individuals argues that early informa-tion processing abnormalities are part of the coreschizophrenia deficit. More research into theirspecificity and relationship to the formation ofexecutive function deficits can clarify their impor-tance as biological correlates of the cognitivesymptom domain in schizophrenia.

Acknowledgements

The Wellcome Trust Clinical Research Facility and theManchester Biomedical Research Centre are acknowledgedfor the facility and administration support for the study. Theauthors would like to thank Dr Richard Drake, Dr ArpanDutta and Stuart Walsh for their help with patient recruitment.

Declaration of interest

Ivan Koychev has been awarded a Manchester Strategic PhDStudentship, sponsored by the University of Manchester andP1vital Ltd. John Francis William Deakin has carried out paidconsultancy work and speaking engagements for Servier,Merck Sharpe and Dohme, AstraZeneca, Janssen and EliLilly. The fees are paid to the University of Manchester toreimburse them for the time taken. John Francis WilliamDeakin owns shares in P1vital Ltd.

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