Parsing evoked and induced gamma response differences in Autism: A visual evoked potential study.

OBJECTIVE: Electroencephalography (EEG) measures of visual evoked potentials (VEPs) provide a targeted approach for investigating neural circuit dynamics. This study separately analyses phase-locked (evoked) and non-phase-locked (induced) gamma responses within the VEP to comprehensively investigate circuit differences in autism. METHODS: We analyzed VEP data from 237 autistic and 114 typically developing (TD) children aged 6-11, collected through the Autism Biomarkers Consortium for Clinical Trials (ABC-CT). Evoked and induced gamma (30-90 Hz) responses were separately quantified using a wavelet-based time-frequency analysis, and group differences were evaluated using a permutation-based clustering procedure. RESULTS: Autistic children exhibited reduced evoked gamma power but increased induced gamma power compared to TD peers. Group differences in induced responses showed the most prominent effect size and remained statistically significant after excluding outliers. CONCLUSIONS: Our study corroborates recent research indicating diminished evoked gamma responses in children with autism. Additionally, we observed a pronounced increase in induced power. Building upon existing ABC-CT findings, these results highlight the potential to detect variations in gamma-related neural activity, despite the absence of significant group differences in time-domain VEP components. SIGNIFICANCE: The contrasting patterns of decreased evoked and increased induced gamma activity in autistic children suggest that a combination of different EEG metrics may provide a clearer characterization of autism-related circuitry than individual markers alone.

Modeling intra-individual inter-trial EEG response variability in autism.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

Patterns of spontaneous neural activity associated with social communication abilities among infants and toddlers showing signs of autism.

Early disruptions to social communication development, including delays in joint attention and language, are among the earliest markers of autism spectrum disorder (autism, henceforth). Although social communication differences are a core feature of autism, there is marked heterogeneity in social communication-related development among infants and toddlers exhibiting autism symptoms. Neural markers of individual differences in joint attention and language abilities may provide important insight into heterogeneity in autism symptom expression during infancy and toddlerhood. This study examined patterns of spontaneous electroencephalography (EEG) activity associated with joint attention and language skills in 70 community-referred 12- to 23-month-olds with autism symptoms and elevated scores on an autism diagnostic instrument. Data-driven cluster-based permutation analyses revealed significant positive associations between relative alpha power (6-9 Hz) and concurrent response to joint attention skills, receptive language, and expressive language abilities. Exploratory analyses also revealed significant negative associations between relative alpha power and measures of core autism features (i.e., social communication difficulties and restricted/repetitive behaviors). These findings shed light on the neural mechanisms underlying typical and atypical social communication development in emerging autism and provide a foundation for future work examining neural predictors of social communication growth and markers of intervention response.

Central Posterior Envelopes for Bayesian Functional Principal Component Analysis.

Bayesian methods provide direct inference in functional data analysis applications without reliance on bootstrap techniques. A major tool in functional data applications is the functional principal component analysis which decomposes the data around a common mean function and identifies leading directions of variation. Bayesian functional principal components analysis (BFPCA) provides uncertainty quantification on the estimated functional model components via the posterior samples obtained. We propose central posterior envelopes (CPEs) for BFPCA based on functional depth as a descriptive visualization tool to summarize variation in the posterior samples of the estimated functional model components, contributing to uncertainty quantification in BFPCA. The proposed BFPCA relies on a latent factor model and targets model parameters within a mixed effects modeling framework using modified multiplicative gamma process shrinkage priors on the variance components. Functional depth provides a center-outward order to a sample of functions. We utilize modified band depth and modified volume depth for ordering of a sample of functions and surfaces, respectively, to derive at CPEs of the mean and eigenfunctions within the BFPCA framework. The proposed CPEs are showcased in extensive simulations. Finally, the proposed CPEs are applied to the analysis of a sample of power spectral densities (PSD) from resting state electroencephalography (EEG) where they lead to novel insights on diagnostic group differences among children diagnosed with autism spectrum disorder and their typically developing peers across age.

Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data.

Electroencephalography (EEG) studies produce region-referenced functional data via EEG signals recorded across scalp electrodes. The high-dimensional data can be used to contrast neurodevelopmental trajectories between diagnostic groups, for example between typically developing (TD) children and children with autism spectrum disorder (ASD). Valid inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, EEG data is collected on TD and ASD children aged two to twelve years old. The peak alpha frequency, a prominent peak in the alpha spectrum, is a biomarker linked to neurodevelopment that shifts as children age. To retain information, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children.

Electrophysiological signatures of brain aging in autism spectrum disorder.

Recent evidence suggests that structural and functional brain aging is atypical in adults with autism spectrum disorder (ASD). However, it remains unclear if oscillatory slowing, a key marker of neurophysiological aging, follows an atypical trajectory in this population. This study examines patterns of age-related oscillatory slowing in adults with ASD, captured by reductions in the brain's peak alpha frequency (PAF). Resting-state electroencephalography (EEG) data from adults (18-70 years) with ASD (N = 93) and non-ASD controls (N = 87) were pooled from three independent datasets. A robust curve-fitting procedure quantified the peak frequency of alpha oscillations (7-13 Hz) across all brain regions. Associations between PAF and age were assessed and compared between groups. Consistent with characteristic patterns of oscillatory slowing, PAF was negatively associated with age across the entire sample (p < .0001). A significant group-by-age interaction revealed that this relationship was more pronounced in adults with ASD (p < .01). These findings invite further longitudinal investigations of PAF in adults with ASD to confirm if age-related oscillatory slowing is accelerated.

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms.

BACKGROUND: Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months. METHODS: Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition. RESULTS: Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD. CONCLUSIONS: Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Functional connectivity during language processing in 3-month-old infants at familial risk for autism spectrum disorder.

Auditory statistical learning (ASL) plays a role in language development and may lay a foundation for later social communication impairment. As part of a longitudinal study of infant siblings, we asked whether electroencephalography (EEG) measures of connectivity during ASL at 3 months of age-differentiated infants who showed signs of autism spectrum disorder (ASD) at age 18 months. We measured spectral power and phase coherence in the theta (4-6 Hz) and alpha (6-12 Hz) frequency bands within putative language networks. Infants were divided into ASD-concern (n = 14) and No-ASD-concern (n = 49) outcome groups based on their ASD symptoms at 18 months, measured using the Autism Diagnostic Observation Scale Toddler Module. Using permutation testing, we identified a trend toward reduced left fronto-central phase coherence at the electrode pair F9-C3 in both theta and alpha frequency bands in infants who later showed ASD symptoms at 18 months. Across outcome groups, alpha coherence at 3 months correlated with greater word production at 18 months on the MacArthur-Bates Communicative Development Inventory. This study introduces signal processing and analytic tools that account for the challenges inherent in infant EEG studies, such as short duration of recordings, considerable movement artifact, and variable volume conduction. Our results indicate that connectivity, as measured by phase coherence during 2.5 min of ASL, can be quantified as early as 3 months and suggest that early alternations in connectivity may serve as markers of resilience for neurodevelopmental impairments.

Quantitative Gait Analysis in Duplication 15q Syndrome and Nonsyndromic ASD.

Motor impairments occur frequently in genetic syndromes highly penetrant for autism spectrum disorder (syndromic ASD) and in individuals with ASD without a genetic diagnosis (nonsyndromic ASD). In particular, abnormalities in gait in ASD have been linked to language delay, ASD severity, and likelihood of having a genetic disorder. Quantitative measures of motor function can improve our ability to evaluate motor differences in individuals with syndromic and nonsyndromic ASD with varying levels of intellectual disability and adaptive skills. To evaluate this methodology, we chose to use quantitative gait analysis to study duplication 15q syndrome (dup15q syndrome), a genetic disorder highly penetrant for motor delays, intellectual disability, and ASD. We evaluated quantitative gait variables in individuals with dup15q syndrome (n = 39) and nonsyndromic ASD (n = 21) and compared these data to a reference typically developing cohort. We found a gait pattern of slow pace, poor postural control, and large gait variability in dup15q syndrome. Our findings improve characterization of motor function in dup15q syndrome and nonsyndromic ASD. Quantitative gait analysis can be used as a translational method and can improve our identification of clinical endpoints to be used in treatment trials for these syndromes. Autism Res 2020, 13: 1102-1110. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Motor impairments, particularly abnormalities in walking, occur frequently in genetic syndromes highly penetrant for autism spectrum disorder (syndromic ASD). Here, using quantitative gait analysis, we find that individuals with duplication 15q syndrome have an atypical gait pattern that differentiates them from typically developing and nonsyndromic ASD individuals. Our findings improve motor characterization in dup15q syndrome and nonsyndromic ASD.

Electrophysiological signatures of visual statistical learning in 3-month-old infants at familial and low risk for autism spectrum disorder.

Visual statistical learning (VSL) refers to the ability to extract associations and conditional probabilities within the visual environment. It may serve as a precursor to cognitive and social communication development. Quantifying VSL in infants at familial risk (FR) for Autism Spectrum Disorder (ASD) provides opportunities to understand how genetic predisposition can influence early learning processes which may, in turn, lay a foundation for cognitive and social communication delays. We examined electroencephalography (EEG) signatures of VSL in 3-month-old infants, examining whether EEG correlates of VSL differentiated FR from low-risk (LR) infants. In an exploratory analysis, we then examined whether EEG correlates of VSL at 3 months relate to cognitive function and ASD symptoms at 18 months. Infants were exposed to a continuous stream of looming shape pairs with varying probability that the shapes would occur in sequence (high probability-deterministic condition; low probability-probabilistic condition). EEG was time-locked to shapes based on their transitional probabilities. EEG analysis examined group-level characteristics underlying specific components, including the late frontal positivity (LFP) and N700 responses. FR infants demonstrated increased LFP and N700 response to the probabilistic condition, whereas LR infants demonstrated increased LFP and N700 response to the deterministic condition. LFP at 3 months predicted 18-month visual reception skills and not ASD symptoms. Our findings thus provide evidence for distinct VSL processes in FR and LR infants as early as 3 months. Atypical pattern learning in FR infants may lay a foundation for later delays in higher level, nonverbal cognitive skills, and predict ASD symptoms well before an ASD diagnosis is made.

Covariate-adjusted region-referenced generalized functional linear model for EEG data.

Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. It is of clinical interest to relate the highly structured EEG data to scalar outcomes such as diagnostic status. In our motivating study, resting-state EEG is collected on both typically developing (TD) children and children with autism spectrum disorder (ASD) aged 2 to 12 years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we consider the oscillations in the spectral density within the alpha band, rather than just the peak location, as a functional predictor of diagnostic status (TD vs ASD), adjusted for chronological age. A covariate-adjusted region-referenced generalized functional linear model is proposed for modeling scalar outcomes from region-referenced functional predictors, which utilizes a tensor basis formed from one-dimensional discrete and continuous bases to estimate functional effects across a discrete regional domain while simultaneously adjusting for additional nonfunctional covariates, such as age. The proposed methodology provides novel insights into differences in neural development of TD and ASD children. The efficacy of the proposed methodology is investigated through extensive simulation studies.

Early patterns of functional brain development associated with autism spectrum disorder in tuberous sclerosis complex.

Tuberous sclerosis complex (TSC) is a rare genetic disorder that confers a high risk for autism spectrum disorders (ASD), with behavioral predictors of ASD emerging early in life. Deviations in structural and functional neural connectivity are highly implicated in both TSC and ASD. For the first time, we explore whether electroencephalographic (EEG) measures of neural network function precede or predict the emergence of ASD in TSC. We determine whether altered brain function (a) is present in infancy in TSC, (b) differentiates infants with TSC based on ASD diagnostic status, and (c) is associated with later cognitive function. We studied 35 infants with TSC (N = 35), and a group of typically developing infants (N = 20) at 12 and 24 months of age. Infants with TSC were later subdivided into ASD and non-ASD groups based on clinical evaluation. We measured features of spontaneous alpha oscillations (6-12 Hz) that are closely associated with neural network development: alpha power, alpha phase coherence (APC), and peak alpha frequency (PAF). Infants with TSC demonstrated reduced interhemispheric APC compared to controls at 12 months of age, and these differences were found to be most pronounced at 24 months in the infants who later developed ASD. Across all infants, PAF at 24 months was associated with verbal and nonverbal cognition at 36 months. Associations between early network function and later neurodevelopmental and cognitive outcomes highlight the potential utility of early scalable EEG markers to identify infants with TSC requiring additional targeted intervention initiated very early in life. Autism Res 2019, 12: 1758-1773. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Approximately half of infants with tuberous sclerosis complex (TSC) develop autism. Here, using EEG, we find that there is a reduction in communication between brain regions during infancy in TSC, and that the infants who show the largest reductions are those who later develop autism. Being able to identify infants who show early signs of disrupted brain development may improve the timing of early prediction and interventions in TSC, and also help us to understand how early brain changes lead to autism.

EEG Data Collection in Children with ASD: The Role of State in Data Quality and Spectral Power.

BACKGROUND: Electroencephalography can elucidate neurobiological mechanisms underlying heterogeneity in ASD. Studying the full range of children with ASD introduces methodological challenges stemming from participants' difficulties tolerating the data collection process, leading to diminished EEGdataretentionandincreasedvariabilityin participant 'state' during the recording. Quantifying state will improve data collection methods and aide in interpreting results. OBJECTIVES: Observationally quantify participant state during the EEG recording; examine its relationship to child characteristics, data retention and spectral power. METHODS: Participants included 5-11 year-old children with D (N=39) and age-matched TD children (N=16). Participants were acclimated to the EEG environment using behavioral strategies. EEG was recorded while participants watched a video of bubbles. Participant 'state' was rated using a Likert scale (Perceived State Rating: PSR). RESULTS: Participants with ASD had more elevated PSR than TD participants. Less EEG data were retained in participants with higher PSR scores, but this was not related to age or IQ. TD participants had higher alpha power compared with the ASD group. Within the ASD group, participants with high PSR had decreased frontal alpha power. CONCLUSIONS: Given supportive strategies, EEG data was collected from children with ASD across cognitive levels. Participant state influenced both EEG data retention and alpha spectral power. Alpha suppression is linked to attention and vigilance, suggesting that these participants were less 'at rest'. This highlights the importance of considering state when conducting EEG studies with challenging participants, both to increase data retention rates and to quantify the influence of state on EEG variables.

Lateral inhibition in the autism spectrum: An SSVEP study of visual cortical lateral interactions.

Circuit level brain dysfunction has been suggested as a common mechanism through which diverse genetic risk factors and neurobiological sequelae lead to the core features of autism spectrum disorder (Geschwind 2009; Port et al. 2014). An important mediator of circuit level brain activity is lateral inhibition, and a number of authors have suggested that lateral inhibition may be atypical in ASD. However, evidence regarding putative atypical lateral connections in ASD is mixed. Here we employed a steady state visual evoked potential (SSVEP) paradigm to further investigate lateral connections within a group of high functioning adults with ASD. At a group level, we found no evidence of altered lateral interactions in ASD. Exploratory analyses reveal that greater ASD symptom severity (increased ADOS score) is associated with increased short range lateral inhibition. These results suggest that lateral interactions are not altered in ASD at a group-level, but that subtle alterations in such neurobiological processes may underlie the heterogeneity seen in the autism spectrum in terms of sensory perception and behavioral phenotype.

Peak alpha frequency is a neural marker of cognitive function across the autism spectrum.

Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants.

Adults with autism spectrum conditions experience increased levels of anomalous perception.

Autism spectrum condition (ASC) is characterised by differences in social interaction and behavioural inflexibility. In addition to these core symptoms, atypical sensory responses are prevalent in the ASC phenotype. Here we investigated anomalous perception, i.e. hallucinatory and/or out of body experiences in adults with ASC. Thirty participants with an ASC diagnosis and thirty neurotypical controls completed the Cardiff Anomalous Perception Scale (CAPS) and the Social Responsiveness Scale (SRS-2). The CAPS is a 32-item questionnaire that asks participants to indicate whether or not they experience a range of anomalous and out of body experiences, and to rate how intrusive and distressing these experiences are. The SRS-2 asks participants to rate the extent to which they identify with a series of 65 statements that describe behaviours associated with the autism phenotype. We found that total CAPS score was significantly higher in the participants with ASC (mean = 14.8, S.D. = 7.9) than the participants without ASC (mean = 3.6, S.D. = 4.1). In addition, the frequency of anomalous perception, the level of distraction and the level of distress associated with the experience were significantly increased in participants with ASC. Importantly, both the frequency of anomalous perceptual experiences and the level of distress caused by anomalous perception in this sample of adults with ASC were very similar to that reported previously in a sample of non-autistic participants who were being treated in hospital for a current psychotic episode. These data indicate that anomalous perceptual experiences are common in adults with ASC and are associated with a high level of distress. The origins of anomalous perception in ASC and the implication of this phenomenon are considered.

Understanding perceptual judgment in autism spectrum disorder using the drift diffusion model.

OBJECTIVE: Two-alternative forced-choice tasks are widely used to gain insight into specific areas of enhancement or impairment in individuals with autism spectrum disorder (ASD). Data arising from these tasks have been used to support myriad theories regarding the integrity, or otherwise, of particular brain areas or cognitive processes in ASD. The drift diffusion model (DDM) provides an account of the underlying processes which give rise to accuracy and reaction time (RT) distributions, and parameterizes these processes in terms which have direct psychological interpretation. Importantly, the DDM provides further insight into the origin of potential group differences in task performance. Here, for the first time, we used the DDM to investigate perceptual decision making in ASD. METHOD: Adults with (N = 25) and without ASD (N = 32) performed an orientation discrimination task. A drift diffusion model was applied to the full RT distributions. RESULTS: Participants with ASD responded more slowly than controls, the groups did not differ in accuracy. Modeled parameters indicated that: (a) participants with ASD were more cautious than controls (wider boundary separation); (b) nondecision time was increased in ASD; and (c) the quality of evidence extracted from the stimulus (drift rate) did not vary between groups. CONCLUSIONS: Taking the behavioral data in isolation would suggest reduced perceptual sensitivity in ASD. However, DDM results indicated that despite response slowing, there was no evidence of differential perceptual sensitivity between participants with and without ASD. Future use of the DDM in investigations of perception and cognition in ASD is highly recommended. (PsycINFO Database Record

Measuring neural excitation and inhibition in autism: Different approaches, different findings and different interpretations.

The balance of neural excitation and inhibition (E/I balance) is often hypothesised to be altered in autism spectrum disorder (ASD). One widely held view is that excitation levels are elevated relative to inhibition in ASD. Understanding whether, and how, E/I balance may be altered in ASD is important given the recent interest in trialling pharmacological interventions for ASD which target inhibitory neurotransmitter function. Here we provide a critical review of evidence for E/I balance in ASD. We conclude that data from a number of domains provides support for alteration in excitation and inhibitory neurotransmission in ASD, but when considered collectively, the available literature provide little evidence to support claims for either a net increase in excitation or a net increase in inhibition. Strengths and limitations of available techniques are considered, and directions for future research discussed.

Superior orientation discrimination and increased peak gamma frequency in autism spectrum conditions.

While perception is recognized as being atypical in individuals with autism spectrum conditions (ASC), the underlying mechanisms for such atypicality are unclear. Here we test the hypothesis that individuals with ASC will show enhanced orientation discrimination compared with neurotypical observers. This prediction is based both on anecdotal report of superior discriminatory skills in ASC and also on evidence in the auditory domain that some individuals with ASC have superior pitch discrimination. In order to establish whether atypical perception might be mediated by an imbalance in the ratio of neural excitation and inhibition (E:I ratio), we also measured peak gamma frequency, which provides an indication of neural inhibition levels. Using a rigorous thresholding method, we found that orientation discrimination thresholds for obliquely oriented stimuli were significantly lower in participants with ASC. Using EEG to measure the visually induced gamma band response, we also found that peak gamma frequency was higher in participants with ASC, relative to a well-matched control group. These novel results suggest that neural inhibition may be increased in the occipital cortex of individuals with ASC. Implications for existing theories of an imbalance in the E:I ratio of ASC are discussed.