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Children with autism spectrum disorder are prescribed various medications to address behavior and mood. In clinical trials, individuals taking concomitant psychotropic medications often are excluded to maintain homogeneity and prevent contamination of clinical endpoints. However, this choice may compromise the representativeness of the sample. In a recent study designed to identify biomarkers and endpoints for clinical trials (the Autism Biomarkers Consortium for Clinical Trials), school-age children with autism spectrum disorder were enrolled without excluding for medications, providing the opportunity to examine characteristics of psychotropic medication use and 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 study 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. The most commonly reported psychotropic medications included melatonin, stimulants, selective serotonin reuptake inhibitors, alpha agonists, and antipsychotics. Our findings suggest that exclusion of children taking concomitant psychotropic medications could limit the representativeness of the study population, perhaps even excluding children who may most benefit from new treatment options.

In this study we investigated the impact of parental language input on language development and associated neuroscillatory patterns in toddlers at risk of Autism Spectrum Disorder (ASD). Forty-six mother-toddler dyads at either high (n = 22) or low (n = 24) familial risk of ASD completed a longitudinal, prospective study including free-play, resting electroencephalography, and standardized language assessments. Input quantity/quality at 18 months positively predicted expressive language at 24 months, and relationships were stronger for high-risk toddlers. Moderated mediations revealed that input-language relationships were explained by 24-month frontal and temporal gamma power (30-50 Hz) for high-risk toddlers who would later develop ASD. Results suggest that high-risk toddlers may be cognitively and neurally more sensitive to their language environments, which has implications for early intervention.

Biomarker development is currently a high priority in neurodevelopmental disorder research. For many types of biomarkers (particularly biomarkers of diagnosis), reliability over short periods is critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) algorithm, allow for targeted characterization of the features that naturally emerge within an EEG PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each individual's EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate test-retest reliability based on the characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. We show that individuals exhibit idiosyncratic PSD signatures that are stable over recording sessions using both characterizations. Our data show that EEG activity from a brief 2-min recording provides an efficient window into characterizing brain activity at the single-subject level with desirable psychometric characteristics that persist across different analytical decomposition methods. This is a necessary step towards analytical validation of biomarkers based on the EEG PSD and provides insights into parameters of the PSD that offer short-term reliability (and thus promise as potential biomarkers of trait or diagnosis) vs. those that are more variable over the short term (and thus may index state or other rapidly dynamic measures of brain function). Future research should address the longer-term stability of the PSD, for purposes such as monitoring development or response to treatment.

Research has identified early appearing differences in gross and fine motor abilities in infants at heightened risk (HR) for autism spectrum disorder (ASD) because they are the younger siblings of children with ASD, and it suggests that such differences may be especially apparent among those HR infants themselves eventually diagnosed with ASD. The present study examined overall and item-level performance on the gross (GM) and fine motor (FM) subscales of the Mullen Scales of Early Learning (MSEL) administered at 6 months to a large, geographically diverse sample of HR infants with varying developmental outcomes (ASD, elevated ADOS without ASD, low ADOS without ASD) and to infants with low ASD risk (low risk [LR]). We also explored whether motor abilities assessed at 6 months predicted ASD symptom severity at 36 months. FM (but not GM) performance distinguished all 3 HR groups from LR infants with the weakest performance observed in the HR-Elevated ADOS children, who exhibited multiple differences from both LR and other HR infants in both gross and fine motor skills. Finally, 6-month FM (but not GM) scores significant predicted 36-month ADOS severity scores in the HR group; but no evidence was found of specific early appearing motor signs associated with a later ASD diagnosis. Vulnerabilities in infants' fine and gross motor skills may have significant consequences for later development not only in the motor domain but in other domains. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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. [ABSTRACT FROM AUTHOR]

Clinical trials in autism spectrum disorder (ASD) often rely on clinician rating scales and parent surveys to measure autism‐related features and social behaviors. To aid in the selection of these assessments for future clinical trials, the Autism Biomarkers Consortium for Clinical Trials (ABC‐CT) directly compared eight common instruments with respect to acquisition rates, sensitivity to group differences, equivalence across demographic sub‐groups, convergent validity, and stability over a 6‐week period. The sample included 280 children diagnosed with ASD (65 girls) and 119 neurotypical children (36 girls) aged from 6 to 11 years. Full scale IQ for ASD ranged from 60 to 150 and for neurotypical ranged from 86 to 150. Instruments measured clinician global assessment and autism‐related behaviors, social communication abilities, adaptive function, and social withdrawal behavior. For each instrument, we examined only the scales that measured social or communication functioning. Data acquisition rates were at least 97.5% at T1 and 95.7% at T2. All scales distinguished diagnostic groups. Some scales significantly differed by participant and/or family demographic characteristics. Within the ASD group, most clinical instruments exhibited weak (≥ |0.1|) to moderate (≥ |0.4|) intercorrelations. Short‐term stability was moderate (ICC: 0.5–0.75) to excellent (ICC: >0.9) within the ASD group. Variations in the degree of stability may inform viability for different contexts of use, such as identifying clinical subgroups for trials versus serving as a modifiable clinical outcome. All instruments were evaluated in terms of their advantages and potential concerns for use in clinical trials. Lay Summary: Evaluating interventions for autism often relies on clinician rating scales and parent surveys to measure results. We compared several common measurement instruments to evaluate which were most likely to be completed, distinguished children with and without ASD, and were impacted by participant and family characteristics. We also examined whether the instruments measured similar aspects of social ability and provided consistent scores across a short period of time. Results may be used to guide better selection of tests for future studies of autism interventions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent proposals have suggested the potential for neural biomarkers to improve clinical trial processes in neurodevelopmental conditions; however, few efforts have identified whether chronological age-based adjustments will be necessary (as used in standardized behavioral assessments). Event-related potentials (ERPs) demonstrate early differences in the processing of faces vs. objects in the visual processing system by 4 years of age and age-based improvement (decreases in latency) through adolescence. Additionally, face processing has been proposed to be related to social skills as well as autistic social-communication traits. While previous reports suggest delayed latency in individuals with autism spectrum disorder (ASD), extensive individual and age based heterogeneity exists. In this report, we utilize a sample of 252 children with ASD and 118 children with typical development (TD), to assess the N170 and P100 ERP component latencies (N170L and P100L, respectively), to upright faces, the face specificity effect (difference between face and object processing), and the inversion effect (difference between face upright and inverted processing) in relation to age. First, linear mixed models (LMMs) were fitted with fixed effect of age at testing and random effect of participant, using all available data points to characterize general age-based development in the TD and ASD groups. Second, LMM models using only the TD group were used to calculate age-based residuals in both groups. The purpose of residualization was to assess how much variation in ASD participants could be accounted for by chronological age-related changes. Our data demonstrate that the N170L and P100L responses to upright faces appeared to follow a roughly linear relationship with age. In the ASD group, the distribution of the age-adjusted residual values suggest that ASD participants were more likely to demonstrate slower latencies than would be expected for a TD child of the same age, similar to what has been identified using unadjusted values. Lastly, using age-adjusted values for stratification, we found that children who demonstrated slowed age-adjusted N170L had lower verbal and non-verbal IQ and worse face memory. These data suggest that age must be considered in assessing the N170L and P100L response to upright faces as well, and these adjusted values may be used to stratify children within the autism spectrum. [ABSTRACT FROM AUTHOR]

Background: Phelan-McDermid Syndrome (PMS) is a rare condition caused by deletion or mutation of the SHANK3 gene. Individuals with PMS frequently present with intellectual disability, autism spectrum disorder, and other neurodevelopmental challenges. Electroencephalography (EEG) can provide a window into network-level function in PMS. Methods: Here, we analyze EEG data collected across multiple sites in individuals with PMS (n = 26) and typically developing individuals (n = 15). We quantify oscillatory power, alpha-gamma phase-amplitude coupling strength, and phase bias, a measure of the phase of cross frequency coupling thought to reflect the balance of feedforward (bottom-up) and feedback (top-down) activity. Results: We find individuals with PMS display increased alpha-gamma phase bias (U = 3.841, p < 0.0005), predominantly over posterior electrodes. Most individuals with PMS demonstrate positive overall phase bias while most typically developing individuals demonstrate negative overall phase bias. Among individuals with PMS, strength of alpha-gamma phase-amplitude coupling was associated with Sameness, Ritualistic, and Compulsive behaviors as measured by the Repetitive Behavior Scales-Revised (Beta = 0.545, p = 0.011). Conclusions: Increased phase bias suggests potential circuit-level mechanisms underlying phenotype in PMS, offering opportunities for back-translation of findings into animal models and targeting in clinical trials. [ABSTRACT FROM AUTHOR]

Biomarker development is currently a high priority in neurodevelopmental disorder research. For many types of biomarkers (particularly biomarkers of diagnosis), reliability over short periods is critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) algorithm, allow for targeted characterization of the features that naturally emerge within an EEG PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each individual's EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate test-retest reliability based on the characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. We show that individuals exhibit idiosyncratic PSD signatures that are stable over recording sessions using both characterizations. Our data show that EEG activity from a brief 2-min recording provides an efficient window into characterizing brain activity at the single-subject level with desirable psychometric characteristics that persist across different analytical decomposition methods. This is a necessary step towards analytical validation of biomarkers based on the EEG PSD and provides insights into parameters of the PSD that offer short-term reliability (and thus promise as potential biomarkers of trait or diagnosis) vs. those that are more variable over the short term (and thus may index state or other rapidly dynamic measures of brain function). Future research should address the longer-term stability of the PSD, for purposes such as monitoring development or response to treatment. [ABSTRACT FROM AUTHOR]

Clinical research in neurodevelopmental disorders remains reliant upon clinician and caregiver measures. Limitations of these approaches indicate a need for objective, quantitative, and reliable biomarkers to advance clinical research. Extant research suggests the potential utility of multiple candidate biomarkers; however, effective application of these markers in trials requires additional understanding of replicability, individual differences, and intra-individual stability over time. The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is a multi-site study designed to investigate a battery of electrophysiological (EEG) and eye-tracking (ET) indices as candidate biomarkers for autism spectrum disorder (ASD). The study complements published biomarker research through: inclusion of large, deeply phenotyped cohorts of children with ASD and typical development; a longitudinal design; a focus on well-evidenced candidate biomarkers harmonized with an independent sample; high levels of clinical, regulatory, technical, and statistical rigor; adoption of a governance structure incorporating diverse expertise in the ASD biomarker discovery and qualification process; prioritization of open science, including creation of a repository containing biomarker, clinical, and genetic data; and use of economical and scalable technologies that are applicable in developmental populations and those with special needs. The ABC-CT approach has yielded encouraging results, with one measure accepted into the FDA's Biomarker Qualification Program to date. Through these advances, the ABC-CT and other biomarker studies in progress hold promise to deliver novel tools to improve clinical trials research in ASD. [ABSTRACT FROM AUTHOR]