Clinical factors associated with genetic diagnosis in suspected neurogenetic disorders in a tertiary care clinic.

PURPOSE: This study aimed to identify phenotypic factors associated with genetic diagnoses in patients with neurodevelopmental disorders and generate a decision tree to assist clinicians in identifying patients most likely to receive a positive result on genetic testing. METHODS: We retrospectively reviewed the charts of 316 patients evaluated in a neurodevelopmental clinic between 2014 and 2019. Patients were categorized based on genetic test results. Analyses were performed to identify variables that discriminate between patients with and without a genetic diagnosis. RESULTS: Patients with a genetic diagnosis were more likely to be female and have a history of motor delay, hypotonia, congenital heart disease, and early intervention. Classification and regression tree analysis revealed that 75% of patients with motor delay had a genetic diagnosis. In patients without motor delay, hypotonia, age of walking, and age at initial evaluation were important indicators of a genetic diagnosis. CONCLUSION: Our findings suggest that motor delay and hypotonia are associated with genetic diagnoses in children with neurodevelopmental disorders. The decision tree highlights patient subsets at greater risk and suggests possible phenotypic screens. Future studies could develop validated decision trees based on phenotypic data to assist clinicians in stratifying patients for genetic testing.

Flexible Regularized Estimation in High-Dimensional Mixed Membership Models.

Mixed membership models are an extension of finite mixture models, where each observation can partially belong to more than one mixture component. A probabilistic framework for mixed membership models of high-dimensional continuous data is proposed with a focus on scalability and interpretability. The novel probabilistic representation of mixed membership is based on convex combinations of dependent multivariate Gaussian random vectors. In this setting, scalability is ensured through approximations of a tensor covariance structure through multivariate eigen-approximations with adaptive regularization imposed through shrinkage priors. Conditional weak posterior consistency is established on an unconstrained model, allowing for a simple posterior sampling scheme while keeping many of the desired theoretical properties of our model. The model is motivated by two biomedical case studies: a case study on functional brain imaging of children with autism spectrum disorder (ASD) and a case study on gene expression data from breast cancer tissue. These applications highlight how the typical assumption made in cluster analysis, that each observation comes from one homogeneous subgroup, may often be restrictive in several applications, leading to unnatural interpretations of data features.

Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.

Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

Hybrid principal components analysis for region-referenced longitudinal functional EEG data.

Electroencephalography (EEG) data possess a complex structure that includes regional, functional, and longitudinal dimensions. Our motivating example is a word segmentation paradigm in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. For each subject, continuous EEG signals recorded at each electrode were divided into one-second segments and projected into the frequency domain via fast Fourier transform. Following a spectral principal components analysis, the resulting data consist of region-referenced principal power indexed regionally by scalp location, functionally across frequencies, and longitudinally by one-second segments. Standard EEG power analyses often collapse information across the longitudinal and functional dimensions by averaging power across segments and concentrating on specific frequency bands. We propose a hybrid principal components analysis for region-referenced longitudinal functional EEG data, which utilizes both vector and functional principal components analyses and does not collapse information along any of the three dimensions of the data. The proposed decomposition only assumes weak separability of the higher-dimensional covariance process and utilizes a product of one dimensional eigenvectors and eigenfunctions, obtained from the regional, functional, and longitudinal marginal covariances, to represent the observed data, providing a computationally feasible non-parametric approach. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group level inference, both geared towards sparse data applications. Analysis of the data from the word segmentation paradigm leads to valuable insights about group-region differences among the TD and verbal and minimally verbal children with ASD. Finite sample properties of the proposed estimation framework and bootstrap inference procedure are further studied via extensive simulations.

Principle ERP reduction and analysis: Estimating and using principle ERP waveforms underlying ERPs across tasks, subjects and electrodes.

Event-related potentials (ERP) waveforms are the summation of many overlapping signals. Changes in the peak or mean amplitude of a waveform over a given time period, therefore, cannot reliably be attributed to a particular ERP component of ex ante interest, as is the standard approach to ERP analysis. Though this problem is widely recognized, it is not well addressed in practice. Our approach begins by presuming that any observed ERP waveform - at any electrode, for any trial type, and for any participant - is approximately a weighted combination of signals from an underlying set of what we refer to as principle ERPs, or pERPs. We propose an accessible approach to analyzing complete ERP waveforms in terms of their underlying pERPs. First, we propose the principle ERP reduction (pERP-RED) algorithm for investigators to estimate a suitable set of pERPs from their data, which may span multiple tasks. Next, we provide tools and illustrations of pERP-space analysis, whereby observed ERPs are decomposed into the amplitudes of the contributing pERPs, which can be contrasted across conditions or groups to reveal which pERPs differ (substantively and/or significantly) between conditions/groups. Differences on all pERPs can be reported together rather than selectively, providing complete information on all components in the waveform, thereby avoiding selective reporting or user discretion regarding the choice of which components or windows to use. The scalp distribution of each pERP can also be plotted for any group/condition. We demonstrate this suite of tools through simulations and on real data collected from multiple experiments on participants diagnosed with Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Software for conducting these analyses is provided in the pERPred package for R.

Behavioral characterization of dup15q syndrome: Toward meaningful endpoints for clinical trials.

Duplication of 15q11.2-q13.1 (dup15q syndrome) is one of the most common copy number variations associated with autism spectrum disorders (ASD) and intellectual disability (ID). As with many neurogenetic conditions, accurate behavioral assessment is challenging due to the level of impairment and heterogeneity across individuals. Large-scale phenotyping studies are necessary to inform future clinical trials in this and similar ID syndromes. This study assessed developmental and behavioral characteristics in a large cohort of children with dup15q syndrome, and examined differences based on genetic subtype and epilepsy status. Participants included 62 children (2.5-18 years). Across individuals, there was a wide range of abilities. Although adaptive behavior was strongly associated with cognitive ability, adaptive abilities were higher than cognitive scores. Measures of ASD symptoms were associated with cognitive ability, while parent report of challenging behavior was not. Both genetic subtype and epilepsy were related to degree of impairment across cognitive, language, motor, and adaptive domains. Children with isodicentric duplications and epilepsy showed the greatest impairment, while children with interstitial duplications showed the least. On average, participants with epilepsy experienced seizures over 53% of their lives, and half of children with epilepsy had infantile spasms. Parents of children with isodicentric duplications reported more concerns regarding challenging behaviors. Future trials in ID syndromes should employ a flexible set of assessments, allowing each participant to receive assessments that capture their skills. Multiple sources of information should be considered, and the impact of language and cognitive ability should be taken into consideration when interpreting results.

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.

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.

A multi-dimensional functional principal components analysis of EEG data.

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.

Longitudinal Effects of Adaptive Interventions With a Speech-Generating Device in Minimally Verbal Children With ASD.

There are limited data on the effects of adaptive social communication interventions with a speech-generating device in autism. This study is the first to compare growth in communications outcomes among three adaptive interventions in school-age children with autism spectrum disorder (ASD) who are minimally verbal. Sixty-one children, ages 5-8 years, participated in a sequential, multiple-assignment randomized trial (SMART). All children received a developmental behavioral communication intervention: joint attention, symbolic play, engagement and regulation (JASP) with enhanced milieu teaching (EMT). The SMART included three 2-stage, 24-week adaptive interventions with different provisions of a speech-generating device (SGD) in the context of JASP+EMT. The first adaptive intervention, with no SGD, initially assigned JASP+EMT alone, then intensified JASP+EMT for slow responders. In the second adaptive intervention, slow responders to JASP+EMT were assigned JASP+EMT+SGD. The third adaptive intervention initially assigned JASP+EMT+SGD; then intensified JASP+EMT+SGD for slow responders. Analyses examined between-group differences in change in outcomes from baseline to Week 36. Verbal outcomes included spontaneous communicative utterances and novel words. Nonlinguistic communication outcomes included initiating joint attention and behavior regulation, and play. The adaptive intervention beginning with JASP+EMT+SGD was estimated as superior. There were significant (p < .05) between-group differences in change in spontaneous communicative utterances and initiating joint attention. School-age children with ASD who are minimally verbal make significant gains in communication outcomes with an adaptive intervention beginning with JASP+EMT+SGD. Future research should explore mediators and moderators of the adaptive intervention effects and second-stage intervention options that further capitalize on early gains in treatment.

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.

Concomitant medication use in children with autism spectrum disorder: Data from the Autism Biomarkers Consortium for Clinical Trials.

LAY ABSTRACT: Children with autism spectrum disorder are prescribed a variety of medications that affect the central nervous system (psychotropic medications) to address behavior and mood. In clinical trials, individuals taking concomitant psychotropic medications often are excluded to maintain homogeneity of the sample and prevent contamination of biomarkers or clinical endpoints. However, this choice may significantly diminish the clinical representativeness of the sample. In a recent multisite study designed to identify biomarkers and behavioral endpoints for clinical trials (the Autism Biomarkers Consortium for Clinical Trials), school-age children with autism spectrum disorder were enrolled without excluding for medications, thus providing a unique opportunity to examine characteristics of psychotropic medication use in a research cohort and to guide future decisions on medication-related inclusion criteria. The aims of the current analysis were (1) to quantify the frequency and type of psychotropic medications reported in school-age children enrolled in the ABC-CT and (2) to examine behavioral features of children with autism spectrum disorder based on medication classes. Of the 280 children with autism spectrum disorder in the cohort, 42.5% were taking psychotropic medications, with polypharmacy in half of these children. The most commonly reported psychotropic medications included melatonin, stimulants, selective serotonin reuptake inhibitors, alpha agonists, and antipsychotics. Descriptive analysis showed that children taking antipsychotics displayed a trend toward greater overall impairment. Our findings suggest that exclusion of children taking concomitant psychotropic medications in trials could limit the clinical representativeness of the study population, perhaps even excluding children who may most benefit from new treatment options.

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.

The diagnostic journey of genetically defined neurodevelopmental disorders.

BACKGROUND: The development of advanced genetic technologies has resulted in rapid identification of genetic etiologies of neurodevelopmental disorders (NDDs) and has transformed the classification and diagnosis of various NDDs. However, diagnostic genetics has far outpaced our ability to provide timely medical counseling, guidance, and care for patients with genetically defined NDDs. These patients and their caregivers present with an unmet need for care coordination across multiple domains including medical, developmental, and psychiatric care and for educational resources and guidance from care professionals. After a genetic diagnosis is made, families also face several barriers in access to informed diagnostic evaluations and medical support. METHODS: As part of Care and Research in Neurogenetics (CARING), a multidisciplinary clinical program for children and adults with neurogenetic disorders, we conducted qualitative clinical interviews about the diagnostic journey of families. This included the overall timeline to receiving diagnoses, experiences before and after diagnosis, barriers to care, and resources that helped them to navigate the diagnostic process. RESULTS: A total of 37 interviews were conducted with parents of children ages 16 months to 33 years. Several key themes were identified: (1) delays between initial caregiver observations and formal developmental or genetic diagnoses; (2) practical barriers to clinical evaluation and care, including long wait times for an appointment, lack of insurance coverage, availability of local evaluations, transportation difficulties, and native language differences; (3) the importance of being part of a patient advocacy group to help navigate the diagnostic journey; and (4) unique challenges faced by adults (18 years or older). CONCLUSIONS: Families of children with complex neurodevelopmental and genetic disabilities face numerous challenges in finding adequate medical care and services for their child. They experience considerable delays in receiving timely diagnoses and face significant barriers that further delay the process of receiving access to services needed for the child's continued care. The gaps indicated in this study speak to the need for more comprehensive coordination of care for patients with intellectual and developmental disabilities, as well as the development of systematic, disorder-specific resources both for providers and families in order to improve patient outcomes.

Measurement of Sleep Behaviors in Chromosome 15q11.2-13.1 Duplication (Dup15q Syndrome).

Duplication of chromosome 15q11.2-q13.1 (dup15q syndrome) results in hypotonia, intellectual disability (ID), and autism symptomatology. Clinical electroencephalography has shown abnormal sleep physiology, but no studies have characterized sleep behaviors. The present study used the Children's Sleep Habits Questionnaire (CSHQ) in 42 people with dup15q syndrome to examine the clinical utility of this questionnaire and quantify behavioral sleep patterns in dup15q syndrome. Individuals with fully completed forms (56%) had higher cognitive abilities than those with partially completed forms. Overall, caregivers indicated a high rate of sleep disturbance, though ratings differed by epilepsy status. Results suggest that clinicians should use caution when using standardized questionnaires and consider epilepsy status when screening for sleep problems in dup15q syndrome.

Comprehensive Assessment of Individuals With Significant Levels of Intellectual Disability: Challenges, Strategies, and Future Directions.

The variety and extent of impairments in individuals with severe-profound levels of intellectual disability (ID) impact their ability to complete valid behavioral assessments. Although standardized assessment is crucial for objectively evaluating patients, many individuals with severe-profound levels of ID perform at the floor of most assessments designed for their chronological age. Additionally, the presence of language and motor impairments may influence the individual's ability to perform a task, even when that task is meant to measure an unrelated construct leading to an underestimation of their true ability. This article provides an overview of the assessment protocols used by multiple groups working with individuals with severe-profound levels of ID, discusses considerations for obtaining high-quality assessment results, and suggests guidelines for standardizing these protocols across the field.

REGION-REFERENCED SPECTRAL POWER DYNAMICS OF EEG SIGNALS: A HIERARCHICAL MODELING APPROACH.

Functional brain imaging through electroencephalography (EEG) relies upon the analysis and interpretation of high-dimensional, spatially organized time series. We propose to represent time-localized frequency domain characterizations of EEG data as region-referenced functional data. This representation is coupled with a hierarchical regression modeling approach to multivariate functional observations. Within this familiar setting we discuss how several prior models relate to structural assumptions about multivariate covariance operators. An overarching modeling framework, based on infinite factorial decompositions, is finally proposed to balance flexibility and efficiency in estimation. The motivating application stems from a study of implicit auditory learning, in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. Using the proposed model, we examine differential band power dynamics as brain function is interrogated throughout the duration of a computer-controlled experiment. Our work offers a novel look at previous findings in psychiatry and provides further insights into the understanding of ASD. Our approach to inference is fully Bayesian and implemented in a highly optimized Rcpp package.

The Neurodevelopmental and Motor Phenotype of SCA21 (ATX-TMEM240).

Spinocerebellar ataxia type 21 (SCA21/ATX-TMEM240) is a rare form of cerebellar ataxia that commonly presents with motor, cognitive, and behavioral impairments. Although these features have been identified as part of the clinical manifestations of SCA21, the neurodevelopmental disorders associated with SCA21 have not been well studied or described. Here we present extensive phenotypic data for 3 subjects from an SCA21 family in the United States. Genetic testing demonstrated the c.196 G>A (p.Gly66Arg) variant to be a second recurrent mutation associated with the disorder. Standardized developmental assessment revealed significant deficits in cognition, adaptive function, motor skills, and social communication with 2 of the subjects having diagnoses of autism spectrum disorder, which has never been described in SCA21. Quantitative gait analysis showed markedly abnormal spatiotemporal gait variables indicative of poor gait control and cerebellar as well as noncerebellar dysfunction. Clinical evaluation also highlighted a striking variability in clinical symptoms, with greater ataxia correlating with greater severity of neurodevelopmental disorder diagnoses. Notably, neurodevelopmental outcomes have improved with intervention over time. Taken together, this case series identifies that the manifestation of neurodevelopmental disorders is a key feature of SCA21 and may precede the presence of motor abnormalities. Furthermore, the coexistence of ataxia and neurodevelopmental disorders in these subjects suggests a role for spinocerebellar pathways in both outcomes. The findings in this study highlight the importance of evaluation of neurodevelopmental concerns in the context of progressive motor abnormalities and the need for timely intervention to ultimately improve quality of life for individuals with SCA21.

ERP evidence of semantic processing in children with ASD.

25% of children with autism spectrum disorder (ASD) remain minimally verbal (MV), despite intervention. Electroencephalography can reveal neural mechanisms underlying language impairment in ASD, potentially improving our ability to predict language outcomes and target interventions. Verbal (V) and MV children with ASD, along with an age-matched typically developing (TD) group participated in a semantic congruence ERP paradigm, during which pictures were displayed followed by the expected or unexpected word. An N400 effect was evident in all groups, with a shorter latency in the TD group. A late negative component (LNC) also differentiated conditions, with a group by condition by region interaction. Post hoc analyses revealed that the LNC was present across multiple regions in the TD group, in the mid-frontal region in MVASD, and not present in the VASD group. Cluster analysis identified subgroups within the ASD participants. Two subgroups showed markedly atypical patterns of processing, one with reversed but robust differentiation of conditions, and the other with initially reversed followed by typical differentiation. Findings indicate that children with ASD, including those with minimal language, showed EEG evidence of semantic processing, but it was characterized by delayed speed of processing and limited integration with mental representations.

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.