a neural substrate for atypical low-level visual processing in autism

A neural substrate for atypical low-level visualprocessing in autism spectrum disorderMyriamW.G.Vandenbroucke,1,2 H. Steven Scholte,2 Herman van Engeland,1 Victor A. F. Lamme2,3

and Chantal Kemner1,4

1Department of Child and Adolescent Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical CentreUtrecht, Utrecht, 2Cognitive Neuroscience Group, Department of Psychology, University of Amsterdam, Amsterdam,3The Netherlands Institute for Neuroscience, Amsterdam,The Netherlands, part of the Royal Academy of Arts and Sciences(KNAW) and 4Department of Neurocognition, Faculty of Psychology, Maastricht University, Maastricht, The Netherlands

Correspondence to: MyriamW.G.Vandenbroucke, University Medical Centre Utrecht, Department of Child andAdolescent Psychiatry, B01.201Heidelberglaan 100, 3584 CXUtrecht, The Netherlands.E-mail: [email protected]

An important characteristic of autism spectrum disorder (ASD) is increased visual detail perception.Yet, thereis no standing neurobiological explanation for this aspect of the disorder.We show evidence from EEGdata, from31 control subjects (three females) and 13 subjects (two females) aged 16^28 years, for a specific impairment inobject boundary detection in ASD, which is present as early as 120ms after stimulus presentation. In line with aneural networkmodel explicating the role of feedforward, horizontal and recurrent processing in visual percep-tion, we can attribute this deficit to a dysfunction of horizontal connections within early visual areas.Interestingly, ASD subjects showed an increase in subsequent activity at lateral occipital sites (225ms), whichmight reflect a compensational mechanism. In contrast, recurrent processing between higher and lower visualareas (around 260ms), associated with the segregation between figure and background, was normal. Ourresults show specific neural abnormalities in ASD related to low-level visual processing. In addition, given thereconciliation between our findings and previous neuropathology and neurochemistry research, we suggestthat atypical horizontal interactions might reflect a more general neural abnormality in this disorder.

Keywords: Asperger; horizontal connections; lateral inhibition; GABA; minicolumns

Abbreviations: ADI-R=autism diagnostic interviewçrevised; ADOS-G=autism diagnostic observation scheduleçgeneric;ASD=autism spectrum disorder; CMS=common mode sense; DRL=driven right leg; ERP=electrophysiological recording;FDR= false discovery rate.

Received July 12, 2007. Revised December 1, 2007. Accepted December 12, 2007. Advance Access publication January 11, 2008

IntroductionAutism spectrum disorder (ASD) is a neurodevelopmentaldisorder characterized by impairments in social interactionand communication, and restricted and repetitive behavioursand interests. Another important characteristic of the disorderis a strong tendency for visual detail processing. In laboratorysettings, this is reflected in superior performance in severaltasks, such as the embedded figures test, the block design testand visual search tasks (for a review and discussion see Dakinand Frith, 2005). However, as yet, there is no standingneurobiological explanation for this aspect of ASD.A model on visual perception, called reverse hierarchy

theory (Hochstein and Ahissar, 2002), relates detail percep-tion to feedback or recurrent activity in the visual cortex,while the processing of global stimulus aspects is associated

with feedforward processing. When a visual stimulus entersthe occipital cortex, initially a general, categorical interpreta-tion is provided, called ‘vision at a glance’ by these authors.Thereafter, through feedback from higher visual areas, detailsare incorporated, called ‘vision with scrutiny’. Since peoplewith ASD show enhanced detail perception, the balancebetween visual feedforward and feedback processing in thesepatients is of specific interest.

Findings from monkey research have provided directevidence for the selective contributions of feedforward, feed-back and also horizontal interactions in visual perception,especially in relation to figure-ground segregation, i.e. thesegmentation of a scene into figure and background (Lamme,1995; Lamme et al., 1998a; Lamme et al., 1999). In short, thereare three essential neural processing steps in segregating

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a figure from its background: (i) texture elements (such asthe lines in Fig. 1A) are detected by neurons that areselectively tuned to features such as orientation (Hubel andWiesel, 1959). Information on the texture elements is

mediated by feedforward processing; (ii) the detection oforientation boundaries (where two different orientationsmeet, see Fig. 1A) is mediated by lateral inhibition betweenneurons with similar orientation preference (Knierim and

Fig. 1 (A) The stimuli used in the discrimination task while EEG activity was measured. (B) The EEG responses (converted to splinelaplacian’s) to the stimuli for controls and subjects with ASD, before any subtraction. (C) The subtractions that were made to isolateactivity related to boundary detection and surface segregation. Different grey levels represent different line orientations. (D) Thestack^frame subtraction wave (for control subjects) to determine the time window, indicated by the grey panel, related to surfacesegregation. (E) The frame^homogeneous subtraction wave (for control subjects) to determine the time window, indicated by thegrey panel, related to boundary detection.

1014 Brain (2008), 131, 1013^1024 M.W.G.Vandenbroucke et al.


Vanessen, 1992). Such inhibition will lead to an enhancedresponse at locations where different orientations meet.Horizontal connections between cells with similar orientationtuning play an important role in such effects (Gilbertand Wiesel, 1989; Malach et al., 1993; Stettler et al., 2002);and (iii) the segregation of a surface from its background hasbeen related to feedback processing (Lamme, 1995; Zipseret al., 1996; Lamme et al., 1998a; Angelucci and Bullier, 2003),i.e. recurrent interactions between higher and lower visualareas. Feedback loops lead to enhanced activity of neuronsresponding to the information inside the boundariescompared to neurons responding to the background(Lamme et al., 1999). This leads to so-called ‘filling-in’ ofthe surface region. The role of feedback in surface segregationhas been directly demonstrated with lesion studies(Hupe et al., 1998; Lamme et al., 1998b): when extra striateareas in the monkey brain were eliminated, the signal relatedto surface filling-in was no longer apparent. The specificroles of feedforward, horizontal and feedback processingin texture segregation are thus supported by neurophysiolog-ical studies and a recent model of Roelfsema et al. (2002)makes their relative contributions explicit.Indeed, the involvement of abnormal feedforward or

recurrent mechanisms in ASD has been suggested. Forinstance, Gustafsson (1997) proposed that excessive inhibi-tory lateral feedback is a prominent feature of the disorder,causing high sensory discrimination. Also, it has been putforward that a failure of neuronal pruning (which is possiblythe underlying cause of abnormally large brain size oftenfound in subjects with ASD, see Frith, 2004; Happe and Frith,2006), results in aberrancies in feedback processing, whilefeedforward systems are intact. Additionally, Happe andFrith suggest that a lack of the modulatory influence offeedback might result in perceptual abnormalities as foundin ASD. Bertone and colleagues (2005) argue in a recentstudy, based on the finding of impaired orientation detectionfor second order, texture defined stimuli, that both lateraland feedback activity might be atypical in ASD. In addition,lateral or horizontal connections not only play an importantrole in processing texture stimuli as used by Bertone et al.,but also in grouping or binding (Polat, 1999; Roelfsema,2006). Specifically, monkey research has shown the selectivityof certain horizontal connections for neurons with similarorientation tuning (Malach et al., 1993) and these connectionsmost likely play a role in perception by Gestalt laws,such as grouping by similarity (Roelfsema, 2006). A recentbehavioural study demonstrated that grouping by Gestaltprinciples, amongst others grouping by similarity, is lessstrong in people with ASD compared to controls (Brosnanet al., 2004). Also, using the embedded figures test, wherethe stimulus configuration has to be suppressed to find theembedded figure in a complex pattern of line elements,it has repeatedly been shown that people with ASD performbetter than healthy control subjects, demonstratingthat this kind of grouping is weaker in this disorder(Jolliffe and Baron-Cohen, 1997; Ropar and Mitchell, 2001;

De Jonge et al., 2006). These theoretical accounts andexperimental findings implicate the involvement of abnormalfunctioning of horizontal and feedback connections in visualperception in ASD.

Altogether, we hypothesize that an imbalance betweenfeedforward, horizontal and feedback processing is a corefeature of ASD, associated with aberrant detail perception.The functioning of these different types of connections hasnever been, to our knowledge, systematically or explicitlystudied in this disease. Since the specific roles of feed-forward, horizontal and recurrent or feedback processinghave been demonstrated in figure-ground segregation oftextured figures (see Fig. 1A), we considered the use ofsuch stimuli appropriate for examining our hypothesis.With electrophysiological recordings (event related poten-tials, ERPs), we studied texture segregation in a group ofASD subjects and compared them to healthy controls. Weused stimuli composed of oriented line segments, whichcontained different amounts of figure surfaces and bound-aries (see Fig. 1A and the Results section for a thoroughexplanation of the stimuli). By employing specific stimuluscontrasts, we could single out ERP activity related toboundary detection and surface segregation, while selec-tively discounting activity related to local feature detection.This enabled us to draw inferences about the balancebetween feedforward, horizontal and feedback processingin subjects with ASD and age and IQ-matched healthycontrols.

Material and methodsSubjectsThirty-one control subjects (three females) and 13 subjects(two females) with ASD participated in this study (five with adiagnosis of autistic syndrome, eight with a diagnosis of Aspergersyndrome), aged between 16 years and 4 months and 28 years and10 months. There were no significant age or IQ differencesbetween the groups (Table 1) and all subjects had normal orcorrected to normal vision.The diagnostic evaluation included a psychiatric observation

and a review of prior records (developmental history, childpsychiatric and psychological observations and tests). ASD wasdiagnosed by a child psychiatrist, using the DSM-IV criteria. Theparents of these subjects were administered the autism diagnosticinterview—revised (ADI-R, Lord et al., 1994) and subjects withASD were administered the autism diagnostic observationschedule—generic (ADOS-G, Lord et al., 1989), both by a trainedrater. Twelve subjects met ADI-R criteria for autism or autism

Table 1 Age and IQ

Age in years TIQ (SD)

Control (n=31) 21.6 (2.1) 117.3 (7.9)ASD (n=13) 20.8 (4.1) 120.5 (11.1)

IQ was measured using the full WAIS-III for subjects with ASD.A short version of the WAIS-III was used to determine IQ forthe control subjects.

A low-level visual deficit in ASD Brain (2008), 131, 1013^1024 1015


spectrum disorder; one subject did not meet criteria forstereotyped behaviour (this subject did meet ADOS-G criteria).All subjects, but one (who did meet ADI-R criteria), met thefull ADOS-G criteria for autism or autism spectrum disorder.The subjects were medication free except for one (who used 20mgseroxat and 3mg risperdal per day) and had no significantneurological history. Both the subjects with ASD and the controlsubjects received a money reward for their participation. Thestudy was approved by the medical ethics committee of theUniversity Medical Centre Utrecht and subjects gave writteninformed consent prior to participation.

Stimuli, conditions and procedureSubjects performed a discrimination task, during which EEGactivity was measured, with randomly presented stack, frameand homogenous stimuli (see Fig. 1A). The stimuli were made ofblack line segments (0.9 cd/m2), with a length of 0.36�, a width of0.02� and an average density of 4.2 line segments per degree,projected randomly on a white background (103 cd/m2). Fourorientations (22.5�, 67.5�, 112.5�, 157.5�) of the line segmentswere used in a balanced way to create the stimuli. The lineorientation at each edge of the texture border of frame andstack stimuli was always at 45� with that of the background and at45� with that of the region enclosed by the border. In framestimuli, the line orientation of the enclosed region was the same asthat of the background, whereas in stack stimuli the lineorientation of the enclosed region was at 90� with that of thebackground. The inner square size of the stack and frame stimuliwas always 1.93�.To establish a parametric design the difficulty level of the

discrimination task was manipulated by varying the thickness ofthe borders of frame and stack stimuli (see Fig. 2A), i.e. 0.32�,0.81� or 1.29�, resulting in a total stimulus size of 2.57�, 3.55� or4.51�. We conjectured that thicker borders would make thedistinction between stack and frame stimuli less visible (it wouldbe more difficult to see the inside of the frame as continuous withthe background). Concerning the ERP data, we hypothesized thatthe manipulation of border width would primarily affect surfacesegregation, reflected in the activity in the stack—frame contrast(see Results section for an explanation on the relation between thestack—frame contrast and surface segregation).During the experiment, subjects fixated a red dot (24 cd/m2,

0.24�) in the centre of the computer screen which was presentduring the whole trial. A trial started with the presentation of a

stack, frame or homogeneous stimulus for 267ms at an unpredic-table location in one of the quadrants of the screen (eccentricitybetween the fixation dot and the closest stimulus corner wasalways 1.7�). The stimulus was immediately followed by a mask(jittered presentation duration of 1817 to 2017ms resulting in atotal trial duration of 2084 to 2284ms), consisting of the sameline elements, but now in random orientations. When subjectspressed a response button, the fixation dot changed from red togreen. To announce the next trial the colour changed back to red267ms before the start of the trial. Responses had to be as fast aspossible and before the colour of the fixation dot changed back tored. Buttons for stack and homogeneous stimuli were alwayspressed by the index and middle finger of one hand (left or right,counterbalanced over subjects) and the button for frame stimuliwas pressed by the index finger of the other hand. Since stackand homogeneous stimuli resemble each other least, this patternof button responses would make confusion between buttonresponses least likely.Three experimental settings made sure subjects had to rely on

their initial percept and a direct ‘cognitive’ comparison of theinner square with the background was impossible: (i) the shortpresentation duration of the stimuli (267ms); (ii) the unpredict-able appearance of the stimuli in one of the quadrants of thescreen; and (iii) the appearance of the mask.Subjects practised a discrimination task beforehand with three

different practice blocks. Then, six experimental blocks of trialswith 32 stimuli per level of border width of each frame and stackstimulus, and 32 homogeneous stimuli were presented to thesubjects.

Psychophysicsçdata analysisPercentage correct and reaction times were separately analysed forstack and frame stimuli using a repeated measures ANOVA withBorder Width (three levels) as within subject factor and Group(patient/control) as a between-subjects factor. Since the homo-genous stimuli could not be parametrically manipulated, percent-age correct and reaction time data for this stimulus werecompared between groups using a one-way ANOVA.

EEGçrecording and data analysisElectroencephalographic activity was recorded by means of aBiosemi 48-channel Active Two EEG system (Biosemi Instrumen-tation BV, Amsterdam, the Netherlands), and data were sampled

Fig. 2 (A) The manipulation of the stack and frame stimuli, i.e. increasing the border width. (B) Performance data of the three-wayalternative forced choice task between homogeneous (‘hom’), stack and frame stimuli. Subjects with ASD scored lower on frame andhomogenous stimuli, whereas performance for stacks was the same compared with controls.



at a rate of 256Hz. Two electrodes in the cap, the CMS (commonmode sense) and DRL (driven right leg), provided an ‘activeground’ in this system. To monitor eye movements, vertical andhorizontal EOG was recorded with electrodes attached above,below and next to each eye.Data were referenced to ‘‘frontal central electrode’’ and filtered

offline with a high-pass filter at 0.5Hz, a low-pass filter at 20Hzand a 60Hz notch filter. In order to compute event-relatedpotentials, segments from 250ms pre-stimulus until 700ms post-stimulus were extracted offline. Before ocular correction, auto-matic raw artefact rejection was applied by removing segmentscontaining voltage steps of more than 50 mV, removing anysegments outside the –200 to 200 mV range as well as segmentscontaining larger than 200mV differences. EOG artefacts wereremoved from the EEG using an algorithm where correctionfactors are calculated on the basis of linear regression (Grattonet al., 1983). After ocular correction, artefact rejection was appliedagain by removing all segments outside the �75 to 75 mV. Artefactrejection resulted in 9% rejected segments in the control groupand 14% rejected segments in the ASD group. Linear local DCde-trending was applied to remove current drift (by subtracting alinear function from each segment). Segments were corrected forbaseline, using the data from the 150ms prior to stimulus onset.All pre-processing steps were done using Brain Vision Analyzer(Brain Products GmbH, Munich, Germany).To establish if the scalp distributions of the subtraction ERPs

were at occipital sites, comparable to previous electrophysiologystudies on figure-ground segregation (Knierim and Vanessen,1992; Lamme, 1995; Caputo and Casco, 1999), spline Laplaciandistribution maps were calculated. This was done by interpolatingERP waves using spherical splines and approximating currentsource densities (Perrin et al., 1989). The resulting maps arespatial second-order derivatives of the scalp potentials lendinggreater weight to local contributions of cortical generators,filtering out deep sources, as well as being reference free (Nunezand Srinivasan, 2006).For each subject, the data were averaged for each stimulus and

for each quadrant separately. We first averaged the data withinquadrants across subjects since stimulation of the different spatiallocations led to a retinotopic organization of the ERP signals,which was similar in both groups. Then, individual subject datawere averaged for each group, followed by averaging the quadrantdata, leaving group averages for homogeneous stimuli, and stackand frame stimuli with different border widths.Before doing the analyses, we pooled some electrodes to

increase the signal-to-noise ratio. Stack and frame stimuli wereaveraged over border width and, as well as homogeneous stimuli,averaged over groups. The subtraction ‘(stack + frame)–homo-geneous’ was used to determine pooling of electrodes by visuallyinspecting the data. Consistent with earlier electrophysiologyfindings (Knierim and Vanessen, 1992; Lamme, 1995; Caputo andCasco, 1999), this figure-ground contrast elicited the largestdifferential activity at occipital sites, i.e. at Oz, O1 and O2; thispool of electrodes was used for further analyses.Next, we wanted to outline in the control group the time

windows related to boundary detection and surface segregation, tosubsequently compare these between the groups. Therefore,subtraction potentials were calculated for frame and stack stimuliwith thin borders: ‘frame–homogeneous’ and ‘stack–frame’ (seeResults section for an explanation and see Fig. 1C). To validlydefine the time windows in the control group, the first moment

of significant deflection was determined with a random effectsanalysis, performed on the difference waves by employing a one-sample t-test at each time point. The average of each subject atthat time point was treated as an observation. Correction formultiple comparisons with respect to the number of time pointsbeing tested was done using the False Discovery Rate(FDR, P50.01, Benjamini and Hochberg, 1995) in MATLAB(The MathWorks, Inc., Natick, MA, USA). Then, from the firstmoment of significant deflection, one-sample t-tests were done inSPSS (SPSS Inc., Chicago, IL, USA) on both difference waves overpooled time segments of 20ms, to determine the total timewindow of the deflection (P50.005).For the resulting time windows of the subtraction potentials

‘frame–homogeneous’ and ‘stack–frame’ a comparison betweengroups was done on segments of 20ms, using a repeated measuresANOVA with Border Width (three levels) as within-subjects factorand Group (patient/control) as between-subjects factor. P-valuesof50.005 were considered significant. The occipito-parietal effectstudied post hoc (see Results section) was found by continuing theanalysis of the 20ms segments after the time window related toboundary detection.

ResultsContrasting stack, frame and homogeneousstimuli to separate surface segregation fromboundary detectionFigure 1A shows the stimuli used in our experiment. In ahomogeneous stimulus, no figure is present. A framestimulus consists of an ‘empty’ frame (border) on ahomogeneous background whereas in case of the stackstimulus the inside of the border is filled with lines of athird orientation. These three stimuli contain the sameelementary features, i.e. local line elements with specificorientations (see Material and methods section). By using,in different exemplars of each stimulus, all orientationsfor background, frame or the region within the frame,these low-level features can be fully balanced over trials.In this way all three stimuli will, on average, elicitidentical responses from orientation-selective mechanisms,i.e. neurons in early visual areas in the feedforward sweep(Hubel and Wiesel, 1959). In addition, the experimentalset-up allows for selectively discounting activity that iscaused by the orientation discontinuity, arising fromhorizontal interactions (Gilbert and Wiesel, 1989; Malachet al., 1993; Stettler et al., 2002): stacks and frames containthe same amount and strength of orientation boundaries.The only difference between these stimuli is that stackscontain an extra texture-defined surface, which results inthe percept of the stacking of two squares. This extrasurface will elicit a relatively enhanced texture segregationsignal in lower occipital areas, arising from feedback fromhigher cortical areas (Lamme, 1995; Zipser et al., 1996;Lamme et al., 1998b; Angelucci and Bullier, 2003).

In Fig. 1B, we show ERP responses to stack, frameand homogeneous texture stimuli, obtained from bothcontrol and ASD subjects. After the subtraction procedure,



outlined in detail in Fig. 1C, we obtain more selectiveresponses. As explained above, the remaining signal fromthe subtraction ‘stack – frame’ is due to the differencein surface organization between stack and frame stimuli.This signal (from control subjects), is shown in Fig. 1D.A significant positive deflection related to surface segrega-tion is found between 215 and 320ms after stimuluspresentation.The contrasts ‘frame – homogeneous’ and ‘stack – homo-

geneous’ isolate activity due to both surface segregationand boundary detection. The contribution of surface-segregation mechanisms will be lowest in the ‘frame –homogeneous’ contrast, as the frame has only a minimalamount of surface. In addition, we know from earlier workin both human subjects and monkeys that boundarydetection precedes surface segregation in time (Caputo andCasco, 1999; Lamme et al., 1999; Roelfsema et al., 2002). In thecontrol subjects, our stack–frame contrast reveals surfacesegregation mechanisms to emanate from 215ms onwards(see above). Therefore, it is logically warranted to attributeany activity found in the frame-homogeneous contrastprior to 215ms to boundary detection mechanisms. Thisactivity is indeed present, from 121ms onwards, as shownin Fig. 1E.Having identified, in control subjects, the ERP signals

that relate to either boundary detection or surface segrega-tion, we can now proceed towards investigating whetherthere are differences between the control (n= 31) and ASD(n= 13) subjects with respect to these two elementaryvisual processes. First, however, we show the psychophysicaldata of the behavioural task.

Stack, frame and homogeneous stimuli:performance data from a parametric designDuring the EEG measurement behavioural data wereobtained, of which group averages are shown in Fig. 2B.Performance decreased with increasing border width (seeFig. 2A) both for frames [F(84,2) = 62.616, P= 0.000] andfor stacks [F(84,2) = 15.729, P= 0.000]. In addition, subjectswith ASD scored lower than controls on homogeneousstimuli [F(42,1) = 11.578, P= 0.001] and on frame stimuli[F(42,1) = 5.363, P= 0.026), but performance was the samein both groups for stack stimuli [F(42,1) = 0.137, P= 0.713].There were no interactions between group and borderwidth for either stacks or frames. These data suggest thatsubjects with ASD are able to detect a stimulus that has ahighly salient surface (stacks), whereas detection of astimulus on the basis of mostly boundaries (frames) isimpaired. This impairment could also have led to confusionbetween homogeneous and frame stimuli, explaining lowerperformance scores on homogeneous stimuli in the ASDsubjects. Finally, it should be noted that there were nodifferences between the groups on RT for either frame[F(42,1) = 0.552, P= 0.462], stack [F(42,1) = 1.156, P= 0.288]or homogeneous stimuli [F(42,1) = 0.872, P= 0.356].

Impaired boundary detectionin subjects with ASDAs explained before, we investigated the specific stimulussubtractions and time windows related to boundarydetection and surface segregation to reveal differencesbetween subjects with ASD and controls in the underlyingvisual processing mechanisms. We should note that whenlooking at the ERP data, prior to subtraction, in Fig. 1B,one will see additional differences between the groups, atvarious latencies, both before and after the intervalsdiscussed here. These differences could very well reflectother fundamental distinctions between controls and ASDin the processing of visual stimuli. However, the currentstimuli and paradigm only allow stimulus subtractionsthat reveal the underlying neural mechanisms specificallyrelated to boundary detection and surface segregation.(For example, the detection of line elements was subtractedout and could not be compared between the groups).We do, therefore, not feel free to speculate about the originof other differences between the groups.

We first compared the ASD subjects with controls on theearly part of the frame–homogeneous contrast, which isspecific for boundary detection. This contrast was made forall three border widths of the frame stimuli (Fig. 3A).Figure 3C shows the subtraction waves for the three levelsof border width. Figure 3B and D depict the activationmaps of the control and ASD subjects respectively, duringthe temporal interval that is specific for boundary detection(roughly 100–200ms). In these maps, activity related to theframe–homogeneous contrast is pooled for all three framestimuli, for reasons outlined subsequently.

As already revealed, the control group showed a negativedeflection related to boundary detection from 121msafter stimulus presentation at central occipital electrodes(Fig. 3B). Remarkably, this negativity was strongly dimin-ished in the ASD group (compare the maps of Fig. 3Dwith those of Fig. 3B, and the ERP traces in Fig. 3C).A repeated measures ANOVA confirmed this: from 121 to203ms the subtraction waves (Fig. 3C) differed significantlybetween the groups [F(42,1)4 9, P5 0.005] over all threelevels of border width. Note that the ERP data includetrials for both correct and incorrect responses. The accuracyof the behavioural response might have confounded theERP difference in boundary detection between the groups.However, excluding the incorrect trials (about 35%) wouldstrongly reduce the statistical power and it would undocounterbalancing of the line orientations that we carefullyapplied when designing the stimuli (see Material andmethods section).

In addition, we found that activity increased with thickerborders on the boundary-related signal from 121 to 141ms[F(42,1)45, P50.005]. This is probably due to the factthat when the ‘width’ of the border increases, the ‘length’ ofthe orientation boundary also enlarges, resulting inenhanced boundary-related signal. However, the effect of



border width was very small and not distinguishable atthe level of the activation maps. Therefore, we pooled theactivity of all three border widths before computing themaps of Fig. 3B and D.We should also note that the boundary-related signal was

weaker for a stimulus presented in the upper visual fieldcompared to a stimulus in the lower visual field, which wasevident in both groups. Therefore, we re-analysed the datafor the upper and the lower visual fields separately.For stimulation of the lower visual field, the differencebetween the groups for the boundary-related ERP was ofthe same magnitude and strength as displayed in Fig. 3,

while stimulation of the upper visual field led to a similar,but less significant trend. There were no differences betweenstimulation of the left or right hemifield.

Our finding that ASD subjects show a diminishedboundary-related negativity is consistent with the psycho-physical results as the subjects with ASD scored lower onframe stimuli than controls. Correct detection of framesrelies more heavily on processing of orientation boundaries,since there is little signal related to surface segregation.A deficit in the identification of frames could well berelated to the diminished boundary-related negativity in thepatient group. To test this, we correlated the performance

Fig. 3 (A) The subtractions for the three different border widths of frame stimuli that were made to isolate activity related to boundarydetection.Different grey levels represent different line orientations (not all line orientations are shown). (B) Activation maps of the frame^homogeneous contrast for the control group, pooled over the three levels of border width. (C) The graphs represent the subtractionwaves frame^homogeneous for the three levels of border width at central occipital sites (O1,O2,Oz; controls left; ASD right). During thetime window related to boundary detection, indicated by the grey panels, activity was strongly diminished for subjects with ASDcompared to controls. (D) Activation maps of the frame^homogeneous contrast for the ASD group, pooled over the three levels ofborder width.



on frame stimuli, averaged over border width, to theboundary-related ERP signal, also averaged over borderwidth, separately for each subject group. Interestingly, wefound that there was a negative correlation between theperformance data and the subtraction ERP for the ASDsubjects on the first 20ms time bin (i.e. 121–141ms,Pearson’s r=�0.55, P= 0.05, see Fig. 4). This means thatASD subjects who scored higher on frame stimuli showed amore negative ERP deflection. A correlation betweenperformance on frames and the boundary-related negativitywas not evident for the control subjects (Pearson’sr=�0.09, P= 0.65). This finding confirms that a deficit inthe detection of frames in ASD subjects is probably relatedto the diminished boundary-related ERP signal, whereaswhen subjects with ASD performed similar to controls,their ERP signals also showed a comparable boundary-related negative deflection.

Normal surface segregationin subjects with ASDSurface segregation was singled out by the stack–framecontrast (see Fig. 5). This contrast was made for all threelevels of border width of the frame and stack stimuli(Fig. 5A). Figure 5C shows the subtraction waves for thethree levels of border width. Figure 5B and D depict theactivation maps of the control and ASD subjects, respec-tively, during the temporal interval that is specific forsurface segregation (roughly 200–300ms), and for stacksand frames with the thinnest borders (stack 1–frame 1, seeFig. 5A). This contrast was selected for presentationbecause it yielded the highest signal-to-noise ratio of thestack–frame activity.As already revealed, the control group showed a positive

deflection related to surface segregation from 215 to 320msafter stimulus presentation at central occipital electrodes,indicated in red at the maps in Fig. 5B. Figure 5D showsthe activation maps for the ASD group. The subtractionwaves (Fig. 5C) and the maps show that there were nodifferences between the groups during this time window.Although it appears as if the positive deflection continuedin the ASD group after this time interval, i.e. after 320ms,group effects were not significant. These data are againconsistent with our psychophysical findings: the subjectswith ASD had the same performance scores compared tocontrols on stack stimuli, for which the detection reliesheavily on surface segregation.On the basis of our behavioural data showing that the

discrimination between stacks and frames decreased withincreasing border width (see Fig. 2B), we expected that thestack–frame difference wave would also decrease (see alsoMaterial and methods section). The graphs in Fig. 5C showthat this was indeed the case in both groups from 242 to320ms [F(84,2)47, P50.005]. It should be noted that thedecrease in the performance data and the decrease in theERP activity for the stack–frame contrast were both linear

[performance: F(42,1)418, P50.005; ERP: F(42,1)410,P50.005]. Since the ERP activity at central occipital sitesprobably reflects processing in striate and extra-striateareas, the results provide additional evidence for the roleof these visual areas in the perceptual interpretation ofa visual scene, as has been suggested previously (Lee et al.,1998; Super et al., 2001; Guo et al., 2004).

Compensation in higher cortical areas?Thus far, we found that boundary detection mechanismsare aberrant in ASD, while surface-segregation mecha-nisms are not. How can this be, given that boundarydetection is considered to be an essential prerequisitefor surface segregation? We further investigated the frame–homogeneous contrast; that is, we looked at the activity‘after’ the time window that was specific to boundary

Fig. 4 Correlation graph of the performance scores on framestimuli and the boundary-related ERP (i.e. frame^homogeneous,averaged over border width) for the time window121^141ms,separately for each subject group (top: ASD, bottom: controls).The negative correlation for the ASD subjects (P=0.05) indicatesthat with increasing performance the boundary-related ERP wasmore negative, i.e. more comparable to controls. There was nosignificant correlation in the control group between performanceand the boundary related ERP (P=0.65)



detection. Surprisingly, it turned out that from 223 to243ms the signal at occipito-parietal electrodes (PO7, PO8,P7 and P8) was more positive in the ASD group comparedto controls (see Fig. 6A). This enhanced positivity wasdependent on the border width of the frame stimuli: thepositive deflection increased with increasing border widthin the ASD group but not in the control group [F(84,2)46,P50.005, see Fig. 6B]. This effect at lateral occipital sites inthe ASD group could cautiously be interpreted as enhancedprocessing and may reflect a compensatory mechanism forthe earlier atypical processing, around 120ms, at lowerlevel—central occipital—sites. See the Discussion sectionfor a more extensive debate on this issue.

DiscussionIn the current research, the neural basis of atypical visualdetail perception in ASD was investigated with ERPs.

We conjectured that this aspect of ASD is due to animbalance between feedforward, horizontal and feedbackactivity. By using a texture segregation task, where surfacesegregation could be varied independently of orientation-based boundary detection, feedforward, horizontal andrecurrent processing could be explicitly tested. The resultsshowed that from 121ms after stimulus presentation,subjects with ASD had strongly diminished activity atcentral occipital electrodes compared to controls inresponse to orientation boundaries. Interestingly, whencontrasting activity of stack and frame stimuli, we foundactivity associated with surface segregation from 215 to320ms at occipital electrodes, which did not differ betweenthe groups. The ERP data parallel the results of thebehavioural data since subjects with ASD scored lower onframe stimuli (for which correct identification reliedmainly on boundary detection), but not on stack stimuli

Fig. 5 (A) The subtractions for the three different border widths of stack and frame stimuli that were made to isolate activity relatedto surface segregation. Different grey levels represent different line orientations (not all line orientations are shown). (B) Activation mapsof the stack^frame contrast for the control group, only for the stack and frame stimuli with thin borders [level 1; see A)]. (C)The graphsrepresent the subtraction waves stack^frame for the three conditions at central occipital sites (O1, O2, Oz; controls left; ASD right).During the time window related to surface segregation, indicated by the grey panels, activity was the same for subjects with ASD andcontrols. (D) Activation maps of the frame^homogeneous contrast for the ASD group, only for the stack and frame stimuli with thinborders (level 1; see A).



(for which the correct identification relied mainly onsurface segregation). A significant correlation for the ASDsubjects between performance on frame stimuli and theboundary-related negativity, i.e. a more negative deflectionfor subjects with higher performance scores, confirmedthe association between the behavioural and electrophysi-ological results. Specifically, ASD subjects who were betterin detecting frame stimuli, also showed a more ‘normal’boundary-related ERP signal, i.e. more comparable to thatof controls.As explained in the Introduction section, there is

evidence from neurophysiological, neuroanatomical andmodelling studies that horizontal connections, mainlythose between cells with similar orientation tuning(Malach et al., 1993), are vital to the process of boundarydetection through lateral inhibition (Knierim and Vanessen,1992; Lamme et al., 1999). Surface segregation, on the otherhand, is associated with recurrent or feedback processingfrom higher to lower visual areas (Lamme et al., 1998a;Lamme and Roelfsema, 2000). Therefore, these data givestrong evidence for aberrant inhibition through horizontalconnections in low-level visual areas in ASD, whereasfeedback mechanisms are intact in these patients. We wouldlike to note that this inference is in agreement with thenotion of Bertone and colleagues (Bertone et al., 2005) thatlateral connectivity is atypical in ASD, which they basedon the findings of impaired orientation discrimination oftextured stimuli. It is the first time that direct evidence fora neural mechanism related to atypical visual perceptionhas been found in ASD. Subsequently, we will indicate howthe findings on lateral inhibition relate to neuropathologyand neurochemistry research, and how malfunctioning ofhorizontal connections might be related to aberrant visualperception in ASD.

Horizontal connections in ASD

An impairment in lateral inhibitionInterestingly, an imbalance between excitation and inhibi-tion in neural circuits in ASD has been suggested as a con-sequence of an imbalance between the amount of excitatoryand inhibitory neurotransmitters. Hussman (2001) proposedthat the imbalance could be due to either increased gluta-mergic signalling, leading to enhanced excitation, orto malfunctioning of the inhibitory neurotransmitterGABA, leading to a reduction in inhibition. There are twoarguments for this idea. First, pathology relating to GABAreceptors emerges as a common factor in several suspectedaetiologies of autism. Second, both disinhibition ofthe GABAergic influence and excessive stimulation of non-NMDA glutamate receptors, generate pathology whichmirrors that observed in autism (Hussman, 2001). This ideaof an enhanced excitation to inhibition ratio is supportedby others (Rubenstein and Merzenich, 2003; Cline, 2005)and Collins et al. (2006) showed, by studying GABAreceptor subunit genes, that indeed the GABAergic systemis involved in the aetiology of autism.

A second line of evidence for disturbed lateral inhibitionthrough horizontal connections comes from studies onthe neuropathology of ASD. Casanova et al. (2002, 2003,2006a, b) have shown that the minicolumn organizationin several areas of the cerebral cortex (including Brodmannareas 3, 4, 9, 17, 21 and 22) of patients with ASD is altered.Minicolumns are functional units of the brain that organizeabout 80–100 neurons with a common set of functionalproperties in cortical space. Several minicolumns (60 to 80)in turn combine into a hypercolumn, as defined by Hubeland Wiesel (1977). The minicolumnar aberrancies in ASDinclude that neurons inside a minicolumn are more

Fig. 6 (A) Activation maps of the frame^homogeneous contrast for the control group and ASD group, pooled over the three levelsof border width. (B) The graphs represent the subtraction waves frame^homogeneous for the three conditions at lateral occipitalsites (PO7, PO8, P7, P8). Activity increased with increasing border width for subjects with ASD but not for control subjects from223 to 243ms.



widely spaced, minicolumns are smaller and, given that thecerebral cortex is not smaller in ASD patients, the authorsinferred that there must be a larger total number ofminicolumns filling the cortical space. In addition, inBrodmann areas 9, 17, 21 and 22, they showed that theminicolumns contained less neuropil space. Neuropilconsists of unmyelinated fibres, in part from inhibitoryprojections of double-bouquet interneurons. Casanova et al.(2002, 2003) hypothesize that the altered structural organiza-tion of minicolumns will lead to the disturbance of theflow of information between minicolumns, diminished lateralinhibition, as well as an enhanced rate of epilepsy-seizures.Around puberty, one-third of the people with ASD will havesuffered at least two unprovoked seizures (depending on thestrictness of the diagnosis and on the level of mentalretardation, Ballaban-Gil and Tuchman, 2000).Both lines of research indicate that inhibition through

horizontal connections is impaired in ASD, in line with ourown findings. While we imply that impaired lateralinhibition is related to atypical visual perception in ASD,altered organization of minicolumns has been found inseveral different brain areas, including the pre-frontalcortex, and GABAergic inhibition plays a role throughoutthe brain. Therefore, we suggest that aberrant lateralinhibition could be a more general neurobiological deficitin ASD (see also Hussman, 2001), not only specific tovisual perception. It would be a promising direction forfuture research to study the functional integrity of theseinhibitory interactions more thoroughly to see how they arerelated to other behavioural symptoms of ASD.

Malfunctioning of horizontal connectionsAs already indicated in the Introduction section, besidesthe role of horizontal connections in the detection of figureboundaries through lateral inhibition, horizontal interactionsalso play an important role in a different aspect of percep-tion, namely grouping or binding (Polat, 1999; Roelfsema,2006). Accordingly, malfunctioning of horizontal connec-tions could also provide an explanation for other features ofatypical visual perception in ASD, namely impaired Gestaltprocessing and grouping (Brosnan et al., 2004), as well assuperior performance on the embedded figures test (seeIntroduction section, Jolliffe and Baron-Cohen, 1997; Roparand Mitchell, 2001; De Jonge et al., 2006).

Enhanced processing at occipito-parietalsites: a compensation mechanism?When we investigated the frame–homogeneous signal afterthe time window related to boundary detection only, wefound an enhanced positivity in the patient groupcompared to controls, around 225ms at occipito-parietalsides. This suggests that, probably due to diminished lateralinhibition through horizontal connections at early stages,the brain puts more effort in the correct identification of astimulus at subsequent analysis stages, eventually resulting

in normal recurrent activity back to early visual areas.At higher-level areas, the orientation discontinuity that ispresent in the scene is probably detected by neurons withlarger receptive fields (Roelfsema et al., 2002). In the ASDsubjects processing by these neurons apparently is enhancedcompared to controls, which is supported by the observa-tion that the difference between ASD and control subjectsdepends on border width: it is strongest for larger borders.We interpret this finding as a compensation mechanism.Interestingly, Belmonte and Yurgelun-Todd (2003) showedwith fMRI compensatory processing in the posteriorintraparietal sulcus in people with ASD during a visualselective attention task. The combination of disrupted earlyprocessing with compensation at higher levels may be ageneral feature of the physiology of perception in ASD.

ConclusionWe have shown a fundamental visual processing aberrancyin ASD, which is probably caused by impaired interactionsthrough horizontal connections in lower visual areas. Thisaberrancy can be compensated later in time in highercortical areas, but the exact mechanism or implication ofthis compensation is not entirely clear. Interestingly, adeficit in neuronal interactions within cortical areas in ASDhas been suggested before, stemming from different lines ofresearch such as neuropathology and neurochemistryresearch, and is apparent in different brain areas besidesthe occipital cortex. Possibly, malfunctioning of horizontalconnections is a more general neurobiological deficitunderlying several symptoms of ASD.

AcknowledgementsThe work described was supported by an InnovationalResearch Incentives grant (VIDI-scheme, 402-01-094) of theNetherlands Organization for Scientific Research (NWO)to C.K.

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a neural substrate for atypical low-level visual processing in autism

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