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Journal of Integrative Neuroscience, Vol. 8, No. 2 (2009) 175–202c© Imperial College Press

Research Report

AN fMRI EXAMINATION OF VISUAL INTEGRATIONIN SCHIZOPHRENIA

STEVEN M. SILVERSTEIN∗,†,¶, SARAH BERTEN∗,†, BRIAN ESSEX†,ILONA KOVACS‡, TERESA SUSMARAS§ and DEBORAH M. LITTLE†,§

∗University of Medicine and Dentistry of New Jersey — UniversityBehavioral HealthCare and Department of Psychiatry

Robert Wood Johnson Medical School†Department of Psychiatry, University of Illinois at Chicago

‡Department of Cognitive ScienceBudapest University of Technology and Economics

§Departments of Neurology and RehabilitationAnatomy and Cell Biology, Ophthalmology and Visual Sciences

and Psychology, University of Illinois at Chicago¶[email protected]

Received 23 February 2009Accepted 2 April 2009

Behavioral and electrophysiological studies of schizophrenia have consistently demon-strated impairments in the integration of visual features into unified perceptual repre-sentations. Specific brain regions involved in this dysfunction, however, remain to beclarified. This study used functional Magnetic Resonance Imaging (fMRI) to examinethe relative involvement of visual cortex areas (involved in form perception) and pari-etal and frontal regions (involved in attention), in the visual integration impairment inschizophrenia. Fourteen patients with schizophrenia and 14 healthy controls were com-pared on behavioral performance and data acquired via fMRI while completing a contourintegration task that had previously been used to identify a visual integration deficit inschizophrenia. The schizophrenia patients demonstrated poorer visual integration thancontrols. Analyses of peak signal change indicated that while the groups were equivalentin area V1, the schizophrenia group demonstrated reduced signal in areas V2–V4, whichare the earliest regions sensitive to global configurations of stimuli. Moreover, whereasthe control group demonstrated greater recruitment of prefrontal and parietal areas dur-ing perception of integrated forms compared to random stimuli, the schizophrenia groupdemonstrated greater recruitment of frontal regions during perception of random stimuli.The two groups differed on brain regions involved in form perception even when they werematched on accuracy levels. The visual integration disturbance in schizophrenia involvesboth deficient basic visual processes (beginning as early as occipital region V2), as well asreduced feedback from visual attention regions that normally serves to amplify relevantvisual representations relative to irrelevant information.

Keywords: Schizophrenia; fMRI; perception; cognition; vision; occipital lobe.

¶Corresponding author.

175

176 Silverstein et al.

1. Introduction

A consistent finding in the literature on cognitive-perceptual impairments inschizophrenia is that of a reduced ability to integrate stimulus elements into coher-ent visual representations. While patients with schizophrenia do appear to be ableto process continuous contour, whether real [7] or illusory [19, 61, 62], visual bindinghas been shown to be deficient when noncontiguous elements need to be integratedinto a perceptual whole [e.g., 11, 48, 58, 59]. Over the past 30 years, at least 30studies have demonstrated such reductions in visual feature integration abilities inschizophrenia [69]. Moreover, in 10 of these studies [14, 22, 43, 46, 48–50, 58, 65,72], the reduced ability to integrate information, and subsequent reduced influenceof visual context, led to superior performance, compared to controls, in makingdecisions about individual features. Therefore, evidence for impairments in visualintegration has been convincingly demonstrated independent of a generalized deficit[6, 28, 29, 55]. These impairments also cannot be accounted for by medication, asthey have been demonstrated in unmedicated patients [20]. This extensive experi-mental literature is consistent with earlier clinical descriptions and first-hand patientaccounts of fragmented perception in schizophrenia [4, 64, 68]. The importance ofvisual integration disturbances in schizophrenia is reflected in the inclusion of thiscognitive domain as a core construct in the recent NIMH-sponsored Cognitive Neu-roscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS)initiative [3, 5, 23].

In contrast to the wealth of behavioral studies of visual integration dysfunctionin schizophrenia, there have been relatively few studies of its underlying neurophys-iology. Electrophysiological studies of nonpatients have generally identified that thelateral occipital cortex (LOC) plays a role in visual binding, as part of a network alsoinvolving the prefrontal cortex [52, 53]. Electrophysiological studies of schizophrenia[11, 12, 18] identified both ventral and dorsal stream abnormalities associated withreduced P1 amplitude when patients viewed fragmented pictures. A recent studyfound reduced N150 amplitude to global fragmented targets in a global–local taskin schizophrenia [26], and the source of that waveform has been localized to V3/V3awithin the LOC [10, 26, 70]. Functional magnetic resonance imaging (fMRI) studiesof visual integration in nonpatients have localized regions subserving intact integra-tion, including both earlier visual areas (e.g., V2), and higher visual areas involvedin shape processing (e.g., V3, V4, the LOC and posterior fusiform regions) [1, 2, 30,38, 42, 52]. Foxe et al. [19] found intact illusory contour processing in schizophrenia,and identified excessive frontal lobe activity during contour processing, which wasinterpreted as a compensation for impaired ventral stream processing. To date, how-ever, fMRI has not been used to examine integration of spatially separated elementsinto unified perceptual wholes in schizophrenia.

Therefore, in this study, we examined integration of visual features into formsusing a psychophysically rigorous contour integration task [31–37, 44, 57, 59] that haspreviously demonstrated sensitivity to both integration deficits and top–down effects

Visual Integration in Schizophrenia 177

Fig. 1. Samples of images from the 2 alternative forced choice (2AFC) contour integration task.Top left: 0◦ jitter, top right: 7–8◦ jitter, center left: 11–12◦ jitter, center right: 15–16◦ jitter, bottomleft: 19–20◦ jitter, bottom right: 23–24◦ jitter. These panels show only the region of the displaycontaining the contour. The actual stimuli contain approximately 75% additional space that containsonly noise elements.

on these in schizophrenia [37, 57, 59]. The task involves the detection of a roughlycircular shape whose contour is comprised of unconnected elements embedded withina background of similar, but randomly placed elements (see Fig. 1). Each elementis an example of a Gabor signal — a Gaussian-modulated, sinusoidal luminancedistribution that reflects the center-surrounded properties of orientation-selectivespatial frequency (feature) detectors in primary visual cortex (V1). Perception ofthe circular contour in this task requires the linking of individual Gabor elementsinto an emergent circular shape [36], which requires context-sensitive interactionsamong neurons in V1 that code the orientation-correlated contour elements [13, 15].This is thought to involve feedback from higher visual areas, such as V2, V3, andV4, where visual form information is initially processed [54]. The embedded contourcannot be detected simply by analysis of individual Gabor elements, or by neuronswith large receptive fields corresponding to the size of the contour [8, 24].

The purpose of this study was to use fMRI to investigate abnormal visual inte-gration in schizophrenia, and in particular, to clarify the contributions of activationchanges in primary visual cortex, in higher visual areas, and outside of the occipi-tal lobe. Our hypothesis was that the schizophrenia group would demonstrate both


poorer behavioral performance (accuracy and reaction time) on the task comparedto controls, and differences in activation of brain regions associated with visual pro-cessing of stimulus configurations. Specifically, if the schizophrenia-related visualintegration impairment is not secondary to more basic impairments, this would bereflected in abnormal levels of activation in visual cortex regions sensitive to stim-ulus grouping (e.g., V2–V4), but not in abnormally high or low levels of activityin area V1, where basic feature processing occurs. In addition, we predicted thatlevels of prefrontal and parietal activation would be reduced compared to controls,demonstrating reduced top–down feedback to, or attentional enhancement of stim-ulus configurations — even in conditions where patients achieved adequate contourintegration.

2. Methods

2.1. Subjects

The sample consisted of 14 outpatients with schizophrenia (9 men) and 14 healthycontrol subjects (5 men). Patient diagnosis was based on the Structured ClinicalInterview for DSM-IV Diagnosis-Patient Version (SCID) [16] which was conductedby the first author or a research assistant who had previously achieved a level ofreliability with the first author across SCID items of κ > 0.75. Inclusion criteria forthe patient group were a diagnosis of schizophrenia, age 18–55, and an ability tosuccessfully complete a practice version of the task prior to the scanning session.Exclusion criteria included active substance abuse or dependence, a current mooddisorder, a history of a neurological disorder or head injury with loss of conscious-ness lasting more than 10 minutes, or documented intellectual impairment. Controlsubjects were not screened for personal or familial psychopathology. However, con-trols were screened for present or past neurological and substance abuse conditionsduring a pre-scan interview.

The groups were matched on age and parental education levels. All subjectscompleted a practice version of the task, and were familiarized with the scanningenvironment (using a mock scanner) the day before the fMRI session. At that ses-sion, all patient subjects were also interviewed using the SCID and the Positive andNegative Syndrome Scale (PANSS) [27, 39]. No potential subjects failed the screen-ing version of the contour integration task. Medication level was assessed for patientsusing published conversion formulas [73] to generate chlorpromazine equivalent dailydoses for second-generation antipsychotic medications. All subjects provided writteninformed consent. Subjects were asked to refrain from alcohol for 24 hours prior toall fMRI sessions and abstain from smoking and caffeine for 12 hours prior to thescan. Each subject was then questioned for the use of these substances prior to thescan. One of the patients and two of the controls reported use of caffeine within6 hours of the MRI scan. Additionally, three patients reported use of nicotine within6 hours of the MRI.


2.2. Contour integration task

The stimuli consisted of a closed chain of Gabor elements forming a left- or right-pointing egg-like shape within a background of randomly oriented Gabor elements.The spacing between the contour elements was kept constant at eight times the wave-length of a single element. The ratio of average adjacent background element spacingto contour element spacing, or signal-to-noise ratio, in each image was 0.9 — a levelthat requires long-range, horizontal, excitatory interactions between the neuronscoding the correlated orientations of the contour elements for intact contour per-ception [13, 36]. Task difficulty was manipulated across 6 conditions, by randomlyjittering the orientation of the contour elements + or − 0◦, 7–8◦, 11–12◦, 15–16◦,19–20◦, or 23–24◦ (see Fig. 1). As contour element orientation deviates from 0◦ tan-gent to its original position, the correlations between orientations of adjacent ele-ments are reduced, and ability to perceive the circular shape is reduced. The abilityof the visual system to rapidly form the long-range, excitatory, context-dependentcircuits that allow for visual integration can therefore be probed by examining behav-ioral performance across these conditions, and specifically, examining the thresholdat which performance reaches a level mid-way between chance and perfect perfor-mance (75%). A reduced ability to activate the neural circuitry involved in visualintegration, as is predicted to characterize schizophrenia, would result in a lowerjitter value at threshold and reduced activation in cortical areas involved in theformation and processing of configurations of elements [1].

A set of 40 images, evenly divided between left- and right-pointing stimuli, waspresented in each block at each of the six difficulty levels. A new shape and back-ground were generated for each card, but all of the contours had the same generalsize and shape. There were 240 images in total. For all images, the Gabor carrierspatial frequency was 5 cycles per degree, and contrast was approximately 95%.

The blocks of images were presented in increasing order of orientation jitter.For each trial, the circular contour was placed horizontally with its narrower partpointing either to the right or to the left. The number of the images pointing to theright or to the left was equal within each block of 40 images, although the orderof presentation of left and right stimuli was randomized within the different blocks.The contour was always positioned centrally around a fixation point that appearedin the center of a solid gray display during the one-second interstimulus interval(ISI). The duration of the stimulus presentation was 2 seconds. The subjects hadto make a decision within that time frame. We used a two-alternative forced-choice(2AFC) method. The subject’s task was to indicate, by a two-button box, which sideof the screen the narrower part of the shape was pointing to. Prior to the first jittercondition and then following each condition a period requiring central fixation waspresented for 30 seconds. The subjects were tested binocularly inside the scanner.Screen size was 8.42 cm high by 17.75 cm wide. The screen was approximately 16 cmfrom the bridge of the nose. Stimuli (contour and noise) subtended 14 visual anglehorizontally and 11.3 vertically. The portion of the stimulus that contained the


contour subtended 5.9 horizontally and 4.68 vertically at its center (with a graduallydecreasing visual angle approaching the point).

The stimuli were displayed in white/gray/black (see Fig. 1) (56 cd/m2) on a graybackground (22 cd/m2). The program to administer the task and record data wereimplemented in Flash and synchronized with image acquisition. Because stimuliwere blocked by condition, and to keep the session as brief as possible, a blockdesign, rather than an event-related design, was used. Although the latter may bemore sensitive to the nature of single-trial data, its typical requirement for longerintertrial intervals also raises the risk of confounding effects of reductions in sustainedattention, which is an issue in schizophrenia. Also, as this was the initial fMRI studyof contour integration in schizophrenia, we wanted to use the same version of thetask, with the same timing parameters that had been used in our earlier behavioralstudy demonstrating sensitivity of this task [37].

2.3. Brain imaging

All images were acquired using a 3.0-Tesla whole body scanner (Signa VHi, GeneralElectric Medical Systems, Waukesha, WI) performing serial gradient echo, echo-planar imaging (epiRT, plane = axial, TR = 3000 ms, TE = 30.7 ms, flip angle =90◦, matrix = 64 × 64, FOV = 20 cm2, voxel size = 3.125 × 3.125 × 5 mm, slicethickness = 4mm, gap = 1 mm, NEX = 1, bandwidth = 62 kHz) for all imageacquisition. The duration of the acquisition was 15.5 minutes during which 28 sliceswere acquired per volume with a total of 310 brain volumes. The functional paradigmwas then followed by acquisition of a high resolution 3D inversion recovery fastspoiled gradient recalled echo sequence (SPGR, plane = axial, TR = 9 ms, TE =2.0 ms, flip angle = 25◦, NEX = 1, bandwidth = 15.6 kHz, acquisition matrix =256×256, FOV = 22×16.5 cm2, slice thickness/gap = 1.5/0 mm/mm, slices = 124).

The contour integration task was presented in the scanner and coordinated withbehavioral measurements by a custom designed MRI synchronization control system.All scan sessions were conducted between 9 AM and 12 noon. Prior to each sessionof data collection, quality assurance protocols were conducted to examine scannerquality in terms of signal stability, level of ghosting, and signal-to-noise ratio. Qualityassurance on the projection equipment and visor used for stimulus display wereconducted at the beginning of each week to ensure stability of stimulus contrastacross the study. No differences in either quality assurance protocol were notedacross the study duration.

2.4. Image preprocessing and analysis

fMRI analysis was conducted using Statistical Parametric Mapping (SPM2) [71].Data from each individual subject were initially corrected for head motion andnone exceeded 3 mm of in-plane motion. The functional data were then coregisteredwith the corresponding anatomical images which were then spatially normalizedto the Montreal Neurological Institute (MNI) template. The normalized functional


data were smoothed with a 6 mm Gaussian smoothing kernel which was approxi-mately two times the original voxel dimensions. The preprocessed functional datafor each individual were then analyzed with a general linear model using 6 experi-mental regressors corresponding to each degree of jitter (0◦, 7◦, 11◦, 15◦, 19◦, 23◦).The onset times of these regressors were convolved with the hemodynamic responsefunction (HDF). Random effects analyses directly comparing the two subject groupswere then conducted on the activation maps for the 28 subjects so that a group acti-vation map could be extracted to identify significant effects across regions for eachappropriate statistical contrast. To correct for multiple comparisons on the imagedata, a false discovery rate (FDR), q, of 0.05 was applied to all contrasts [21]. Theapplication of an FDR controls the proportion of false positives only among thosevoxels that exceed the statistical threshold and therefore reduces the likelihood ofType I errors [17]. A cluster threshold requiring a suprathreshold volume of 15 mm3

was applied after the FDR to further reduce the likelihood of Type 1 errors [17].Analysis of functional data was accomplished in two phases. First, voxel-wise

comparisons were made between all jitter conditions and between the control andpatient groups. These comparisons were used to identify the networks that wereactivated, and to examine any gross changes between conditions and between groupsin clusters of activation. However, as one primary hypothesis involves the modulationof signal in the visual cortical regions, we also carried out a directed region of interestanalysis on the individual subject data to extract peak signal changes for eachsubject. As differentiation between primary, secondary, and tertiary visual corticalregions is not clearly defined by anatomical markers, the Wake Forest UniversityPick Atlas Tool was used [40]. Within these regions of interest, peak signal changefor each subject was extracted for those voxels which were significantly activated onthe group maps. In other words, a cluster was defined as the voxels which exceedthe statistical threshold on the random effects analysis for each group of subjects.This cluster was then used as a mask and applied to the smoothed, normalized datafrom each subject. Peak values (beta weights) and average signal change (from thenormalized, smoothed, time course) were extracted from each voxel identified bythe mask from each subject. Visual inspection of the region of interest relative tothe SPGR was carried out on each individual subject to ensure that the bordersof primary and secondary visual cortices occurred along the calcarine fissure to thecuneus and lingual gyrus [9] and that tertiary visual cortex was defined from theborders of V1/V2 to the middle occipital gyrus [9].

3. Results

3.1. Demographic data

The patient and control groups did not differ significantly in age [schizophrenia =32.62 (8.92), control = 30.07 (6.47), t(25)= 0.85, p > 0.40], education level [schizo-phrenia = 14.27 (3.30), control = 15.43 (3.46), t(25) = −0.89, p > 0.38], maternal


education [schizophrenia = 13.36 (2.90), control = 13.21 (2.00), t(25)= 0.26, p > 0.79],or paternal education [schizophrenia = 13.54 (2.60), control = 14.86 (2.45), t(25)=−1.36, p > 0.18]. There was a trend towards a group difference on gender composition[χ2(1)= 3.59, p = 0.06].

3.2. Clinical variables

Thirteen of the 14 patient subjects were taking antipsychotic medication. Forthese patients, the mean daily dose (in CPZ equivalents) was 353.85 mg (SD = 193,minimum = 100, maximum = 700). Mean symptom ratings on the five PANSS fac-tors were as follows: Positive = 2.92 (mild), Negative =2.63 (minimal to mild),Cognitive = 2.08 (minimal), Excitement = 2.04 (minimal), and Depression = 2.65(minimal to mild).

3.3. Performance

Overall, both groups of subjects showed reduced accuracy [controls: F (5, 65)= 69.14,p < 0.001; patients: F (5, 60)= 39.56, p < 0.0001] and increased latency [controls:F (5, 65)= 43.95, p < 0.001; patients: F (5, 60)= 3.59, p = 0.001] as the degree of jit-ter increased. However, as can be seen in Fig. 2A, there was no interaction betweendegree of jitter and group membership in accuracy of response [F (5, 125)= 1.083,p > 0.37] although controls performed significantly better than patients overall [con-trol mean = 73.4%, SEM= 1.9; patient mean =67.0%, SEM= 2.0; F (1, 25)= 5.378,p < 0.05]. Although the patients did not differ from controls on the easiest or mostdifficult conditions (where ceiling and floor effects, respectively, are expected) [0◦:t(26)= 0.92, p > 0.37; 7◦: t(26)= 1.32, p > 0.20; 23◦: t(26)= 0.16, p > 0.88], theydemonstrated a consistent level of relatively reduced accuracy in the more discrim-inating, intermediate jitter conditions [11◦: t(26)= 2.53, p < 0.05; 15◦: t(26)= 2.22,p < 0.05; 19◦: t(26)= 2.16, p < 0.05].

As is seen in Fig. 2B, there was a group × condition interaction for response laten-cies [F (5, 125)= 5.19, p < 0.001]. Both groups showed an overall increase in latencyas degree of jitter increased, with a differential increase for controls [F (5, 65)= 43.95,p < 0.001] relative to patients [F (5, 60)= 3.59, p = 0.01]. As jitter increased and accu-racy was reduced, the controls showed a systematic increase in reaction time (RT).Although the patients also showed reduced accuracy with increased jitter, they didnot show a corresponding systematic increase in latency. There was a trend for con-trols to show increased latency relative to patients on the 3 largest degree of jitterconditions (15◦, 19◦, 23◦), however, the only significant difference was observed inthe 19◦ jitter condition [t(26)= 1.71, p = 0.05].

Effects of subject gender were examined on task performance (accuracy andresponse latency) for each group of subjects independently as well as across bothgroups (2-way ANOVA with gender and subject group as between-subjects factors).None of the results were statistically significant. For example, for the sample as


Fig. 2. Accuracy (top, A) and reaction time (bottom panel, B) are presented for patients (whitecircles) and controls (black circles) for each degree of jitter (x-axis). Threshold refers to the pointmidway between perfect and chance-level performance, and is the point of most reliable discrimi-nation between individuals and groups.

a whole, the between-subjects analyses involving gender produced the followingresults: 0◦ condition: F (1, 26)= 0.009, p = 0.93; 7◦ condition: F (1, 26)= 0.122, p =0.73; 11◦ condition: F (1, 26)= 0.18, p = 0.68; 15◦ condition: F (1, 26)= 0.66, p = 0.42;19◦ condition: F (1, 26)= 0.001, p =0.99; 23◦ condition: F (1, 26)= 0.24, p = 0.628.Although we are underpowered to detect a difference on behavioral performance ifone does exist, the high p-values suggest that the lack of significant findings mayrepresent a true nondifference between males and females on this specific task.

3.4. Baseline networks

Activation maps for data collected during the 0◦ condition (i.e., 0◦ compared tocentral fixation) are presented in Fig. 3 (detailed coordinates of peak activation fromeach region are presented in Table 1). Relative to periods of central fixation, bothcontrols (Fig. 3A) and patients (Fig. 3B) showed a widespread network of activationduring the forced choice task that included the left and right frontal eye fields (middlefrontal gyri), left and right superior and inferior parietal lobules, superior, middle,


Fig. 3. Activation maps for the 0◦ jitter condition relative to central fixation for controls (A),patients (B). Regions that were differentially greater in controls relative to patients (C) and patientsgreater than controls (D) are also presented. Significant voxels are presented corrected for multiplecomparisons with a p-valueFDR < 0.05.

and inferior temporal gyri, visual cortices (primary, secondary, tertiary) and regionswithin the cerebellum. The random effects analyses comparing patients to controlsare presented in Figs. 3C and 3D. Controls showed differentially greater activationthan patients bilaterially in regions identified as dorsolateral prefrontal cortex, rightventrolateral prefrontal cortex, left and right frontal eye fields, left and right superiorparietal lobules, V3, and in right V1 (Fig. 3C). Additionally, in this contrast a clusterof activation was also identified in the anterior cingulate. Examination of the timecourse in the anterior cingulate confirmed that this activation was not the resultof increased activity in the controls but instead of decreased signal in the patients.In comparison, patients showed increased activity in right superior temporal gyrus,head of the left caudate nucleus, and left middle temporal gyrus (Fig. 3D).

As a further test of potential gender effects, we examined the effects of genderon activation maps within each jitter condition relative to fixation. Even when weremoved the correction for multiple comparisons and set the p-value to 0.05, therewere no significant clusters of activation between the gender groups. We also re-ranthis analysis without a cluster threshold and without a correction for multiple com-parisons. At this threshold, significant voxels were identified within V2 on the right,


Table

1.

Magnit

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of

pea

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tati

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)and

coord

inate

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MN

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each

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are

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for

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(V4)

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62

6.8

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Supple

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−610

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852

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5.7

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(FE

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311

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48

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213

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53

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862

65

3.8

738

763

70

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8

Super

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ht

7−2

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058

2354

4.2

1−3

1−5

966

1957

9.4

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21

−60

71

3126

5.6

918

−61

80

1672

8.0

4

Infe

rior

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ht

40

−42

−41

48

856

4.2

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7−4

343

702

8.8

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67

−26

51

1168

4.9

850

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50

576

10.2

9

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813

156∗

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1−2

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111

46

8.3

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8156∗

6.1

818

−26

635

3.9

7

Super

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Tem

pora

lG

yru

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22

−52

−26

14

36

4.2

2−4

9−1

316

60

9.9

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82

3.8

747

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56

5.3

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158

8.1

259

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646

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20

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119

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9−5

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98

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38

−56

−8387

6.1

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−38

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67

8.4

5


Table

1.

(Continued

)

(A)

Contr

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sk

Max.

MN

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Max.

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ight

17/18/19

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−88

12

2111∗

6.2

6−2

1−7

610

3526∗

5.8

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t17/18/19

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−80

16

2111∗

8.7

230

−68

12

3526∗

5.0

9

Cer

ebel

lum

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ht

∼−1

7−7

0−5

686

5.6

7−2

3−8

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9102

6.4

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21

−78

−50

123

6.1

128

−68

−47

111

6.4

2

(C)

Contr

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ts0

(D)

Patien

ts>

Contr

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ola

tera

lP

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46

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42

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7.6

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Fie

lds

(FE

F)

Rig

ht

8−5

19

49

31

5.8

8∼

∼∼

∼∼

Lef

t8

46

851

36

8.0

3∼

∼∼

∼∼

Super

ior

Pariet

alLobule

Rig

ht

7−1

6−6

656

51

6.0

1∼

∼∼

∼∼

Lef

t7

25

−57

55

113

3.1

4∼

∼∼

∼∼

Caudate

(hea

d)

Rig

ht

24

∼∼

∼∼

∼−1

29

10

36

4.7

7

Super

ior

Tem

pora

lG

yru

sR

ight

22

∼∼

∼∼

∼−4

3−1

818

31

3.1

2

Mid

dle

Tem

pora

lG

yru

sLef

t21

∼∼

∼∼

∼58

−38

651

7.3

8

VisualC

ort

exR

ight

17/18/19

−36

−61

16

23

4.9

∼∼

∼∼

∼


and within the anterior cingulate for controls, with women showing greater activationthan men. When examined in the other direction (men > women) significant voxelswere identified within the horn of the lateral ventricles. Overall, however, it appearsthat there are no meaningful gender differences on this task, and none of these reachstatistical significance when the data are corrected for multiple comparisons.

3.5. Networks subserving object recognition

To examine the networks implicated in recognition of coherent objects and the deci-sion making which follows, we contrasted activation during the 0◦ jitter conditionrelative to the (least organized) 23◦ jitter condition in controls and in patients.Because performance was near 90% in the 0◦ condition and at chance in the 23◦ con-dition for both controls and patients, contrasts between these conditions representrecognition of a coherent object relative to an unorganized stimulus field. Resultingactivation maps are presented in Fig. 4, and detailed coordinates of peak activationfrom each region are presented in Table 2. In controls, there was greater activity inthe 0◦ condition relative to the 23◦ condition involving regions in prefrontal cortex

Fig. 4. Activation maps demonstrating activity during 0◦ jitter compared to a condition in whichcontours were not visible (i.e., 23◦ jitter), in controls (A) and in patients (C) and activationmaps depicting greater activation in the 23◦ jitter condition relative to 0◦ in controls (B) andin patients (D). Replication of these findings with contrasts between the 0◦ and 19◦ conditions ispresented in the bottom panel. Significant voxels are presented corrected for multiple comparisonswith a p-valueFDR < 0.05.


Table

2.

Magnit

ude

of

pea

kact

ivity

(maxim

um

z-s

tati

stic

),co

ord

inate

sin

MN

Isp

ace

,and

corr

espondin

gB

rodm

ann

are

as

(BA

)fo

rea

chsignifi

cant

clust

erof

act

ivati

on

for

contr

ast

sbet

wee

nex

trem

ejitt

erco

ndit

ions

for

contr

ols

(A:0◦

>23◦

and

B:23◦

>0◦ )

and

pati

ents

(C:0◦

>23◦

and

D:23◦

>0◦ )

.∼

Den

ote

sno

signifi

cant

clust

ers

inth

eid

enti

fied

regio

n.

(A)

Contr

ols

0>

23

(B)

Pati

ents

0>

23

BA

MN

IC

oord

inate

sk

Max.

MN

IC

oord

inate

sk

Max.

xy

zZ

xy

zZ

Pre

fronta

lC

ort

ex(P

FC

)R

ight

∼−3

240

−2150

5.3

56

72

049

6.5

7Lef

t∼

30

54

12

96

3.9

9∼

∼∼

∼∼

Ante

rior

Cin

gula

te∼

24

12

30

23

48

8.7

5∼

∼∼

∼∼

Fro

nta

lE

ye

Fie

lds

(FE

F)

Rig

ht

8−3

19

59

60

9.1

6∼

∼∼

∼∼

Lef

t8

33

957

30

5.6

1∼

∼∼

∼∼

Caudate

Rig

ht

24

−12

816

27

5.4

1∼

∼∼

∼∼

Lef

t24

10

418

44

4.7

5∼

∼∼

∼∼

Thala

mus

Rig

ht

∼−1

4−3

24

46

7.9

7∼

∼∼

∼∼

Lef

t∼

6−3

04

35

6.6

8∼

∼∼

∼∼

Super

ior

Tem

pora

lG

yru

sR

ight

22

−51

−26

14

45

16.1

3∼

∼∼

∼∼

Lef

t22

50

−21

16

44

11.2

5∼

∼∼

∼∼

Mid

dle

Tem

pora

lG

yru

sR

ight

21

−49

−50

431

5.2

7∼

∼∼

∼∼

Lef

t21

56

−49

150

5.8

1∼

∼∼

∼∼

Infe

rior

Tem

pora

lG

yru

sR

ight

20

−46

−60

−17

57

3.8

7∼

∼∼

∼∼

Lef

t20

42

−49

−436

2.6

1∼

∼∼

∼∼

VisualC

ort

exR

ight

17/18/19

−26

−81

438

8.1

1∼

∼∼

∼∼

Lef

t17/18/19

21

−72

837

11.0

4∼

∼∼

∼∼

Fusi

form

Gyru

sR

ight

37

−32

−46

−14

39

10.4

6−2

4−4

6−1

820

3.8

1Lef

t37

52

−72

836

7.0

728

−64

−26

48

9.2

0

Para

hip

poca

mpalG

yru

sR

ight

34

∼∼

∼∼

∼−2

6−3

62

41

3.1

7Lef

t34

∼∼

∼∼

∼14

−36

824

16.2

9


Table

2.

(Continued

)

(C)

Contr

ols

23

>0

(D)

Patien

ts23

>0

BA

MN

IC

oord

inate

sk

Max.

MN

IC

oord

inate

sk

Max.

xy

zZ

xy

zZ

Orb

itofr

onta

lC

ort

exR

ight

11/12

∼∼

∼∼

∼20

48

−637

12.6

6Lef

t11/12

∼∼

∼∼

∼−1

242

−643

8.3

4

Pre

fronta

lC

ort

exR

ight

∼∼

∼∼

∼∼

−30

26

12

32

3.8

3Lef

t∼

∼∼

∼∼

∼26

26

10

46

4.7

2

Mid

dle

Tem

pora

lG

yru

sR

ight

21

∼∼

∼∼

∼−5

1−3

13

35

4.8

1Lef

t21

∼∼

∼∼

∼52

−46

435

10.7

3

VisualC

ort

exR

ight

17/18/19

−23

−77

192

6.8

2−2

3−7

211

28

9.8

1Lef

t17/18/19

23

−68

269

5.8

928

−70

11

27

10.8

3


(bilaterally), anterior cingulate, frontal eye fields, superior, middle, and inferior tem-poral gyri, posterior cingulate, V2/V3 and fusiform gyri. In contrast, only regionsin visual cortex were more active in the 23◦ condition relative to the 0◦ condition incontrols. In patients, two regions identified as left and right parahippocampal gyriand another region identified as right fusiform gyrus were differentially greater inthe 0◦ condition relative to 23◦. When the 0◦ condition was subtracted from the 23◦

condition in patients, a network which included left orbitofrontal cortex, left andright caudate (head), left and right medial prefrontal cortex, left middle temporalgyrus, and bilateral primary visual cortex was identified.

As a check on the reliability of the results from the 0◦ vs. 23◦ condition con-trast, we also compared the groups on the 0◦ vs. 19◦ contrast, and this revealed asimilar pattern of results (see Fig. 4). The controls showed greater activity in the0◦ condition in prefrontal regions, the anterior cingulate, superior and inferior pari-etal lobules, and in the superior, middle, and inferior temporal regions. In contrast,patients showed only small clusters of significant activity in the thalamus, superiortemporal gyri, and parahippocamal gyri in the 0◦ condition when contrasted withthe 19◦ condition. For controls, there was increased activity during the 19◦ condi-tion (compared to the 0◦ condition) primarily in the head of the caudate and visualregions. Similar to contrasts between the 23◦ and 0◦ conditions, patients showedincreased activity in the 19◦ (relative to 0◦) condition across a widespread of net-work including prefrontal, inferior frontal, superior, middle, and inferior temporalregions, visual cortices, and regions in the fusiform gyri.

3.6. Signal modulation in visual cortices

One primary hypothesis in the current investigation is that the signal in visualcortex regions that are maximally sensitive to basic shape features may differ inpatients relative to controls. To examine this directly we extracted peak inten-sities from regions associated with visual processing and object integration [V1,V2/V3, V4, inferotemporal cortex (IT)] in each condition (see Fig. 5). Compar-isons between peak activation within each hemisphere and degree of activation

Fig. 5. Regions of interest for extraction of the peak signal change within V1 (green), V2/V3(red), V4 (blue), and inferotemporal cortex (IT) (yellow). Peak signal intensity for controls (blackcircles) and patients (white circles) for areas involved in visual processing.


(cluster size) were conducted. None of these comparisons reached statistical signifi-cance (p > 0.05) although the comparison between hemispheres for peak activationin V2/V3 trended to show an increase in peak activity with right greater than leftfor controls [F (1, 26)= 2.96, p = 0.071]. As such, all further comparisons collapsedacross cerebral hemispheres. In primary visual cortex (V1) there was a main effectof condition [F (5, 130)= 5.80, p < 0.001]. There was no main effect of subject group[control, patient; F (1, 26)= 0.33, p > 0.57] or interaction between group and degreeof jitter [F (5, 130)= 0.38, p > 0.86]. In V2/V3, signal change also increased as degreeof jitter increased [F (5, 130)= 6.98, p < 0.001]. However, there was a main effect ofsubject group such that controls showed higher peak signal changes than patientsdid [F (1, 26)= 5.68, p = 0.025]. The degree of increased signal change in controls rel-ative to patients was consistent across all conditions of jitter, as revealed by a lack ofgroup × condition interaction [F (5, 130)= 0.38, p > 0.86]. Similar effects were foundfor V4 with main effects of subject group [F (1, 26)= 4.89, p < 0.05] and condition[F (5, 130)= 5.09, p < 0.001] but no group × condition interaction [F (5, 130)= 1.08,p > 0.72]. There were no significant effects of peak signal change for subject groupor condition for IT within the inferior temporal lobe.

3.7. Networks matched on accuracy

To ensure that the above results were not confounded by correlates of poor perfor-mance in the patient group, a final set of analysis compared the groups across threecontrasts where, on each one, they were matched on performance (see Fig. 2): (1)patients at 7◦, controls at 11◦; (2) patients at 11◦, controls at 15◦; and (3) patientsat 15◦, controls at 19◦ (see Fig. 6).

As can be seen, 6A–C controls showed greater activation (even when matchedon behavior) in the head of the caudate (Figs. 6B, 6C), both inferior and superiorparietal lobules (Figs. 6A–C), left and right putamen (Figs. 6A, B), right dorsolateralprefrontal cortex (Figs. 6A, C), and regions in V2/V3. In contrast, patients showed

Fig. 6. Contrasts between controls and patients when matched for accuracy (A: 11◦ in controlsand 7◦ in patients; B: 15◦ in controls and 11◦ in patients; C: 19◦ in controls and 15◦ in patients).Greater activity for controls (top) and greater activity for patients (bottom) is presented. Significantvoxels are presented corrected for multiple comparisons with a p-valueFDR < 0.05.


greater activation in the anterior cingulate, left dorsolateral prefrontal cortex (butmore medially than the regions of greater activity in controls), fusiform gyri, superiortemporal gyri, middle temporal gyri, and V5. Peak activation within each region andcorresponding coordinates are presented in Table 3.

3.8. Relationships between behavioral data,

medication, and symptoms

For correlational analyses, indices of visual integration were constructed from thebehavioral data by examining the differences between performance at the 0 jit-ter condition and performance in other conditions. Relationships between theseperformance-based contrast values and medication dose (in CPZ equivalents) wereexamined by correlating these values with CPZ equivalent for each subject withschizophrenia (reliable medication data could not be obtained for two patients).The r values for these correlations were: 0.03 (7◦), −0.08 (11◦), 0.15 (15◦), 0.11(19◦), and 0.20 (23◦), consistently indicating an independence of visual integrationand medication level. Correlations between symptom levels and these performanceindices were not significant.

4. Discussion

There were two major findings from the fMRI data. First, while in all cases exceptone controls and patients were equivalent in their signal change in area V1, signalstrength was consistently relatively reduced in patients in higher visual cortex areas(e.g., V2, V3, V4). Because V1 is most sensitive to individual features (as opposed toemergent holistic features), whereas areas V2–V4 are maximally sensitive to globalstimulus organization, these data suggest that the performance deficits reflect atrue weakening of visual integration processes, and are not secondary to impairedsensory registration or feature integration. This hypothesis is consistent with fMRIdata from healthy subjects, and from monkey studies, demonstrating that activationin V1 is generally not increased during perception of contours, and that even whenV1 activity is found [1], signals are typically strongest in higher tier areas such asV3 and V4, and in more anterior visual areas [42, 45]. These data are also consistentwith ERP findings of reduced N150 during a global–local task in schizophrenia [26],data localizing the N150 source to visual areas V3/V3a [10, 26, 70], and findings ofreduced occipital lobe gamma oscillations during a visual processing task, even whenbehavioral performance is equivalent to that of controls [63]. It is noteworthy thatcontrols demonstrated more activation in areas V2 and V3 even when matched topatients on accuracy level, and that patients demonstrated reduced activation evenin the 0◦ and 23◦ jitter conditions. The 0◦ condition result suggests that even whenthe contour stimulus was maximally structured, allowing for highly accurate contourperception, patients nevertheless were characterized by a weaker neural signal whencoding the stimulus. As stimulus structure was weakened by introducing orientation


Table

3.

Magnit

ude

of

pea

kact

ivity

(maxim

um

z-s

tati

stic

),co

ord

inate

sin

MN

Isp

ace

,and

corr

espondin

gB

rodm

ann

are

as

(BA

)fo

rea

chsignifi

cant

clust

erof

act

ivati

on

for

contr

ast

sbet

wee

nco

ntr

ols

and

pati

ents

matc

hed

on

beh

avio

ralper

form

ance

.∗

Den

ote

san

inability

todiff

eren

tiate

left

from

right.

Thes

enum

ber

sare

iden

tica

lbec

ause

ofth

isdiffi

culty.

∼D

enote

sno

signifi

cant

clust

ers

inth

eid

enti

fied

regio

n.

Contr

ols

11

>Patien

ts7

Pati

ents

7>

Contr

ols

11

BA

MN

IC

oord

inate

sk

Max.

MN

IC

oord

inate

sk

Max.

xy

zZ

xy

zZ

Dors

ola

tera

lP

refr

onta

lC

ort

exR

ight

46

−49

29

27

49

5.8

3−3

838

16

235

3.5

0Lef

t46

30

51

837

6.0

132

60

14

40

5.4

1

Pre

fronta

lC

ort

exR

ight

∼−3

034

44

33

5.3

3−6

58

16

105

4.2

9Lef

t∼

22

44

20

83

3.4

26

54

40

60

3.6

0

Ante

rior

Cin

gula

teR

ight

32

−16

30

10

78

4.2

7∼

∼∼

∼∼

Lef

t32

18

28

983

3.0

95

31

11

195

6.4

5

Super

ior

Pariet

alLobule

Rig

ht

7−3

4−7

832

305

3.0

5∼

∼∼

∼∼

Lef

t7

24

−72

32

662

6.5

2∼

∼∼

∼∼

Infe

rior

Pariet

alLobule

Rig

ht

40

−39

−26

41

333

9.5

6∼

∼∼

∼∼

Lef

t40

51

−29

45

82

3.8

1∼

∼∼

∼∼

Puta

men

Lef

t∼

22

412

110

5.6

3∼

∼∼

∼∼

Fusi

form

Gyru

sR

ight

37

∼∼

∼∼

∼−3

8−4

6−2

0102

5.7

6Lef

t37

∼∼

∼∼

∼38

−56

4230

4.5

0

Mid

dle

Tem

pora

lG

yru

sR

ight

19

∼∼

∼∼

∼−5

9−4

22

80

3.0

5Lef

t19

∼∼

∼∼

∼52

−50

547

3.0

9

VisualC

ort

ex(V

1,V

2,V

3)

Rig

ht

17/18/19

−19

−73

273

5.1

7∼

∼∼

∼∼

Lef

t17/18/19

23

−90

10

58

6.4

8∼

∼∼

∼∼


Table

3.

(Continued

)

Contr

ols

15

>Patien

ts11

Patien

ts11

>C

ontr

ols

15

BA

MN

IC

oord

inate

sk

Max.

MN

IC

oord

inate

sk

Max.

xy

zZ

xy

zZ

Dors

ola

tera

lP

refr

onta

lC

ort

exR

ight

46

∼∼

∼∼

∼−3

150

442

4.8

2Lef

t46

∼∼

∼∼

∼30

37

−2102

7.2

9

Pre

fronta

lC

ort

exR

ight

∼−1

851

20

48

6.1

5∼

∼∼

∼∼

Lef

t∼

25

44

837

3.9

2∼

∼∼

∼∼

Caudate

Rig

ht

24

−20

20

637

4.2

6∼

∼∼

∼∼

Lef

t24

13

615

68

5.1

8∼

∼∼

∼∼

Super

ior

Pariet

alLobule

Rig

ht

7−4

1−7

635

153

8.1

6−4

6−6

649

118

4.8

2Lef

t7

38

−65

29

99

6.2

839

−61

35

67

9.7

2

Infe

rior

Pariet

alLobule

Rig

ht

40

−52

−38

37

60

6.6

9−5

1−3

536

49

3.8

2Lef

t40

49

−35

41

67

5.7

242

−51

38

218

7.7

6

Puta

men

Lef

t∼

26

−5−2

34

3.0

9∼

∼∼

∼∼

Fusi

form

Gyru

sR

ight

37

−41

−38

−11

110

4.2

9∼

∼∼

∼∼

Lef

t37

40

−32

−17

64

4.2

6∼

∼∼

∼∼

Mid

dle

Tem

pora

lG

yru

sR

ight

19

−56

−55

−2215

5.4

1∼

∼∼

∼∼

Lef

t19

47

−47

−5107

3.8

9∼

∼∼

∼∼


Table

3.

(Continued

)

Contr

ols

19

>Patien

ts15

Patien

ts15

>C

ontr

ols

19

BA

MN

IC

oord

inate

sk

Max.

MN

IC

oord

inate

sk

Max.

xy

zZ

xy

zZ

Dors

ola

tera

lP

refr

onta

lC

ort

exR

ight

46

−14

64

830

4.1

5∼

∼∼

∼∼

Ven

trola

tera

lP

refr

onta

lC

ort

exLef

t∼

10

64

134

6.1

8∼

∼∼

∼∼

Caudate

Rig

ht

24

−12

10

668

5.7

0∼

∼∼

∼∼

Lef

t24

22

22

−251

3.8

1∼

∼∼

∼∼

Super

ior

Pariet

alLobule

Rig

ht

7−4

0−6

841

64

3.5

9−3

4−7

829

62

4.2

9Lef

t7

39

−70

36

39

4.5

924

−72

27

68

3.0

8

Infe

rior

Pariet

alLobule

Rig

ht

40

−42

−32

32

46

3.2

5−3

9−2

643

82

7.2

5Lef

t40

47

−34

45

59

3.7

951

−29

45

55

6.1

9

Post

erio

rC

ingula

teR

ight

32

∼∼

∼∼

∼5

−54

0125∗

5.8

9Lef

t32

∼∼

∼∼

∼3

−46

7125∗

4.5

5

Mid

dle

Tem

pora

lG

yru

sR

ight

19

−46

−55

−756

4.2

6−4

8−5

61

41

3.9

2Lef

t19

48

−46

381

3.9

9∼

∼∼

∼∼

VisualC

ort

ex(V

1,V

2,V

3)

Rig

ht

17/18/19

∼∼

∼∼

∼−1

0−7

81

61

4.8

9


uncertainty, however, continued weakening of the neural coding occurred, and wasassociated with impaired behavioral performance.

Second, the overall network of brain activity during task performance was morewidely distributed in controls, and involved greater recruitment of frontal and pari-etal areas in the control of visual attention. It is likely that activation in these moreanterior areas reflected both feedforward processing of the contours from posteriorareas, and the generation of feedback from frontal and parietal to visual areas tostrengthen the salience of the overall contour representation relative to backgroundnoise [41].

In the control group, reaction time increased as a function of contour elementorientation jitter (i.e., as the stimuli became more difficult to integrate). This wasassociated with an increase in visual cortex activation, presumably reflecting pro-longed visual analysis, but not activation in other cortical regions. In contrast, theschizophrenia group demonstrated an attenuated pattern of increasing RTs as afunction of orientation jitter. Moreover, they demonstrated increased activation pri-marily in anterior regions, with less visual cortex activation increase in the conditionwhere contours were least visible. This pattern of results suggests that the nor-mal linkage between the requirement for prolonged visual analysis, increased visualcortex activity, and delayed response preparation is disrupted in schizophrenia. Itappears as if, in schizophrenia, enhanced demands for visual analysis led to a morediffuse pattern of brain activity, one that does not support the prolonged mainte-nance of visual processing required for successful performance, or allow for a delay inbehavioral responding until adequate visual analysis is completed. Specific evidencein support of this hypothesis is that the largest differences between patients andcontrols in RT were in the three most difficult conditions.

Visual integration performance was not related to either medication dose or tosymptoms. Although the issue of relating performance to medication was not a focusof this study (given consistent evidence of a nonrelationship in past studies [69]),it should be noted that this study was underpowered to detect these relationshipswith correlational analyses. The same issue of power holds for analysis of symptom-performance relationships. However, as patients in this study were all clinically sta-ble and were experiencing relatively low levels of symptoms (e.g., mean PANSSsymptom scores were within the minimal-mild range for all 5 factors), the studydid not include a symptomatically heterogeneous enough group of patients to detectthese relationships. In general, cognition-symptom relationships are observed morereliably in more symptomatically heterogeneous samples [60], and specifically, visualintegration deficits have been linked to clinical disorganization when symptomaticsamples are studied [25, 28, 47, 51, 59, 65–67, 69]. The finding of behavioral and neu-rophysiological evidence for visual integration in a relatively asymptomatic sample,however, suggests that the contour integration task is a sensitive and psychophys-ically rigorous measure of visual integration, and also that integration dysfunctionmay be present even in relatively remitted patients — although the impairment can


be expected to be reduced compared to more acutely or chronically symptomaticpatients.

A potential limitation of this study is that the stimulus presentation used a blockdesign in which the difficulty level increased with each successive block. This poten-tially confounds the observed results with a nonspecific order effect. The design waschosen based on prior data that schizophrenia patients and controls demonstratedperformance differences using this version of the task, and that this version is botha reliable and valid measure of visual integration [37]. Still, a direct comparisonbetween random versus blocked presentation of stimuli could be informative. In aprior behavioral study by our group using a contour detection task (as opposed to the2AFC version used in this study), both healthy controls and schizophrenia patientsdemonstrated superior performance when stimuli were presented in increasing orderof difficulty, compared to when they were presented in a random order [57]. Thissuggests that while activation differences might exist between the two conditions,group differences might be similar across the two conditions. However, it is alsopossible that while both groups might perform better and have increased activationin the blocked condition, the group difference might be of a different magnitude inthe random condition, which would be informative regarding top–down effects oncontour processing.

Another limitation of the study involves a potential confound between contourintegration and attentional processes involved in visual search in the more difficultconditions. This is especially relevant to the contrasts comparing the 23◦ and 19◦

jitter conditions to the 0◦ condition, where inability to perceive a contour may haveled to attentional, or other unknown processes being used by subjects in what weassumed to be a control condition. This can be addressed to some extent in futurestudies by using passive viewing conditions where no response is required. On theother hand, we consider it unlikely that the greater activation in post-V1 areas in thecontrols, in the 0◦ vs. 19◦ and 0◦ vs. 23◦ contrasts, reflected such a confound because:(1) controls demonstrated these effects even when matched to patients on highlevels of accuracy, in contrasts in which data from more difficult conditions were notincluded; (2) in between-group contrasts comparing the groups on peak activationin each jitter condition alone, controls demonstrated greater activation in V2/V3and V4; and (3) our results from controls are strikingly similar to the nonclinicalhuman and monkey data observed by Altmann et al. [1], using tasks which did notrequire a subject response, and from analyses that did not contrast either easieror more difficult conditions. Overall, this suggests that the group differences inthe between-condition contrasts comparing contour-visible and contour-not-visibleconditions reflect true schizophrenia-related visual cortex processing abnormalities,as opposed to confounds related to either their poor motivation or increased effortexerted in the two most difficult conditions.

Although there was a trend towards a group difference in male:female ratio,it is unlikely that our findings are an artifact of this sex difference. First, males


and females did not differ significantly in task performance, either within groups oroverall. Second, an ERP study [26] using a global–local task found that a relativeimpairment in identifying global forms was limited to female schizophrenia patients,whereas most subjects in our patient group were male. Third, in a re-analysis of anearlier study using a contour detection version of the contour integration task [57](N = 87), we found no main effect of gender (p = 0.66) or diagnosis vs. gendereffect (p = 0.7).

In summary, the results of this study extend past behavioral findings of visualintegration impairment in schizophrenia, by linking these accuracy and reactiontime disturbances to specific brain regions. The data highlight both the importanceof regional occipital lobe (V2–V4) activity in the visual integration impairmentin schizophrenia, as well as the changes in processing outside of the visual cortexthat can affect perception and task performance. Because it is known that visualintegration impairments are most pronounced in patients with prominent disor-ganized symptoms [25, 28, 47, 51, 56, 59, 65–67, 69], that symptomatic patientsimprove their visual integration task performance with treatment [67], and thatdegree of task score change covaries with change in disorganized symptoms [67], itis possible that repeated fMRI investigation of visual integration in patients mov-ing from the acute to stabilization phases of illness may reveal brain mechanismsinvolved in the normalization of perceptual, and perhaps other cognitive integrativefunctions.

Acknowledgments

This research was funded by a NARSAD Independent Investigator Award to StevenSilverstein, and by Hungarian Science Foundation OTKA NF grant 60806 to IlonaKovacs. We also thank Sarah Carson for her assistance in formatting the paper.None of the authors have a conflict or potential conflict of interest regarding anyproducts or procedures used in this research.

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