High-frequency oscillatory response to illusory contour in typically developing boys and boys with autism spectrum disorders

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

Available online at www.sciencedirect.com

Journal homepage: www.elsevier.com/locate/cortex

Research report

High-frequency oscillatory response to illusory contour in typically developing boys and boys with autism spectrum disorders Tatiana A. Stroganova a,c, Elena V. Orekhova b, Andrey O. Prokofyev a,c,*, Marina M. Tsetlin a,c, Vitaliy V. Gratchev d, Alexey A. Morozov e and Yuriy V. Obukhov e a

The MEG Centre, Moscow State University of Psychology and Education, Russia Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sweden c Laboratory of developmental psychogenetics, Psychological Institute of Russian Academy of Education, Moscow, Russia d Clinical Department for the Study of Adolescent Psychiatry, Mental Health Research Center of Russian Academy of Medical Sciences, Moscow, Russia e Lab 144, Institute of Radio-Engineering and Electronics, Russian Academy of Sciences, Moscow, Russia b

article info

abstract

Article history:

Illusory contour (IC) perception, a fruitful model for studying the automatic contextual

Received 23 June 2010

integration of local image features, can be used to investigate the putative impairment of

Revised 18 August 2010

such integration in children with autism spectrum disorders (ASD). We used the illusory

Accepted 22 February 2011

Kanizsa square to test how the phase-locked (PL) gamma and beta electroencephalogram

Action editor Mike Anderson

(EEG) responses of typically developing (TD) children aged 3e7 years and those with ASD

Published online 3 March 2011

were modulated by the presence of IC in the image. The PL beta and gamma activity strongly differentiated between IC and control figures in both groups of children (IC effect).

Keywords:

However, the timing, topography, and direction of the IC effect differed in TD and ASD

EEG

children. Between 40 msec and 120 msec after stimulus onset, both groups demonstrated

Kanizsa square

lower power of gamma oscillations at occipital areas in response to IC than in response to

Autism spectrum disorder

the control figure. In TD children, this relative gamma suppression was followed by rela-

Preschool children

tively higher parieto-occipital gamma and beta responses to IC within 120e270 msec after

Gamma oscillations

stimulus onset. This second stage of IC processing was absent in children with ASD. Instead, their response to IC was characterized by protracted (40e270 msec) relative reduction of gamma and beta oscillations at occipital areas. We hypothesize that children with ASD rely more heavily on lower-order processing in the primary visual areas and have atypical later stage related to higher-order processes of contour integration. ª 2011 Elsevier Srl. All rights reserved.

* Corresponding author. Laboratory of developmental psychogenetics, Psychological Institute of Russian Academy of Education, Mokhovaya 9-4, Moscow 125009, Russia. E-mail address: [email protected] (A.O. Prokofyev). 0010-9452/$ e see front matter ª 2011 Elsevier Srl. All rights reserved. doi:10.1016/j.cortex.2011.02.016

702

1.

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

Introduction

Autism is a developmental disorder of neurological origin that is currently defined by behavioral criteria, which include impairments in social interaction, impairments in verbal and nonverbal communication, and restricted interests and activities. Although not included in the formal diagnostic criteria for autism or autism spectrum disorders (ASD), abnormalities of sensory perception are observed in the great majority of children with ASD and may significantly contribute to the development of the autistic phenotype (Gerrard and Rugg, 2009). Sensory abnormalities in ASD have been most intensively investigated in the visual modality. People with ASD often have an uneven profile of visual abilities with both peaks and troughs in performance. They show deficit in tasks requiring Gestalt perception (Brosnan et al., 2004; Grinter et al., 2010) but demonstrate superior performance in a number of tasks relying on processing of local features, such as the embedded figures task (Jolliffe and Baron-Cohen, 1997), the block design subtest (Caron et al., 2006), visual search (Baldassi et al., 2009; Joseph et al., 2009) and first-order grating discrimination (Bertone et al., 2005). The mechanisms underlying these perceptual atypicalities are still debated. Frith (1989) suggested that people with ASD have weak central coherence resulting in the inability to process information in global (gestalt) form. The reduced connectivity in the distributed cortical region was suggested to be the neural basis for the weak central coherence (Just et al., 2007). Updating the theory, Happe and Frith (2006) noted that the weak central coherence may result from local processing bias rather than a genuine inability to attend to global aspects of the stimulation (Happe and Frith, 2006). Mottron et al. (2006) further argued that the local processing bias in ASD stems from over-functioning of brain areas typically involved in primary perceptual functions without any additional deficits (Caron et al., 2006; Mottron et al., 2006). Indeed, functional magnetic resonance imaging (fMRI) studies reveal atypically strong activation in visual areas during performance of various tasks requiring visual processing (Ring et al., 1999; Sahyoun et al., 2010; Soulieres et al., 2009). There is also limited evidence of the role of underconnectivity in autistic visual perception (Belmonte et al., 2009). The electroencephalographic studies have also suggested atypical neural correlates of visual perception in individuals with ASD. Vandenbroucke et al. (2008) found in adults with ASD abnormal early brain evoked responses related to visual boundary detection and contour segregation (Vandenbroucke et al., 2008). Several studies have revealed atypical modulation of the event-related potentials (ERP) by spatial frequency of visual stimulation in adults and children with ASD (Jemel et al., 2010; Milne et al., 2009; Vlamings et al., 2010). Webb et al. (2006) reported that ERP abnormalities in ASD depended on the social content (face vs object) of the visual stimulation (Webb et al., 2006). Data on brain response to illusory contours (ICs) could significantly contribute to the existing neurofunctional evidence on atypical visual perception in ASD. ICs, such as the Kanizsa square illusion, are subjectively perceived boundaries without any physical differences at the border

(Ramachandran, 1987). They provide a fruitful model for studying automatic perceptual grouping of local image features (Loffler, 2008; Seghier and Vuilleumier, 2006). Investigation of this phenomenon may shed the light on the origin of the atypical Gestalt perception in ASD. Is perception of the ICs impaired in ASD? Two existing behavioral studies in children with ASD produced contradictory results. One of them reported the impaired perception of illusory figures in children with ASD (Happe, 1996), while the other did not find such a deficit (Milne and Scope, 2008). Even if children with ASD are able to perceive ICs, the underlying neurofunctional mechanisms may differ in ASD. Studies of neural activity related to IC perception may help to reveal putative distortion of perceptual grouping processes in ASD. The perception of ICs involves activity modulations in multiple stages of the visual hierarchy [for review see (Nieder, 2002; Seghier and Vuilleumier, 2006)]. The findings suggest that processing of real and illusory contours is based on similar cortical mechanisms. Both real and illusory contours activate early stages of the monkey visual pathway (Grosof et al., 1993; Lee and Nguyen, 2001; Peterhans and von der Heydt, 1991; Sheth et al., 1996). In primary visual cortical area V1, the ICs trigger activity that differs from that elicited by the real contours (Ramsden et al., 2001). Psychophysical studies in humans further support the importance of the early areas in IC processing (Pillow and Rubin, 2002). Contrary, the human imaging studies unanimously suggest a wide network of predominantly higher order visual areas (Larsson et al., 1999; Murray et al., 2002). Based on these findings, Seghier and Vuilleumier (Seghier and Vuilleumier, 2006) concluded that processing of illusory figures engages several brain areas at early as well as intermediate and perhaps late stages in the visual hierarchy. Most likely, the perception of ICs involves a multitude of perceptual processes, some of which are instantiated in lower order areas of the visual cortex (e.g., collinearity processing), whereas others rely on higher order visual areas (e.g., amodal perceptual grouping and shape formation). Recently, the authors of this paper employed the ERP technique to investigate neural correlates of IC processing in young boys with ASD and in typically developing (TD) boys aged 3e6 years (Stroganova et al., 2007). In TD boys, the illusory Kanizsa square, compared to a control stimulus, elicited enhanced negativity of the N1 peak in the evoked response: the IC effect. This IC effect has previously been described in healthy adults and is linked to intermediate perceptual grouping (Herrmann and Bosch, 2001). Boys with ASD demonstrated a significant inverted IC effect, i.e., more positive N1 amplitude to the IC. There is convincing evidence that the effect of enhanced positivity on the evoked response is related to low-order collinearity processing (Khoe et al., 2004). We suggest that TD boys relied upon intermediate perceptual grouping processing, whereas boys with ASD differentiated between the Kanizsa square and the control figure mainly using collinearity processing mechanisms implemented in the neural circuitry of the primary visual cortex (area V1). These findings provide the first tentative neurophysiological evidence for the superior role of early visual areas in processing coherent shapes in ASD and for the possible deficit in higher-level perceptual grouping.

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

It has been shown that gamma response is also strongly related to IC processing [for review, see (Herrmann et al., 2009)]. Therefore, investigation of gamma oscillations could provide additional information on Gestalt perception in ASD. Highfrequency oscillations in the beta/gamma (approx. 15e60 Hz) range are thought to reflect an underlying temporal structure and coordination of neuron population activity. These oscillations play an important role in neural coding by assembly formation and may be associated with the binding of perceptual information [for review, see (Uhlhaas et al., 2009)]. Studies on gamma response to ICs in humans first evidenced the role of so-called induced gamma at the relatively late stage of processing (250e300 msec) (Tallon-Baudry et al., 1996). TallonBaudry et al. have shown that both illusory (i.e., Kanizsa) and real triangles are accompanied by an increase in non-[phaselocked (PL)] (i.e., induced) gamma activity compared to stimulus configurations with no triangle. The findings have led to the conclusion that the feature binding of an illusory figure is associated with an induced gamma response. Pulvermuller et al., however, argued that perceptual processes occur much earlier and that the induced gamma response is related to cognitive associative learning rather than to perceptual processes per se (Pulvermuller et al., 1999). Indeed, analogous increases in induced gamma power accompanied awareness of visual stimuli independent of whether they were correctly discriminated (Summerfield et al., 2002). In a study similar to that of Tallon-Baudry et al., Herrmann et al. reported that the gamma response was mainly related to the illusory triangle being close in shape to a target in a discrimination task rather than to the presence/absence of the illusory triangle (Herrmann et al., 1999). Moreover, Yuval-Greenberg et al. argued that the late broadband transient-induced gamma band response recorded by scalp electroencephalography reflects properties of miniature saccade dynamics rather than neuronal oscillations (Yuval-Greenberg et al., 2008). Thus, the nature of the processes modulating the induced gamma power during Gestalt perception is still a question of debate. Currently, there is a growing body of evidence showing a relationship between short latency PL betaegamma response and visual perception of coherent objects in the human brain (Spencer et al., 2004; Wu and Zhang, 2009). The phase-locked gamma band response (plGBR) appears early (with latency of approx. 40e90 msec) after stimulus onset with high phase synchrony across the trials; its source is located near early sensory areas (Herrmann et al., 2010). The strong dependency of this response on the size or spatial frequency of the visual stimulus (Busch et al., 2004) implies that the plGBR is generated during an early processing stage, which is strongly affected by physical properties of the stimulation. On the other hand, the plGBR is modulated by attention (Stefanics et al., 2004) and familiarity (Herrmann et al., 2004), suggesting involvement of topedown influences. This dual nature of plGBR makes it an ideal candidate for studying the putative imbalance in lower- and higher-level processing stages during Gestalt perception in ASD. Early PL sensory-evoked oscillations are most reliably found among the gamma oscillatory responses that make them especially suitable for searching putative abnormalities in clinical populations. Interestingly, in schizophrenia, a disorder associated with perceptual abnormalities similar to ASD, plGBR is

703

strongly reduced and does not exhibit the normal enhancement during Gestalt perception (Spencer et al., 2004). Converging evidence suggests that properties of highfrequency gamma and beta oscillations are altered in ASD and related to aberrant brain development in this disorder. The excess of gamma band oscillations during sustained visual attention in children with ASD is directly related to the degree of developmental delay (Orekhova et al., 2007). The stimulus-induced gamma oscillations in ASD are reduced (Grice et al., 2001) or abnormally modulated by the spatial frequency of simple visual stimuli (Milne et al., 2009). These considerations suggest possible alteration of the PL gamma response to ICs in ASD as well. In this study, we analyzed PL gamma and beta responses to the IC in children with ASD and in TD children. Although the Kanizsa illusion effect on induced gamma response is already present in infants (Csibra et al., 2000), the PL high-frequency response related to IC processing was not investigated in pediatric samples. Therefore, we had two specific aims: (1) to explore PL gamma and beta responses to the Kanizsa square in TD children and (2) to look for their possible abnormalities in ASD children. We used an experimental procedure similar to that described by Spencer et al. (Spencer et al., 2004) but with the two alterations. First, stimuli spanned a visual angle of 9
instead of approx. 7.5
. As the magnitude of plGBR is directly related to stimulus size (Busch et al., 2004), the larger stimuli may result in more reliable plGBR. Moreover, taking into consideration the relative disadvantage in processing low spatial frequencies in autism (Grinter et al., 2010), the putative gamma abnormalities could be more readily detected in ASD using large-size stimuli. Second, in Spencer et al.’s study, subjects were asked to respond by pressing a button according to presence of the illusory square (Spencer et al., 2004); in contrast, we used a passive viewing task. The children’s attention toward the stimulus display was maintained by short animation movies interspersed with stimuli presentation. This design allows us to analyze the IC effect even in young and developmentally delayed children with ASD and to avoid the potentially confounding influence of decision-making processes on gamma response.

2.

Methods

2.1.

Participants

Two groups of children participated in this study: 23 boys with ASD aged 3e7 years [mean age ¼ 60.4 months, standard deviation (SD) ¼ 13.9] and 23 age-matched TD boys (mean age ¼ 61.4 months, SD ¼ 14.7). Of these 46 subjects, 18 boys with ASD and 16 TD boys were involved in a previous N1 study (Stroganova et al., 2007). Boys with ASD were recruited from local developmental disabilities departments and from psychiatry clinics. The control group comprised boys attending regular schools or daycare centers. The diagnosis of ASD (autism in 18 boys and Asperger’s disorder in five boys) was performed by an experienced clinician based in the Diagnostic and Statistical Manual of Mental Disorder-IV-TR criteria and confirmed by a clinical psychologist using the Childhood Autism Rating Scale (Schopler et al., 1986). None of

704

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

the boys with ASD had epilepsy, and no other neurological comorbidity was found. All children were medication-free for at least 2 months at the time of investigation. Developmental quotients in boys with ASD were evaluated by mental-age-appropriate tests. The Psychoeducational Profile (Schopler et al., 1990) was used in 10 boys (young and/or without speech). The Kaufman Assessment Battery for Children (Kaufman and Kaufman, 1983) was used for the remaining 13 boys with ASD. Given that different tests were used to assess IQ and mental age, we recalculated the relative developmental delay on the basis of psycho-educational profile mental age or mental age derived from IQ measurement according to the following formula: % delay ¼ 100 (Mental Age/Chronological Age). In the ASD group, the mean developmental delay was 12.5% (SD ¼ 18.6, range 0e60%). The study was approved by the ethical committee of the Moscow State University of Psychology and Education. Informed consent was obtained from the parents of all children.

2.2.

Stimuli

The stimuli, four symmetrical black inducer disks, were presented with Presentation software (Neurobehavioral Systems Inc., Albany, California, USA) on a 17-inch computer monitor positioned 50 cm in front of a participant. Each disk had one missing 90
segment with the length of inducing edges equal to the radius. These disks were arranged to produce the illusory percept of the Kanizsa square (134 trials) or not to yield the illusory percept (134 trials) (Fig. 1). No instruction was given to the children. To maintain their attention on the computer screen, the test stimuli were interspersed with 67 short (3e6 sec) animated movies. The same set of movies was presented to each participant. All stimuli were presented pseudorandomly on a PC monitor on a white background. Each trial began by presenting a fixation cross in the center of the screen, after which one of the stimuli appeared. The stimulus duration was 500 msec, and interstimulus intervals varied randomly between 500 msec and 1000 msec. Both Kanizsa and control stimuli subtended a visual angle of 9
 9
including inducer disks, while the illusory square (Fig. 1) subtended 5.4
. The ratio of the radius of the inducer disks and the side-length of the illusory square was 1/3.

2.3.

Procedure

An electroencephalogram (EEG) was recorded using a 32channel SynAmps system (Neuroscan, El Paso, Texas, USA) with a linked ears reference and .5e100 Hz band-pass filter at a sample rate of 500 Hz. Four electrooculogram (EOG)

Fig. 1 e Kanizsa and control stimuli.

electrodes were used to record horizontal and vertical eye movements; EOG electrodes were placed at the outer canti of the eyes and above and below the left eye. Electrode impedance was kept below 10 kU for all channels. The data were post hoc digitally filtered with 1 Hz high-pass and 48e52 Hz band-stop Butterworth filters. For filtering, Matlab routine ‘filtfilt’ (Matlab 6.5, the MathWorks Inc.) was used. This routine first applies a two-order Butterworth filter forward and then again backward in order to ensure that phase delays introduced by the filter are nullified. EEG and EOG signals were stored on a hard disk synchronously with the video records. The behavior of the participants was coded offline to identify epochs when they were not attending to the stimuli. The trials in which a participant did not fixate on the visual display and EEG epochs with movement artifacts and extreme signal amplitudes (>100 mV) were excluded from further analysis. There were significant differences between boys with ASD and TD boys in the number of artifact-free EEG epochs sampled for both Kanizsa (ASD: mean 77.6; SD ¼ 29.7; TD: 107.2; SD ¼ 5.1) and control (ASD: mean 77.1; SD ¼ 29.3; TD: 108.1; SD ¼ 4.7) trials. Therefore, the number of trials was taken as the covariate in all analyses of variance (ANOVAs) where ASD and TD groups were compared directly. EOG artifact correction was performed using a regression approach implemented in SCAN 4.2 software (Scan 4.2 System, El Paso, Texas, USA).

2.4.

Analysis of stimulus-evoked high-frequency activity

The data epochs comprising from 500 msec pre- to 1000 msec post-stimulus EEG were extracted. The epoched data were linearly de-trended. To derive time-varying power, we applied a wavelet-based decomposition of the EEG data with complex Morlet’s wavelets j(t):   1 jðtÞ ¼ pffiffiffiffiffiffiffiffi expð2ipFc tÞexp  t2 =Fb pFb where t is time; Fb is a bandwidth parameter; and Fc is a wavelet center frequency. The convolution of the signal s(t) by Morlet-wavelet results in the complex-valued wavelet-coefficients W(t):     ZN 1 xt 1 xt sðxÞ$J ¼ pffiffiffi dx WðtÞ ¼ pffiffiffisðxÞ5J a a a a N

where a ¼ Fc =f0 , and f0 is the center frequency of a given frequency band. To quantify the power of the evoked response, PL to the stimulus onset, the complex wavelet transformed data from each single trial were averaged. From this complex average, the squared modulus was taken as a measure of the power of the PL response. The time-frequency plots of the PL power were obtained with a frequency ranging from 1 Hz to 50 Hz in 1-Hz step (Fc ¼ 1.0, Fb ¼ 1.0). In order to visualize the grand average evoked responses in the time-frequency domain, we plotted the time course of PL power for Kanizsa and control stimuli from 100 msec to 400 msec relative to the stimulus onset. Within this time interval, the minemax normalization of power at each 1-Hz step was applied across time points and

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

experimental conditions, separately for each electrode location. Minemax normalization rather than a more traditional signal-to-noise computation was chosen in order to yield a measure that allows visualization of a potentially noisy baseline. Note that this approach does not allow for direct between-group comparison in the response power; rather, it

705

favors visualization of the between-group difference in the relative strength of the response to IC and control figure. Fig. 2 shows electrodes Oz and P4, which show the most representative effects for the occipital and parietal electrode groups. To quantify plGBR/plBBR, we used coefficients Fc ¼ .094, Fb ¼ 32.054 for the beta frequency range (center frequency

Fig. 2 e Grand average time-frequency plots of PL oscillatory response to illusory Kanizsa square and control stimulus in TD boys and boys with ASD. Vertical axis: frequency (Hz). Horizontal axis: time in seconds relative to stimulus onset. Vertical line marks stimulus onset. Power values for each 1-Hz band from L100 msec to 400 msec were minemax normalized across stimuli types for each scalp area and separately for the TD and ASD group.

706

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

f0 ¼ 17.75 Hz) and Fc ¼ .106, Fb ¼ 31.483 for the gamma frequency range (center frequency f0 ¼ 36.5 Hz) that provides pffiffiffi 2 times decrease of the magnitude of the frequency response at the upper and lower boundaries of the frequency band (11.5e24 Hz for beta and 25e48 Hz for gamma). In the beta band this provides frequency resolution of 12.5 Hz and time resolution of 64 msec; in the gamma band, e 23 Hz and 34 msec, respectively. Time courses of plGBR and plBBR responses are plotted in Fig. 3. For the statistical analysis, the PL power values within the gamma and beta bands were obtained for consecutive time windows of 38 msec (gamma) or 72 msec (beta) covering a time period of approximately 0e400 msec after the stimulus onset. Specifically, values of PL gamma power were centered at the post-stimulus time points 19, 58, 96, 135, 173, 212, 250, 288, 327, and 365 msec and values of PL power at the poststimulus time points 36, 107, 179, 250, 321, and 393 msec. This selection allows statistical independence of adjacent power

pffiffiffiffiffiffi values, i.e., the 10 times decrease in amplitude of the impulse response on the previous and subsequent adjacent time points. The baseline interval was chosen from 200 msec to 100 msec prior to stimulus onset in order to avoid signal leakage into the baseline. Based on previous evidence that the gamma-band responses during processing of simple geometric shapes are most pronounced over posterior visual areas (Busch et al., 2004), we included occipital and parietal regions of interest (ROIs: O1, O2, Oz, P3, P4, and Pz) in the analysis. Electrodes P7 and P8 were excluded due to relatively high contamination by myogenic artifacts. As the first step of statistical analysis, repeated ANOVA measures of the plGBR/plBBR were performed separately for the TD and ASD group; then, the groups were compared directly. The repeated measures factors were Stimulus (Kanizsa and control stimuli), Time (for 10 levels for plGBR and six levels for plBBR), Area (occipital, parietal) and Laterality

Fig. 3 e Time courses of the grand average (a) plGBR and (b) plBBR power in TD boys and boys with ASD. Blue line: control stimulus. Red line: Kanizsa figure. Vertical line marks stimulus onset. A different scale was used for the plGBR and plBBR at Oz and P4 electrodes in order to improve visibility of the time course of plGBR at the parietal electrodes. Mean log10 amplitude maps illustrate topographic distribution of the plGBR and plBBR at their corresponding amplitude maxima (91 msec and 139 msec) for each stimulus and subject group.

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

(left, right, and midline). The design of the ANOVA for the between-group comparison is described in the Results section. GreenhouseeGeisser correction was used when appropriate. Uncorrected degrees of freedom and corrected p values are reported. Post-hoc comparisons for significant ANOVA effects were performed using a two-tailed Wilcoxon sign rank test for matched samples with Bonferroni correction. Visual inspection of the grand average plGBR in ASD and TD children (Fig. 3) shows that both groups had two peaks of the response. In order to avoid loss of statistical power due to multiple post-hoc comparisons when exploring ANOVA interaction effects, including factor time, we averaged plGBR/ plBBR within two time intervals roughly corresponding to these two plGBR peaks. The early period was between approximately 40 msec and 120 msec for plGBR (corresponding to the average value of plGBR at 58 msec and 96 msec time points) and 70e140 msec for plBBR (107 msec point). The late period was approximately 120e270 msec for plGBR (averaged across 135 msec, 173 msec, 212 msec and 250 msec time points). For the plBBR the late periods were approximately 140e210 msec (179 msec point) and, for some types of analyses, 210e280 msec (250 msec point) The same time windows were applied for the subsequent non-parametric analysis. The data were largely non-normally distributed in accordance with the notion of violation of normality assumption for gamma-band power distribution (Lachaux et al., 2005). Violation of the normality assumption may lead to both unjustified acceptance and rejection of the null hypothesis (Nikulin and Brismar, 2006). In order to verify significant ANOVA interaction effects, the non-parametric multi-way analysis based on step-wise subtraction technique (Morozov et al., 2007; Stroganova et al., 2007) was performed. We further refer to this method as nonparametric analysis. Fig. 6 demonstrates application of this nonparametric analysis for testing Group X Stimulus X Area interaction for the average plGBR in the 40e120 msec range. First, for the difference (Δ1) between the Kanizsa and the control, plGBR power was calculated separately at the baseline and within the post-stimulus window of interest for each of six ROIs and for each subject. This resulted in 552 Δ1 values (six ROI X 2 temporal windows X 46 subjects). Next, the difference in Δ1 between baseline and the post-stimulus temporal window of interest (Δ2) was computed as 276 Δ2. Then, the difference between occipital and parietal areas in the Δ2 was computed, which yielded 46 values of Δ3, one for each subject. Finally, we formed 23 ASD-control pairs matched by chronological age. To estimate the significance of the three-way interaction effect, we compared the Δ3 values of each boy with ASD spectrum disorder to those of his age-matched partner by means of a one-tailed Wilcoxon matched-pairs signed rank test. To test more simple interactions, e.g., GroupXStimulus, the same subtraction technique was applied. However, in order to exclude the effects of area and laterality, we pooled together the plGBR values recorded from different topographical sites and then applied the above-described subtraction technique. As the data from different electrode locations are not statistically independent, this procedure might have increased the probability of Type I error. To reduce dimensionality of the data and decrease probability of the error, we applied

707

principal component analysis (PCA) and computed the factor scores of each subject in the Kanizsa or control condition on the first principal component explaining the maximum portion of variance. Then, we computed the difference in these factor scores between the Kanizsa and control conditions for each subject (Δ1), formed 23 ASD-control pairs, and submitted this measure to a one-tailed Wilcoxon sign rank test for the matched samples. Statistics for both types of comparisons are reported in the Results section.

2.5.

Verification of experimental paradigm

To compare our experimental paradigm with that of Spencer and colleagues (Spencer et al., 2004) and ensure that our paradigm modification is suitable to investigate the IC effect on the plGBR, we tested this paradigm in 15 healthy adults. The results were compatible with those obtained by Spencer (Spencer et al., 2004), who used active rather than passive viewing and slightly smaller (7.5
) stimuli than in our study (see online Supplementary materials).

3.

Results

3.1.

The plGBR in TD boys

Time courses of plGBR to Kanizsa and control stimuli in TD children are shown in Figs. 2 and 3. Repeated measures ANOVA showed highly significant effects of Time [F(9,198) ¼ 14.91; ε ¼ .35; p < .0001] and Area [F(1,22) ¼ 22.25; p < .0001]. For both stimulus types, a plGBR was found within 270 msec after the stimulus onset with a strong peak at about 100 msec post-stimulus and response maximum at the occipital electrodes. ANOVA also yielded significant Stimulus X Time interaction [F(9, 198) ¼ 2.77; ε ¼ .49; p < .03], which can be better explained within the three-way interaction Stimulus X Time X Area [F(9,198) ¼ 2.91; ε ¼ .40; p < .03]. This interaction showed significant changes in the IC effect depending on the time window and area of registration. Furthermore, we limited analysis to two time intervals: 40e120 msec and 120e270 msec (Fig. 4). These time windows were chosen on the basis of the grand mean averages in such a way that they roughly corresponded to the two peaks of the plGBR response (see Fig. 3). Within the earlier 40e120 msec window, nonparametric analysis yielded significant effects of Stimulus for the mean plGBR power collapsed across six electrodes (sign rank: p < .0001) and when the redundancy of the power measurements was eliminated with PCA (sign rank: p < .04). This effect manifested in higher plGBR for the control compared to the IC stimulus (inverted IC effect). Stimulus X Area interaction was also significant (sign rank for collapsed power values: p < .0001, for PCA factor scores: p < .004), due to much greater stimulus effect on plGBR at occipital than at parietal electrodes sites (sign rank for collapsed power values at occipital sites: p < .0001; at parietal sites: p ¼ .2). Within the 120e270 msec window, nonparametric analysis yielded significant Stimulus effect on gamma power values (sign rank: p < .0001 for collapsed power values; p < .006 for PCA factor scores), which was opposite of that observed for

708

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

Fig. 4 e Mean difference values of PL gamma power between Kanizsa and control stimuli (IC effect) in TD group. Latticed bars: pre-stimulus values averaged from L200 msec to L100 msec before the stimulus onset. Gray bars: post-stimulus values averaged 40e120 msec (left) and 120e270 msec (right) after stimulus onset. Positive/ negative values correspond to greater/lower mean plGBR power, respectively, during Kanizsa stimulus as compared to control. Upper row: IC effect averaged across six electrode locations. Bottom row: Stimulus X Area interaction, comparison of IC effect averaged separately for occipital and parietal electrodes. Whiskers denote .95 confidence intervals. p-values: * p < .05; ** p < .005; *** p < .0005; **** p < .00005.

the early plGBR. This later direct IC effect was equally pronounced over occipital and parietal electrode sites (sign rank: p < .01 for collapsed power values at occipital sites; p < .01 at parietal sites; Stimulus X Area interaction p ¼ .5). Thus, in TD children, the IC effect clearly exhibited two qualitatively different time phases. The first short-latency and spatially narrowly localized phase reflected greater occipital plGBR for the control than for the Kanizsa stimulus (inverted IC effect). The later phase was characterized by greater and more widespread occipito-parietal plGBR for the Kanizsa figure (direct IC effect).

3.2.

The plGBR in boys with ASD

ANOVA results yielded highly significant effects of Time [F(9,198) ¼ 12.30; ε ¼ .36; p < .0001], Area [F(1,22) ¼ 13.00; p < .002], and their interaction [F(9,198) ¼ 2.94; ε ¼ .36; p < .04]. Inspection of the respective means (Figs. 2 and 3) shows that in children with ASD, plGBR to both visual stimuli was maximal at around 100 msec after the stimulus onset with strong preponderance over the occipital electrodes. There was also a significant main effect of Stimulus [F(1,22) ¼ 9.10; p < .007] with larger plGBR for the control than for Kanizsa stimulus. Unlike that of TD children, this stimulus effect

Fig. 5 e Mean difference in PL gamma power between Kanizsa and control stimuli (IC effect) in ASD group. Latticed bars: pre-stimulus values averaged from L200 msec to L100 msec before stimulus onset. Gray bars: post-stimulus values averaged from 40 msec to 250 msec. Left: IC effect averaged across six electrode locations. Right: Stimulus X Area interaction comparison of IC effects averaged separately for occipital and parietal electrodes. Other designations are the same as in Fig. 4.

was not modulated by the time interval, as evidenced by nonsignificant Stimulus X Time and Stimulus X Time X Area interactions ( p > .2 for both). Additionally, as shown in Fig. 5, ANOVA yielded a Stimulus X Area interaction indicating a greater inverted IC effect at the occipital electrode sites [F(1,22) ¼ 4.38; p < .05]. Both ANOVA effects were confirmed by non-parametric comparisons: regarding the stimulus main effect within the 40e270 msec post-stimulus time interval, sign rank: p < .007 for collapsed power values, and p < .02 for PCA factor scores; regarding Stimulus X Area interaction, sign rank: p < .02 for collapsed power values, and p < .1 for PCA factor scores. Thus, unlike TD peers, children with ASD displayed a protracted inverted IC effect during the entire time window of the plGBR (40e270 msec post-stimulus). No evidence of a direct IC effect similar to that observed in TD children was found in children with ASD.

3.3. Comparison of plGBR IC effects in boys with ASD and TD boys Group differences in the IC effect on plGBR were analyzed for the 40e270 msec interval, during which reliable plGBR was observed. We performed analysis of covariance (ANCOVA) with an independent factor Group and repeated measures factors Stimulus (Kanizsa, control), Time (poststimulus time points 58, 96, 135, 173, 212 and 250 msec), Area (occipital, parietal) and Laterality (left, right and midline). The number of sampled epochs, averaged across Kanizsa and control stimuli presentations, was taken as a covariate. The analysis was repeated using non-parametric analysis. Both analyses convergently yielded significant Group X Stimulus interaction [ANCOVA: F(1,43) ¼ 4.13; p < .05; sign rank: p < .006 for the mean power values collapsed across electrode sites and p < .013 for PCA scores]. The covariate did not significantly interact with any effects that included the

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

repeated measure factor Stimulus or its interaction with other factors. The post-hoc non-parametric analysis showed that the IC effect on plGBR mean power in the 40e270 msec interval was highly significant for the ASD group ( p < .0009 for collapsed mean power values; p < .03 for PCA scores) but not for the TD group. The previous analysis constrained to the TD group revealed two qualitatively different IC effects in the 40e120 msec and 120e270 msec time windows. Therefore, we compared the TD and ASD groups separately in these two intervals using nonparametric analysis (Fig. 6). For the early phase of the plGBR, no between-group difference in IC effect was found, as evidenced by nonsignificant Group X Stimulus interaction (sign rank: p ¼ .2 for collapsed mean power values, p ¼ .5 for PCA scores). According to the sign rank test both groups displayed significantly greater plGBR to control than to Kanizsa stimulus (TD: p < .0001 for collapsed mean power values, p < .017 for PCA scores; ASD: p < .0002 for collapsed mean power values, p < .015 for PCA scores). For the late plGBR (120e270 msec), the nonparametric test yielded highly significant Group X Stimulus interaction (sign rank: p < .0002 for collapsed mean power values, p < .003 for PCA scores). During this time interval, the IC perception had opposite effects on the plGBR power in TD boys and in boys with ASD (Fig. 6). The TD boys demonstrated greater plGBR in response to the Kanizsa figure as opposed to the control (sign rank: p < .0001 for collapsed mean power values and p < .006 for PCA scores), while boys with ASD showed higher plGBR response to the control stimulus (sign rank: p < .03 for collapsed mean power values and p ¼ .1 for PCA scores). Thus, only TD boys had both early inverted and later direct phases of the IC effect. As shown in Fig. 3, the ASD boys displayed the inverted IC effect during the entire period of significant plGBR (40e270 msec).

3.4.

709

p < .0001]. The inspection of the respective means showed that the plBBR was much greater at the occipital areas compared to the parietal areas with clear predominance at the midline electrode site (Oz), where it reached maximum within the 70e140 msec time window (Figs. 3b and 7). In the parietal area, plBBR was higher over lateral electrodes (P3; P4) than at midline ones (Pz) and reached maximum later (140e210 msec) than at the occipital sites. The plBBR returned to the baseline level after 300 msec post-stimulus. The Stimulus factor significantly interacted with the topographical factors [Stimulus X Area X Time [F(5,110) ¼ 4.16, ε ¼ .48, p < .017; Stimulus X Area X Laterality X Time: F(10, 220) ¼ 2.19, ε ¼ .45, p < .07]. The main effect of the Stimulus across the entire 400 msec post-stimulus interval and the six electrodes approached the significance level [F(1,22) ¼ 3.29; p < .09], reflecting greater power of plBBR under IC than under the control condition (Figs. 3b and 7). Non-parametric analysis of the IC effect in the time interval between 0 msec and 270 msec yielded significant Stimulus main effect (sign rank: p < .001) mainly due to highly significant increase of PL beta oscillation during Kanizsa compared to control stimulus within the 140e210 msec time window (sign rank: p < .0001) and subsequent 210e280 msec time window (sign rank: p < .04). Nonparametric analysis of the PCA factor scores across the 0e280 msec post-stimulus interval confirmed the presence of this IC effect (sign rank: p < .02). This direct IC effect was evident at both occipital and parietal areas, although it started earlier at parietal electrodes (70e140 msec: p < .02 for parietal and p < .5 for occipital sites) than at occipital electrodes (140e210 msec: p < .0008 for parietal and p < .0001 for occipital sites). Thus, in TD boys, the Kanizsa stimulus elicited higher plBBR at the occipital and parietal electrode sites compared to the control stimulus, mainly within 140e210 msec after stimulus onset, although this direct IC effect appeared earlier at parietal electrodes.

The plBBR in TD boys

Time course of the plBBR to Kanizsa and control stimuli in TD children is shown in Fig. 3. Repeated ANOVA measures showed significant effects of Time [F(5,110) ¼ 16.56; ε ¼ .47; p < .0001], Area [F(1,22) ¼ 9.17; p < .007], and their interactions with Laterality [Area X Laterality X Time: F(10,220) ¼ 9.84, ε ¼ .41,

Fig. 6 e Difference in IC effect on plGBR power between ASD and TD groups. IC effect averaged across six electrodes locations. Left: post-stimulus time interval of 40e120 msec. Right: post-stimulus time interval of 120e270 msec. Other designations are the same as in Fig. 4.

3.5.

The plBBR in boys with ASD

The time course and topography of plBBR in ASD boys were roughly similar to those in TD children (Fig. 7). ANOVA showed significant interaction Area X Laterality X Time [F(10,220) ¼ 3.89, ε ¼ .43, p < .005] reflecting the greatest plBBR within 140e280 msec after the stimulus onset at the midline electrode sites. IC perception modulated this plBBR [Stimulus X Time interaction: F(5,110) ¼ 3,38; ε ¼ .41, p < .05]. Unlike in TD boys, however, no interactions between stimulus type and topographical factors were observed. To further analyze the Stimulus X Time effect, we performed non-parametric analysis in two consequent plBBR time windows, 70e140 msec and 140e210 msec, as shown in Fig. 7. The stimulus effect was significant only for the 70e140 msec time window, and the control stimulus elicited greater plBBR than Kanizsa figure (sign rank: p < .0001 for collapsed power values and p < .002 for PCA factor scores). Within this time window, the inverted IC effect was most pronounced at the occipital electrode sites (sign rank: p < .0001) and did not reach the significance level at the parietal electrodes.

710

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

Fig. 7 e IC effect on plBBR power in TD group (left), ASD group (middle) and between groups (right). IC effect is averaged across six electrode locations. Upper row: post-stimulus time interval of 40e120 msec. Lower row: post-stimulus time interval of 120e270 msec. Other designations are the same as in Fig. 4.

3.6. Comparison of the IC effect on plBBR in boys with ASD and TD boys According to ANCOVA, the main effect of Group and its interaction with repeated-measures factors (i.e., Stimulus, Time, Area, and Laterality) were not significant. However, the nonparametric analysis performed separately for two time windows (70e140 msec and 140e210 msec) revealed significant Group X Stimulus interaction for both periods. As evident from Fig. 7, only boys with ASD showed a significant inverted IC effect in the earlier time window; in the later time window, only TD boys demonstrated a direct IC effect. In other words, in boys with ASD, the effect of IC on the PL beta response was quite different from that observed in their TD peers. Unlike TD boys, boys with ASD showed an inverted occipital IC effect during the early phase of the plBBR (70e140 msec) but failed to show the direct IC effect during its later phase (140e210 msec).

3.7. age

Correlations of inverted and direct IC effects with

The age of our subjects varied from 3 years to 7 years. To see how the direct and inverted IC effects vary with age, we performed Spearman rank order correlation analysis separately for the ASD and TD groups. For this analysis, we chose the time, frequency band (plGBR or plBBR), and topographical maxima for each effect. For the inverse IC effect, dependency on age was assessed for the plGBR in the occipital midline

region (Oz) in the two-wavelet windows centered at 96 msec and 135 msec. The direct IC effect of age on plBBR was tested in the left parietal region (P3) for the 179 msec-centered window. At each maximum, the IC effects were calculated as the absolute difference in high-frequency power between the Kanizsa and control stimuli. The inverted IC effect (Kanizsa < control) correlated with age only in boys with ASD and only at 96 msec (R ¼ .54, p < .01). This means that the inverted IC effect in ASD boys increased with age. The direct IC effect (Kanizsa > control, 179 msec) correlated with age only in TD boys (R ¼ .56, p < .01). This correlation reflects an increase of this effect with age in the TD population.

4.

Discussion

This study investigates how IC processing is reflected in PL gamma and beta oscillations in TD children and children with ASD. We found that PL beta and gamma-band activity differentiated between IC and non-illusory stimuli in both ASD and TD boys. The high-frequency responses to IC were qualitatively different in the two groups. In TD boys, two distinct phases of PL high frequency response to IC were observed approximately corresponding to 40e120 msec and 120e270 msec after stimulus onset. Compared to the control stimulus, the IC evoked higher plGBR and plBBR in the later time window (120e270 msec, direct IC effect) but lower plGBR in the earlier window (40e120 msec, inverted IC effect). Boys with

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

ASD demonstrated an abnormally prolonged inverted IC effect for both time windows but lacked the direct IC effect. Developmental data on visual gamma oscillations are scarce and completely lacking for the early childhood period. Therefore, we first address general characteristics of the PL high-frequency EEG response in the pediatric population. Then, we discuss possible mechanisms underlying temporal dynamics of high-frequency response to IC in typical development and ASD.

4.1. Visual PL gamma and beta responses in TD and ASD boys We observed distinct PL gamma and beta responses to visual sensory stimulation in TD pre-school children aged 3e7 years. While the absolute spectral power of the occipital gammaband response in children was small (<1 mkV2), it was nevertheless comparable to or even larger than that reported for adults [e.g., (Frund et al., 2007); see also Supplementary materials]. Moreover, it has been reliably provoked by visual stimulation, suggesting a high signal-to-noise ratio (Fig. 3). The visually evoked high-frequency oscillations were strongest at the occipital electrodes, started shortly after stimulus onset, and were sustained for more than 200 msec with power peaking around 100 msec (Fig. 3). These results are consistent with the findings from adults and older children, both showing occipital evoked gamma activity in response to visual stimulation within a time range of about 100 msec after the stimulus onset (Bottger et al., 2002; Busch et al., 2004; Senkowski and Herrmann, 2002; Werkle-Bergner et al., 2009). It is generally acknowledged that stimulus-related highfrequency EEG oscillations reflect activation of the cortical areas. These inhibition-based oscillations occur within a population of interconnected excitatory and inhibitory neurons and require for their expression excitatory drive to the cortical networks (Uhlhaas et al., 2009). For the evoked gamma oscillations to be detected in the surface EEG, the stimulus should activate a large enough cortical network of synchronized neural assemblies. The larger stimuli are likely to activate a proportionally larger surface of the early visual cortices and may increase likelihood of detection of the gamma response in surface EEG (Busch et al., 2004; Hoogenboom et al., 2006; Werkle-Bergner et al., 2009). It is likely that the large size of the visual stimuli (9
visual arc) was an important factor that allowed us to record highly reliable gamma response in our pediatric subjects. Interestingly, ASD and TD children did not differ greatly in the magnitude of the phased-locked visual gamma and beta responses to relatively simple visual stimuli. It has been previously suggested that the abnormally high excitability of cortical and subcortical structures in ASD may result in the formation of noisy and unstable cortical networks (Rubenstein and Merzenich, 2003). The hyper-excited and spatially broad but disorganized cortical assemblies might produce either increased PL gamma response to external stimulation or, conversely, decreased response due to poorly synchronized volley of signals from such networks. Our current data support neither of these two possibilities. There was only a statistical tendency ( p ¼ .15) toward elevated occipital gamma response amplitude in ASD subjects.

711

This result is in accordance with the work of Milne et al. (2009), who found in ASD subjects a normal magnitude of the total (PL plus non-PL) event-related gamma increase in response to simple visual stimuli in the striate or near striate cortex. As it was the case for the PL gamma in our study, Milne et al. observed reliable gamma response during 50e200 msec after stimulus onset. Contrary to these findings, Brown et al. reported in mildly retarded control, but not in ASD subjects suppression of total gamma power in 0e400 msec post-stimulus interval in response to Kanizsa figures (Brown et al., 2005). The difference between our results and that of Brown et al. may be due to differences in stimuli design, methods of data analysis, and control group used (TD controls in our study and in the study of Milne et al, and mildly retarded controls in the study of Brown et al.),. The other important factors could be the different number of subjects (23 ASD subjects in our study, 20 in the study of Milne et. al, and six in the study of Brown et al) and different signal-to-noise ratios (minimal number of trials was 40 in our study, 54 in the study of Milne et al, and seven in the study of Brown et al.).

4.2.

Early stage of IC processing: the inverted IC effect

In both TD and ASD children, the PL gamma response differentiated the Kanizsa and control stimuli within the first 40e120 msec of stimulus processing. This early stage of the IC effect was expressed as the relative suppression of evoked gamma power in response to IC and was evident exclusively at the occipital electrodes (Figs. 3a and 4). This inverted IC effect (higher gamma power in response to control than to Kanizsa stimulus) has not been previously reported in human studies [for review, see (Seghier and Vuilleumier, 2006)]. On the other hand, our results are in agreement with the studies of macaque monkeys that investigated the IC effect using optical imaging and single unit recording from primary visual cortex V1 (Ramsden et al., 2001). Ramsden et al. found that illusory responsive cells in primate V1 showed an orientation-specific response to the abutting line grating IC. The authors reported that the illusory responsive cells exhibited the strongest responses to the preferred orientation of the real line and the weakest responses to the illusory line of the same orientation (the activation reversal effect). The illusory line resulted in discharge suppression relative to the mean spontaneous firing rates during the baseline period. Notable for comparison with our data, this IC activation reversal in monkey V1 was observed both at the single-cell level and in the population response detected by optical imaging maps, suggesting the possibility of detecting this phenomenon in the scalp-recorded EEG. A similar suppressive effect of IC on monkey V1 cell’s discharge rate was described for a Kanizsa-like illusory moving bar (Peterhans and von der Heydt, 1989). Although the exact neurophysiological mechanism of the V1 suppression effect is unknown, “de-emphasis of IC orientation in V1 may be an important signal of contour identity and may, together with illusory signal from V2, provide a unique signature for IC representation” (Ramsden et al., 2001). Thus, at this low-level processing stage, the gaps in the collinear lines of the IC are emphasized rather than filled. This stage may be essential for

712

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

differentiation between real and illusory contours in the visual system. Most likely, the same mechanism exists in the human brain and may contribute to the local occipital inverted IC effect observed in the present study. Given the animal results, we suggest that it is the suppression of neural discharges of the cell populations in V1 that responded to the gap in collinear lines in the Kanizsa figure, which might have led to a short-lasting relative reduction in occipital gamma power in our pediatric subjects. As the control figure did not contain such illusory lines, the neural response of these orientationspecific cell populations may remain at the greater spontaneous discharge rate. Why has this inverted IC effect never been described in adults? We believe that the most probable explanation is the age-related differences in the relative activation of lower- and higher-order cortical areas provoked by visual stimuli. In both ASD and TD children, there was a sharp reduction in the power of the PL beta and gamma responses from the occipital to parietal and more anterior scalp areas (Fig. 3). This topographical gradient was much greater than that observed in adults [(Frund et al., 2007); see also Supplementary materials], suggesting the relatively small activation of higher-order visual areas in young children. Findings from the developmental fMRI study in monkeys support this hypothesis (Kourtzi et al., 2006). The authors found the delayed developmental onset of BOLD responses in extra-striate visual areas (MT/V5, V4) compared to the V1. The immaturity of these extra-striate areas and the weakness of its feedback signaling to V1 may favor detection of the inverted illusory-specific response characteristic of early local activity in the striate cortex. Although differences in the early inverted IC effect in the TD and ASD groups did not reach statistical significance in the case of plGBR, the between-group difference was significant in case of plBBR. In the beta band, ASD children demonstrated the inverted IC effect mimicking that in the gamma range, as it was exclusively occipital and spanned the time window of the greatest inverted gamma IC effect. It is likely that the PL gamma and beta responses to IC in ASD children may reflect basically the same phenomenon, that is, beta reflecting activation of relatively more dispersed neural populations within the visual cortex (Kopell et al., 2000; Traub et al., 1999). The presence of the inverted IC effect in children with ASD covering an atypically wide frequency range is well in line with the hypothesis of enhanced processing in early visual areas in this group of disorders (Mottron et al., 2006). Interestingly, the magnitude of the occipital inverted IC effect in children with ASD increased with age, suggesting that they may rely on this low-level stage of visual processing even more over the course of development from 3 years to 7 years of age.

4.3.

Later stage of IC processing: the direct IC effect

The direct IC effect, increased gamma and beta responses to IC stimulus at the occipital and parietal electrodes, was observed during the later phase (120e270 msec after stimulus). This processing stage was reliably observed only in the TD children, as shown in Fig. 3. The direct IC effect has been previously described in adults, mainly for non-PL gamma oscillations with longer latency (approx. 300 msec post-stimulus) (Tallon-

Baudry et al., 1996). The absence of the IC effect for PL highfrequency oscillations has been frequently reported in line with the opinion that PL gamma activity is mainly related to low-level sensory processing (Karakas and Basar, 1998). Notably, recent studies have found an effect of IC perception on early gamma band oscillations in typical adults, suggesting a relation between early PL gamma and visual featurebinding processes (Spencer et al., 2004; Wu and Zhang, 2009). At least one of these studies (Spencer et al., 2004) employed stimuli with an unusually large visual angle (approx. 7.5
). There is convincing evidence in the literature that the physical properties of the stimuli affect not only the amplitude of the plGBR per se but also the modulatory influence of non-sensory stimulus features such as saliency on this response (Busch et al., 2006; Debener et al., 2003; Stefanics et al., 2004). It is likely that the large stimulus size in our study was critical for our observation of the reliable direct IC effect on the plGBR in young children. The effect of stimulus size seems to be less important when activity is recorded directly from the cortex. Indeed, invasive studies in animals consistently find an early facilitatory IC effect on the single-unit discharge rate in the V2 visual cortex (Lee and Nguyen, 2001). What is the nature of the PL direct IC effect observed in TD children? Animal studies have shown that gamma EEG derived from the cortex using small electrodes closely correlated with local multi-unit synchronization and to a lesser extent with a single-unit discharge rate (Berens et al., 2008). The direct IC effect on plGBR might reflect a relative increase in local synchronization between neural populations in response to the IC and/or to the increase in the number and strength of activated neurons. Animal studies provide support for both these explanations. Lee and Nguyen (2001) used a static display of a Kanizsa-type square in monkeys and found that the neurons from V1 and, to a much greater extent, from V2 showed stronger responses (i.e., higher average firing rate) to the IC than to an amodal contour or to figures with rotated corners. The difference was significant in the 100e150 msec interval after appearance of the stimuli in the receptive field (Lee and Nguyen, 2001). Concurrently, in the monkeys’ visual areas (Fries et al., 2001), the amplitude of stimulus-locked gamma-frequency oscillations in the first 150 msec of the response could be modulated by attention/perceptual salience, even in the absence of discharge rate change; this likely reflects changes in neuronal synchronization. The visually evoked gamma response in humans and monkeys is thought to originate in the primary visual cortex and/or in early extra-striate visual areas [for review, see (Herrmann et al., 2010)]. This can explain our data in terms of the sharp drop in stimulus-evoked gamma and beta responses at the parietal as compared to occipital electrode locations (Knyazeva et al., 1999). However, despite the four-fold difference in magnitude of the PL high-frequency response, the latency and magnitude of the direct IC effect in the gamma band were roughly the same at the parietal and occipital scalp areas in our TD subjects and was even more pronounced at the parietal electrodes for the beta oscillations (see Fig. 3). Single-cell studies have demonstrated the important role of early visual areas (V1, V2) in the processing of ICs (Grosof et al., 1993; Lee and Nguyen, 2001; Sheth et al., 1996). One of the mechanisms underlying contour integration is believed to

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

be the long-range horizontal connections formed by the axons of pyramidal cells in the primary visual cortex (V1) that link cells with non-overlapping receptive fields but with similar orientation preference (Gilbert and Wiesel, 1979, 1983, 1989; Rockland et al., 1982; Schmidt et al., 1997; Stettler et al., 2002). This hard-wired intra-cortical connectivity may mediate contour integration, both in terms of its orientation specificity and its spatial extent (Li and Gilbert, 2002; Stettler et al., 2002). From these and other findings, some authors have concluded that IC sensitivity is a feed-forward, bottomeup process (Ffytche and Zeki, 1996). However, the important role of feedback connections from higher-order visual areas in processing subjective contours is evidenced by the much longer latency of neuron responses in early visual areas V1 and V2 for ICs compared to real contours (Lee and Nguyen, 2001). Additionally, a recent study of monkeys’ neural responses to the Kanizsa figure in the inferior temporal cortex showed that higher-order visual areas contribute to IC processing at the same 100e180 msec preattentive stage as earlier visual areas, possibly modulating responses of those V1/V2 neurons that fill the gap in the contour (Sary et al., 2008). The analogous conclusion on the role of feedback pathways to the early visual areas in the contour integration has been driven from a recent study on collinear facilitation in V1 (Li et al., 2008). Human MEG and EEG/fMRI co-registration results have also led to the notion that IC sensitivity first occurs in higher-order visual processing areas and that the IC-related activations observed in V1/V2 may result from feedback modulation from the lateral occipital cortex coupled with processing in the dorsal stream parietal areas (Kaiser et al., 2004; Murray et al., 2002). It has been suggested that cells of the higher order visual areas have large receptive fields encompassing two or more inducers that can backwards facilitate the cells participating in representation of the shape (surface or contours) formed by the inducers (Givre et al., 1994). The interaction between bottomeup and topedown processing could explain both the broad spread and relatively late timing of the direct IC effect on the plGBR and plBBR in our TD subjects. Indeed, the relatively long latency of this effect is consistent with the idea that topedown influences take time to develop after the stimulus onset. The broadband frequency range of the late direct IC effect is also in line with its topedown origin. While gamma response reaches maximum at about 100 msec after the stimulus onset, the beta response tends to peak later (150 msec) and demonstrates the direct parieto-occipital IC effect only during the later and more prolonged phase (120e270 msec) of the response (see Fig. 3). This finding agrees with human and animal data on the so-called gamma-to-beta transition (Kisley and Cornwell, 2006). Single unit studies, invitro preparation, computer stimulation, and human studies suggest that action potential bursts of neuron populations responding to stimulation tend to occur at a gamma rhythm (30e80 Hz) but switch to a beta rhythm (10e25 Hz) when the stimulation becomes relatively more intense and a large-scale recruitment of excitatory neurons takes place (Kopell et al., 2000; Traub et al., 1999). Thus, the relatively late strong direct IC effect in the beta response of TD children appears to signify recruitment by incoming stimulation of a spatially distributed system with multiple interacting regions.

713

To sum up, our results clearly show that in TD children, the PL high-frequency response to IC contains two qualitatively different phases that may reflect distinct neural mechanisms that sequentially control contour integration. The earlier phase manifests itself as an inverted IC effect, i.e., lower occipital gamma response to the IC than to the control figure. This inhibitory stage of IC processing may originate from the suppression of neuronal discharges of V1 orientation selective cells whose receptive fields overlap the gap in the collinear lines and may rely exclusively on V1 intrinsic connections. At this stage, the existing gaps in the collinear lines are underlined rather than filled in, and information about the contour breaks is conveyed to the higher-order visual areas. The next phase is reflected in the direct IC effect, i.e., the broadband and widespread enhancement of gamma and beta responses to illusory figure. This facilitatory stage may encompass contour integration processes related to the excitatory feedback signal from higher-order visual areas subserving shape discrimination (Roelfsema et al., 2002). At this stage, the gaps in the contour collinear lines are filled in by means of selective facilitation of neural discharges of orientation selective cells of the striate and extrastriate cortex. This process is thought to have a substrate in the long-range excitatory horizontal connections in the V1 and V2 visual areas and strongly depends on topedown influences from higherorder visual areas that are specific to contour detection (Li et al., 2008). The direct high-frequency IC effect observed in our study in TD children and adults (See Supplementary material) might originate from a match between the presented stimuli and stored long-term memory traces for familiar visual stimuli, e.g., square shape (Herrmann et al., 2010). Taken together, the timing and topography of stimuluslocked gamma response in children are in accordance with the view that IC perception involves multiple stages of neural processing in visual areas (Nieder, 2002; Seghier and Vuilleumier, 2006).

4.4. The high frequency response in ASD: a missing processing stage? The most striking finding in our study was the specific, but anomalous, high-frequency response to Gestalt stimuli in ASD children. Unlike their TD peers, the ASD children did not have two distinct phases of the IC effect. They demonstrated only the inverted IC effect and were completely lacking the later direct effect, e.g., higher plGBR and plBBR to the Kanizsa than to the control figure (Figs. 6 and 7). Notably, the plGBR to Kanizsa square in the ASD children was reduced relative to that evoked by the control stimulus during almost the entire time period of reliable gamma response. This finding corroborates and extends our previous results on inverted IC effect on the amplitude of the N170 component of the event-related response in children with ASD (Stroganova et al., 2007). It shows that the same inverted signature of IC processing exists in another measure of brain activity: the PL gamma oscillatory response. Comparing the latency and duration of plGBR to IC versus control stimulus in typical and ASD children suggests that this response is not unique to the brain of individuals with ASD but reflects an anomalously prolonged early processing stage that is not followed by a qualitatively different

714

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

step in illusory shape computation, as it also occurs in typical children. As discussed above, the direct IC effect in occipito-parietal gamma and beta responses in typical children may result from selective excitatory feedback from the higher-order visual areas to the striate and early extra-striate cortex. This feedback may serve to fill a gap in the contour of the illusory shape. The lack of such a direct effect in children with ASD agrees well with the proposed weakening of long-distance functional interactions in the brain of ASD individuals. The long-lasting reduction of the occipital high-frequency response to the Kanizsa figure compared to the control stimulus seems to be the only type of PL response to the IC in ASD children. In accordance with our above explanation of this inverted IC effect, we suggest that in children with ASD, differentiation between the Kanizsa square and the control figure was based predominantly on relatively low-level mechanisms of the primary visual cortex (V1). This proposition of a missing processing stage is in line with the hypothesis of reduced synchronization in visual processing networks (Brock et al., 2002) and with more recent models of neuronal long-distance under-connectivity in the brain of individuals with ASD. A great body of neuroanatomical (Keller et al., 2007) and functional evidence (Just et al., 2004) suggests that in autism, the reduced longdistance connectivity together with excessive local, shortdistance connectivity disrupt formation and functioning of large-scale, long-distance assemblies [for review, see (Courchesne et al., 2007)]. Bertone et al. (Bertone et al., 2005) hypothesize that the failure of long-distance networks might create deficiency in the higher-order processing of visual information while favoring elementary computation within local brain regions (the so-called the superior when autonomous, inferior when synchronized principle). Our results are well in accordance with this interpretation. Atypical neural connectivity in ASD, e.g., enhanced local lateral connections in V1 (Casanova et al., 2002) in combination with long-range between-area underconnectivity (Barnea-Goraly et al., 2004), may lead to an imbalance in the lower- and higher-order visual processing of the Kanizsa square that favors more autonomous stimulus processing in the low-order cortical areas. Analysis of the age dynamics of the IC effects provide an intriguing possibility that perceptual differences between ASD and TD children may increase with age. The correlations with age suggest that TD children increasingly rely on intermediate grouping processes, including integration of information over distributed visual areas, while ASD children rely on enhanced processing in early visual areas. Our results may have some implications for the ongoing discussion on the nature of local processing bias in ASD (Happe and Frith, 2006; Mottron et al., 2006). Indeed, the atypically broad-band inverted IC effect in children with ASD suggests enhancement of the very early contour processing stage (40e120 msec), which may rely exclusively on V1 intrinsic connections. The over-functioning of the low-level mechanism may be further enhanced by the weakening of topedown recurrent feedback modulation of V1 activity presumably underlying the direct IC effect. From this viewpoint, the two alternative explanations for local bias in the visual perception

in autism (i.e., enhanced perceptual functioning and weak central coherence) may be not mutually exclusive. The weakening contextual modulation in the visual processing early during development may contribute substantially to an atypical developmental course of perceptual abilities in children with ASD, including deficit of holistic perception (Brosnan et al., 2004) and face-processing abnormality (Hubl et al., 2003). Moreover, recent fMRI evidence suggests that the enhanced reliance on processing in early visual areas may also influence non-visual abilities such as reasoning (Soulieres et al., 2009) and linguistic processing (Sahyoun et al., 2010). Interestingly, the neurophysiological correlates of Kanizsa square processing are quite different in similar disorders characterized by difficulties in integrating perceptual features. For example, in Williams syndrome, no differences in the N1 amplitude elicited by the Kanizsa square and the control figure were found (Grice et al., 2003). In patients with schizophrenia, Gestalt stimuli failed to elicit any effect on the occipital component of the early gamma band oscillation (Spencer et al., 2004). Therefore, it is likely that the observed inverted IC effect on the N1 component of ERP (Stroganova et al., 2007) and on the late PL high-frequency response (present study) are the unique features of the ASD electrophysiological endophenotype. Several limitations in our study should be highlighted. We cannot exclude the possibility that the observed betweengroup differences were affected by such factors as attentiveness and perceptual salience of the IC. Although we controlled for the allocation of overt attention to visual display for all participants, TD children could pay closer attention to both stimulus categories. We believe, however, that attention was unlikely to be the major source of the observed between-group differences. Indeed, the allocation of attention to visual stimuli increases the PL gamma response (Herrmann et al., 1999), but the ASD and control participants did not differ in the general magnitude of this response. Yet another concern is that the square shape was more familiar to TD children and that differences in familiarity (i.e., saliency) contributed to the observed difference in the direct IC effect (Herrmann et al., 2010). However, we believe that this is hardly the case, because both behavioral (Otsuka et al., 2008) and neurophysiological (Csibra et al., 2000) evidence suggests that the Kanizsa IC is a salient stimulation even for 8-monthold TD infants that does not require much learning or visual experience. If present, the decreased IC salience in ASD may be secondary in relation to disordered cortical processing of IC. To conclude, our findings suggest fundamental abnormality of the visual system in children with ASD that may affect their processing of even elementary visual information. This low-level processing abnormality may further affect diverse aspects of perception and cognition in ASD.

5.

Funding

This work was supported by the Russian Fund for Basic Researches (grant 09-06-12042-ofi_m), the Ministry of Education and Science of the Russian Federation (project GK 02.740.11.0376), and the Swedish Research Council (project K2010-62X-2140-01-2).

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

Acknowledgments We warmly thank all children and their parents for their participation.

Supplementary material Supplementary material related to this article can be found online at doi:10.1016/j.cortex.2011.02.016.

references

Baldassi S, Pei F, Megna N, Recupero G, Viespoli M, Igliozzi R, et al. Search superiority in autism within, but not outside the crowding regime. Vision Research, 49(16): 2151e2156, 2009. Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, and Reiss AL. White matter structure in autism: Preliminary evidence from diffusion tensor imaging. Biological Psychiatry, 55(3): 323e326, 2004. Belmonte MK, Gomot M, and Baron-Cohen S. Visual attention in autism families: ‘unaffected’ sibs share atypical frontal activation. The Journal of Child Psychology and Psychiatry, 51(3): 259e276, 2009. Berens P, Keliris GA, Ecker AS, Logothetis NK, and Tolias AS. Feature selectivity of the gamma-band of the local field potential in primate primary visual cortex. Frontiers in Neuroscience, 2(2): 199e207, 2008. Bertone A, Mottron L, Jelenic P, and Faubert J. Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity. Brain, 128(Pt 10): 2430e2441, 2005. Bottger D, Herrmann CS, and von Cramon DY. Amplitude differences of evoked alpha and gamma oscillations in two different age groups. International Journal of Psychophysiology, 45(3): 245e251, 2002. Brock J, Brown CC, Boucher J, and Rippon G. The temporal binding deficit hypothesis of autism. Developmental Psychopathology, 14(2): 209e224, 2002. Brosnan MJ, Scott FJ, Fox S, and Pye J. Gestalt processing in autism: Failure to process perceptual relationships and the implications for contextual understanding. The Journal of Child Psychology and Psychiatry, 45(3): 459e469, 2004. Brown C, Gruber T, Boucher J, Rippon G, and Brock J. Gamma abnormalities during perception of illusory figures in Autism. Cortex, 41(3): 364, 2005. Busch NA, Debener S, Kranczioch C, Engel AK, and Herrmann CS. Size matters: Effects of stimulus size, duration and eccentricity on the visual gamma-band response. Clinical Neurophysiology, 115(8): 1810e1820, 2004. Busch NA, Schadow J, Frund I, and Herrmann CS. Time-frequency analysis of target detection reveals an early interface between bottomeup and topedown processes in the gamma-band. NeuroImage, 29(4): 1106e1116, 2006. Caron MJ, Mottron L, Berthiaume C, and Dawson M. Cognitive mechanisms, specificity and neural underpinnings of visuospatial peaks in autism. Brain, 129(Pt 7): 1789e1802, 2006. Casanova MF, Buxhoeveden DP, Switala AE, and Roy E. Minicolumnar pathology in autism. Neurology, 58(3): 428e432, 2002. Courchesne E, Pierce K, Schumann CM, Redcay E, Buckwalter JA, Kennedy DP, et al. Mapping early brain development in autism. Neuron, 56(2): 399e413, 2007.

715

Csibra G, Davis G, Spratling MW, and Johnson MH. Gamma oscillations and object processing in the infant brain. Science, 290(5496): 1582e1585, 2000. Debener S, Herrmann CS, Kranczioch C, Gembris D, and Engel AK. Topedown attentional processing enhances auditory evoked gamma band activity. NeuroReport, 14(5): 683e686, 2003. Ffytche DH and Zeki S. Brain activity related to the perception of illusory contours. NeuroImage, 3(2): 104e108, 1996. Fries P, Reynolds JH, Rorie AE, and Desimone R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science, 291(5508): 1560e1563, 2001. Frith U. Autism: Explaining the Enigma. Blackwell: London, 1989. Frund I, Busch NA, Schadow J, Korner U, and Herrmann CS. From perception to action: Phase-locked gamma oscillations correlate with reaction times in a speeded response task. BMC Neuroscience, 8: 27, 2007. Gerrard S and Rugg G. Sensory impairments and autism: A re-examination of causal modelling. Journal of Autism and Developmental Disorders, 39(10): 1449e1463, 2009. Gilbert CD and Wiesel TN. Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex. Nature, 280(5718): 120e125, 1979. Gilbert CD and Wiesel TN. Clustered intrinsic connections in cat visual cortex. The Journal of Neuroscience, 3(5): 1116e1133, 1983. Gilbert CD and Wiesel TN. Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. The Journal of Neuroscience, 9(7): 2432e2442, 1989. Givre SJ, Schroeder CE, and Arezzo JC. Contribution of extrastriate area V4 to the surface-recorded flash VEP in the awake macaque. Vision Research, 34(4): 415e428, 1994. Grice SJ, Haan MD, Halit H, Johnson MH, Csibra G, Grant J, et al. ERP abnormalities of illusory contour perception in Williams syndrome. NeuroReport, 14(14): 1773e1777, 2003. Grice SJ, Spratling MW, Karmiloff-Smith A, Halit H, Csibra G, de Haan M, et al. Disordered visual processing and oscillatory brain activity in autism and Williams syndrome. NeuroReport, 12(12): 2697e2700, 2001. Grinter EJ, Maybery MT, Pellicano E, Badcock JC, and Badcock DR. Perception of shapes targeting local and global processes in autism spectrum disorders. The Journal of Child Psychology and Psychiatry, 51(6): 717e724, 2010. Grosof DH, Shapley RM, and Hawken MJ. Macaque V1 neurons can signal ‘illusory’ contours. Nature, 365(6446): 550e552, 1993. Happe F and Frith U. The weak coherence account: Detail-focused cognitive style in autism spectrum disorders. Journal of Autism and Developmental Disorders, 36(1): 5e25, 2006. Happe FG. Studying weak central coherence at low levels: Children with autism do not succumb to visual illusions. A research note. The Journal of Child Psychology and Psychiatry, 37(7): 873e877, 1996. Herrmann CS and Bosch V. Gestalt perception modulates early visual processing. NeuroReport, 12(5): 901e904, 2001. Herrmann CS, Frund I, and Lenz D. Human gamma-band activity: A review on cognitive and behavioral correlates and network models. Neuroscience & Biobehavioral Reviews, 34(7): 981e992, 2010. Herrmann CS, Mecklinger A, and Pfeifer E. Gamma responses and ERPs in a visual classification task. Clinical Neurophysiology, 110(4): 636e642, 1999. Herrmann CS, Munk MH, and Engel AK. Cognitive functions of gamma-band activity: Memory match and utilization. Trends in Cognitive Sciences, 8(8): 347e355, 2004. Hoogenboom N, Schoffelen JM, Oostenveld R, Parkes LM, and Fries P. Localizing human visual gamma-band activity in frequency, time and space. NeuroImage, 29(3): 764e773, 2006. Hubl D, Bolte S, Feineis-Matthews S, Lanfermann H, Federspiel A, Strik W, et al. Functional imbalance of visual pathways indicates alternative face processing strategies in autism. Neurology, 61(9): 1232e1237, 2003.

716

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

Jemel B, Mimeault D, Saint-Amour D, Hosein A, and Mottron L. VEP contrast sensitivity responses reveal reduced functional segregation of mid and high filters of visual channels in autism. Journal of Vision, 10(6): 13, 2010. Jolliffe T and Baron-Cohen S. Are people with autism and Asperger syndrome faster than normal on the Embedded Figures Test? The Journal of Child Psychology and Psychiatry, 38(5): 527e534, 1997. Joseph RM, Keehn B, Connolly C, Wolfe JM, and Horowitz TS. Why is visual search superior in autism spectrum disorder? Developmental Science, 12(6): 1083e1096, 2009. Just MA, Cherkassky VL, Keller TA, Kana RK, and Minshew NJ. Functional and anatomical cortical underconnectivity in autism: Evidence from an FMRI study of an executive function task and corpus callosum morphometry. Cerebral Cortex, 17(4): 951e961, 2007. Just MA, Cherkassky VL, Keller TA, and Minshew NJ. Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity. Brain, 127(Pt 8): 1811e1821, 2004. Kaiser J, Buhler M, and Lutzenberger W. Magnetoencephalographic gamma-band responses to illusory triangles in humans. NeuroImage, 23(2): 551e560, 2004. Karakas S and Basar E. Early gamma response is sensory in origin: A conclusion based on cross-comparison of results from multiple experimental paradigms. International Journal of Psychophysiology, 31(1): 13e31, 1998. Kaufman AC and Kaufman NL. Kaufman Assessment Battery for Children: Administration and Scoring Manual. MN: American Guidance Service, 1983. Keller TA, Kana RK, and Just MA. A developmental study of the structural integrity of white matter in autism. NeuroReport, 18(1): 23e27, 2007. Khoe W, Freeman E, Woldorff MG, and Mangun GR. Electrophysiological correlates of lateral interactions in human visual cortex. Vision Research, 44(14): 1659e1673, 2004. Kisley MA and Cornwell ZM. Gamma and beta neural activity evoked during a sensory gating paradigm: Effects of auditory, somatosensory and cross-modal stimulation. Clinical Neurophysiology, 117(11): 2549e2563, 2006. Knyazeva MG, Kiper DC, Vildavski VY, Despland PA, MaederIngvar M, and Innocenti GM. Visual stimulus-dependent changes in interhemispheric EEG coherence in humans. Journal of Neurophysiology, 82(6): 3095e3107, 1999. Kopell N, Ermentrout GB, Whittington MA, and Traub RD. Gamma rhythms and beta rhythms have different synchronization properties. The Proceedings of the National Academy of Sciences Online (US), 97(4): 1867e1872, 2000. Kourtzi Z, Augath M, Logothetis NK, Movshon JA, and Kiorpes L. Development of visually evoked cortical activity in infant macaque monkeys studied longitudinally with fMRI. Magnetic Resonance Imaging, 24(4): 359e366, 2006. Lachaux JP, George N, Tallon-Baudry C, Martinerie J, Hugueville L, Minotti L, et al. The many faces of the gamma band response to complex visual stimuli. NeuroImage, 25(2): 491e501, 2005. Larsson J, Amunts K, Gulyas B, Malikovic A, Zilles K, and Roland PE. Neuronal correlates of real and illusory contour perception: Functional anatomy with PET. European Journal of Neuroscience, 11(11): 4024e4036, 1999. Lee TS and Nguyen M. Dynamics of subjective contour formation in the early visual cortex. The Proceedings of the National Academy of Sciences Online (US), 98(4): 1907e1911, 2001. Li W and Gilbert CD. Global contour saliency and local colinear interactions. Journal of Neurophysiology, 88(5): 2846e2856, 2002. Li W, Piech V, and Gilbert CD. Learning to link visual contours. Neuron, 57(3): 442e451, 2008. Loffler G. Perception of contours and shapes: Low and intermediate stage mechanisms. Vision Research, 48(20): 2106e2127, 2008.

Milne E and Scope A. Are children with autistic spectrum disorders susceptible to contour illusions? British Journal of Developmental Psychology, 26: 91e102, 2008. Milne E, Scope A, Pascalis O, Buckley D, and Makeig S. Independent component analysis reveals atypical electroencephalographic activity during visual perception in individuals with autism. Biological Psychiatry, 65(1): 22e30, 2009. Morozov AA, Morozov VA, Obukhov YV and Stroganova TA. A Non-Parametric Method of Multi-Way Analysis of EventRelated Response in Human EEG. 8th International Conference on Pattern Recognition and Image Analysis: New Information Technologies (PRIA-8-2007, October 8-12, 2007): Conference Proceedings, 3: 92e95, 2007. Mottron L, Dawson M, Soulieres I, Hubert B, and Burack J. Enhanced perceptual functioning in autism: An update, and eight principles of autistic perception. Journal of Autism and Developmental Disorders, 36(1): 27e43, 2006. Murray MM, Wylie GR, Higgins BA, Javitt DC, Schroeder CE, and Foxe JJ. The spatiotemporal dynamics of illusory contour processing: Combined high-density electrical mapping, source analysis, and functional magnetic resonance imaging. The Journal of Neuroscience, 22(12): 5055e5073, 2002. Nieder A. Seeing more than meets the eye: Processing of illusory contours in animals. Journal of Comparative Physiology A Neuroethology, Sensory, Neural, and Behavioral Physiology, 188(4): 249e260, 2002. Nikulin VV and Brismar T. Phase synchronization between alpha and beta oscillations in the human electroencephalogram. Neuroscience, 137(2): 647e657, 2006. Orekhova EV, Stroganova TA, Nygren G, Tsetlin MM, Posikera IN, Gillberg C, et al. Excess of high frequency electroencephalogram oscillations in boys with autism. Biological Psychiatry, 62(9): 1022e1029, 2007. Otsuka Y, Yamazaki Y, Konishi Y, Kanazawa S, Yamaguchi MK, and Spehar B. The perception of illusory transparent surfaces in infancy: Early emergence of sensitivity to static pictorial cues. Journal of Vision, 8(16): 6.1e12 2008. Peterhans E and von der Heydt R. Mechanisms of contour perception in monkey visual cortex. II. Contours bridging gaps. The Journal of Neuroscience, 9(5): 1749e1763, 1989. Peterhans E and von der Heydt R. Subjective contoursebridging the gap between psychophysics and physiology. Trends in Neurosciences, 14(3): 112e119, 1991. Pillow J and Rubin N. Perceptual completion across the vertical meridian and the role of early visual cortex. Neuron, 33(5): 805e813, 2002. Pulvermuller F, Keil A, and Elbert T. High-frequency brain activity: Perception or active memory? Trends in Cognitive Sciences, 3(7): 250e252, 1999. Ramachandran VS. Visual perception of surfaces: A biological theory. In Petry S and Meyer GE (Eds), The Perception of Illusory Contours. NY: Springer, 1987: 93e108. Ramsden BM, Hung CP, and Roe AW. Real and illusory contour processing in area V1 of the primate: A cortical balancing act. Cerebral cortex, 11(7): 648e665, 2001. Ring HA, Baron-Cohen S, Wheelwright S, Williams SCR, Brammer M, Andrew C, et al. Cerebral correlates of preserved cognitive skills in autism e A functional MRI study of Embedded Figures Task performance. Brain, 122: 1305e1315, 1999. Rockland KS, Lund JS, and Humphrey AL. Anatomical binding of intrinsic connections in striate cortex of tree shrews (Tupaia glis). The Journal of Comparative Neurology, 209(1): 41e58, 1982. Roelfsema PR, Lamme VA, Spekreijse H, and Bosch H. Figureground segregation in a recurrent network architecture. Journal of Cognitive Neuroscience, 14(4): 525e537, 2002. Rubenstein JL and Merzenich MM. Model of autism: Increased ratio of excitation/inhibition in key neural systems. Genes, Brain and Behavior, 2(5): 255e267, 2003.

c o r t e x 4 8 ( 2 0 1 2 ) 7 0 1 e7 1 7

Sahyoun CP, Belliveau JW, Soulieres I, Schwartz S, and Mody M. Neuroimaging of the functional and structural networks underlying visuospatial vs. linguistic reasoning in highfunctioning autism. Neuropsychologia, 48(1): 86e95, 2010. Sary G, Koteles K, Kaposvari P, Lenti L, Csifcsak G, Franko E, et al. The representation of Kanizsa illusory contours in the monkey inferior temporal cortex. European Journal of Neuroscience, 28(10): 2137e2146, 2008. Schmidt KE, Goebel R, Lowel S, and Singer W. The perceptual grouping criterion of colinearity is reflected by anisotropies of connections in the primary visual cortex. European Journal of Neuroscience, 9(5): 1083e1089, 1997. Schopler E, Reichler R, Bashford A, Lansing M, and Marcus L. Psychoeducational Profile Revised (PEP-R). Austin: Pro-Ed, 1990. Schopler E, Reichler R, and Remler BR. The Childhood Autism Rating Scale (CARS) for Diagnostic Screening and Classification of Autism. NY: Irvington Publishers, 1986. Seghier ML and Vuilleumier P. Functional neuroimaging findings on the human perception of illusory contours. Neuroscience & Biobehavioral Reviews, 30(5): 595e612, 2006. Senkowski D and Herrmann CS. Effects of task difficulty on evoked gamma activity and ERPs in a visual discrimination task. Clinical Neurophysiology, 113(11): 1742e1753, 2002. Sheth BR, Sharma J, Rao SC, and Sur M. Orientation maps of subjective contours in visual cortex. Science, 274(5295): 2110e2115, 1996. Soulieres I, Dawson M, Samson F, Barbeau EB, Sahyoun CP, Strangman GE, et al. Enhanced visual processing contributes to matrix reasoning in autism. Human Brain Mapping, 30(12): 4082e4107, 2009. Spencer KM, Nestor PG, Perlmutter R, Niznikiewicz MA, Klump MC, Frumin M, et al. Neural synchrony indexes disordered perception and cognition in schizophrenia. The Proceedings of the National Academy of Sciences Online (US), 101(49): 17288e17293, 2004. Stefanics G, Jakab A, Bernath L, Kellenyi L, and Hernadi I. EEG early evoked gamma-band synchronization reflects object recognition in visual oddball tasks. Brain Topography, 16(4): 261e264, 2004. Stettler DD, Das A, Bennett J, and Gilbert CD. Lateral connectivity and contextual interactions in macaque primary visual cortex. Neuron, 36(4): 739e750, 2002. Stroganova TA, Orekhova EV, Prokofyev AO, Posikera IN, Morozov AA, Obukhov YV, et al. Inverted event-related

717

potentials response to illusory contour in boys with autism. NeuroReport, 18(9): 931e935, 2007. Summerfield C, Jack AI, and Burgess AP. Induced gamma activity is associated with conscious awareness of pattern masked nouns. International Journal of Psychophysiology, 44(2): 93e100, 2002. Tallon-Baudry C, Bertrand O, Delpuech C, and Pernier J. Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. The Journal of Neuroscience, 16(13): 4240e4249, 1996. Traub RD, Whittington MA, Buhl EH, Jefferys JG, and Faulkner HJ. On the mechanism of the gamma –> beta frequency shift in neuronal oscillations induced in rat hippocampal slices by tetanic stimulation. The Journal of Neuroscience, 19(3): 1088e1105, 1999. Uhlhaas PJ, Pipa G, Lima B, Melloni L, Neuenschwander S, Nikolic D, et al. Neural synchrony in cortical networks: History, concept and current status. Frontiers in Integrative Neuroscience, 3: 17, 2009. Vandenbroucke MW, Scholte HS, van Engeland H, Lamme VA, and Kemner C. A neural substrate for atypical low-level visual processing in autism spectrum disorder. Brain, 131(Pt 4): 1013e1024, 2008. Vlamings PH, Jonkman LM, van Daalen E, van der Gaag RJ, and Kemner C. Basic abnormalities in visual processing affect face processing at an early age in autism spectrum disorder. Biological Psychiatry, 2010. Webb SJ, Dawson G, Bernier R, and Panagiotides H. ERP evidence of atypical face processing in young children with autism. Journal of Autism and Developmental Disorders, 36(7): 881e890, 2006. Werkle-Bergner M, Shing YL, Muller V, Li SC, and Lindenberger U. EEG gamma-band synchronization in visual coding from childhood to old age: Evidence from evoked power and intertrial phase locking. Clinical Neurophysiology, 120(7): 1291e1302, 2009. Wu X and Zhang D. Early induced beta/gamma activity during illusory contour perception. Neuroscience Letters, 462(3): 244e247, 2009. Yuval-Greenberg S, Tomer O, Keren AS, Nelken I, and Deouell LY. Transient induced gamma-band response in EEG as a manifestation of miniature saccades. Neuron, 58(3): 429e441, 2008.