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BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder with a worldwide prevalence of 1-2%. In low-resource environments, in particular, early identification and diagnosis is a significant challenge. Therefore, there is a great demand for 'language-free, culturally fair' low-cost screening tools for ASD that do not require highly trained professionals. Electroencephalography (EEG) has seen growing interest as an investigational tool for biomarker development in ASD and neurodevelopmental disorders. One of the key challenges is the identification of appropriate multivariate, next-generation analytical methodologies that can characterise the complex, nonlinear dynamics of neural networks in the brain, mindful of technical and demographic confounders that may influence biomarker findings. The aim of this study was to evaluate the robustness of recurrence quantification analysis (RQA) as a potential biomarker for ASD using a systematic methodological exploration of a range of potential technical and demographic confounders.MethodsRQA feature extraction was performed on continuous 5-second segments of resting state EEG (rsEEG) data and linear and nonlinear classifiers were tested. Data analysis progressed from a full sample of 16 ASD and 46 typically developing (TD) individuals (age 0-18 years, 4802 EEG segments), to a subsample of 16 ASD and 19 TD children (age 0-6 years, 1874 segments), to an age-matched sample of 7 ASD and 7 TD children (age 2-6 years, 666 segments) to prevent sample bias and to avoid misinterpretation of the classification results attributable to technical and demographic confounders. A clinical scenario of diagnosing an unseen subject was simulated using a leave-one-subject-out classification approach.ResultsIn the age-matched sample, leave-one-subject-out classification with a nonlinear support vector machine classifier showed 92.9% accuracy, 100% sensitivity and 85.7% specificity in differentiating ASD from TD. Age, sex, intellectual ability and the number of training and test segments per group were identified as possible demographic and technical confounders. Consistent repeatability, i.e. the correct identification of all segments per subject, was found to be a challenge.ConclusionsRQA of rsEEG was an accurate classifier of ASD in an age-matched sample, suggesting the potential of this approach for global screening in ASD. However, this study also showed experimentally how a range of technical challenges and demographic confounders can skew results, and highlights the importance of probing for these in future studies. We recommend validation of this methodology in a large and well-matched sample of infants and children, preferably in a low- and middle-income setting.

Although the diagnosis of autism spectrum disorder (ASD) is based on behavioral signs and symptoms, the evaluation of a child with ASD has become increasingly focused on the identification of the genetic etiology of the disorder. In this review, we begin with a clinical overview of ASD, highlighting the heterogeneity of the disorder. We then discuss the genetics of ASD and present updated guidelines on genetic testing. We then consider the insights gained from the identification of both single gene disorders and rare variants, with regard to clinical phenomenology and potential treatment targets.

This work explores a feature of brain dynamics, metastability, by which transients are observed in functional brain data. Metastability is a balance between static (stable) and dynamic (unstable) tendencies in electrophysiological brain activity. Furthermore, metastability is a theoretical mechanism underlying the rapid synchronization of cell assemblies that serve as neural substrates for cognitive states, and it has been associated with cognitive flexibility. While much previous research has sought to characterize metastability in the adult human brain, few studies have examined metastability in early development, in part because of the challenges of acquiring adequate, noise free continuous data in young children. To accomplish this endeavor, we studied a new method for characterizing the stability of EEG frequency in early childhood, as inspired by prior approaches for describing cortical phase resets in the scalp EEG of healthy adults. Specifically, we quantified the variance of the rate of change of the signal phase (i.e., frequency) as a proxy for phase resets (signal instability), given that phase resets occur almost simultaneously across large portions of the scalp. We tested our method in a cohort of 39 preschool age children (age =53 ± 13.6 months). We found that our outcome variable of interest, frequency variance, was a promising marker of signal stability, as it increased with the number of phase resets in surrogate (artificial) signals. In our cohort of children, frequency variance decreased cross-sectionally with age (r = −0.47, p = 0.0028). EEG signal stability, as quantified by frequency variance, increases with age in preschool age children. Future studies will relate this biomarker with the development of executive function and cognitive flexibility in children, with the overarching goal of understanding metastability in atypical development.

We quantified the potential effects of physiologic artifact on the estimation of EEG band power in a cohort of typically developing children in order to guide artifact rejection methods in quantitative EEG data analysis in developmental populations. High density EEG was recorded for 2 min while children, ages 2–6, watched a video of bubbles. Segments of data were categorized as blinks, saccades, EMG or artifact-free, and both absolute and relative power in the theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz) and gamma (35–45 Hz) bands were calculated in 9 regions for each category. Using a linear mixed model approach with artifact type, region and their interaction as predictors, we compared mean band power between clean data and each type of artifact. We found significant differences in mean relative and absolute power between artifacts and artifact-free segments in all frequency bands. The magnitude and direction of the differences varied based on power type, region, and frequency band. The most significant differences in mean band power were found in the gamma band for EMG artifact and the theta band for ocular artifacts. Artifact detection strategies need to be sensitive to the oscillations of interest for a given analysis, with the most conservative approach being the removal of all EMG and ocular artifact from EEG data. Quantitative EEG holds considerable promise as a clinical biomarker of both typical and atypical development. However, there needs to be transparency in the choice of power type, regions of interest, and frequency band, as each of these variables are differentially vulnerable to noise, and therefore, their interpretation depends on the methods used to identify and remove artifacts.

Background: Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD.Methods: EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate.Results: Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found.Conclusions: The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism. © 2013 Peters et al; licensee BioMed Central Ltd.

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., Competing Interests: Conflict of Interest Sunil Mehta acted as a paid consultant to Mirium Pharmaceuticals. All other authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Lay Abstract: In some cases, a clinician's perceptions of a child's autism-related behaviors are not the same as the child's caregiver's perceptions. Identifying how these discrepancies relate to the characteristics of the child is critical for ensuring that diagnosis procedures are unbiased and suitable for all children. This study examined whether discrepancies between clinician and caregiver reports of autism features related to the child's sex at birth. We also explored how the discrepancies related to the age at which the child received their autism diagnosis and how much intervention they received. We found that clinicians rated autism features higher than caregivers for boys and rated autism features lower than caregivers for girls. In addition, lower clinician relative to parent ratings was related to being diagnosed at an older age and receiving less intervention. These findings suggest that there is more to learn about the presentation of autism-related behaviors in girls. When caregiver and clinician ratings of autism features do not align, it may be important to consider caregivers' ratings to obtain a more accurate picture of the child's autism features and the support they may need., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: G.D. is on the Scientific Advisory Boards of Akili, Inc., Nonverbal Learning Disability Project, and Tris Pharma, Inc., provided consultation to Apple, Inc., Gerson Lehrman Group, and Guidepoint Global, LLC, received speaker fees from WebMD and book royalties from Guilford Press, Oxford University Press, Springer Nature Press. G.D. reports grant funding from NICHD, NIMH, and the Simons Foundation; G.D. has developed technology, data, and/or products that have been licensed to Apple, Inc. and Cryocell, Inc. and G.D. and Duke University have benefited financially. R.A.B. is currently employed by Apple. F.S. consults for Roche Pharmaceutical Company and Janssen Research and Development. J.C.M. consults with Customer Value Partners, Bridgebio, Determined Health, and BlackThorn Therapeutics, has received research funding from Janssen Research and Development, serves on the Scientific Advisory Boards of Pastorus and Modern Clinics, and receives royalties from Guilford Press, Lambert, Oxford, and Springer.

Background: The TAND (Tuberous Sclerosis Complex [TSC]-Associated Neuropsychiatric Disorders) Checklist was developed as a clinical screener for neurodevelopmental disorders in TSC. Most studies have described patterns in older children and adults. This study sought to better understand behavioral concerns as measured by the TAND Checklist in young children with TSC., Methods: We examined patterns of caregiver responses to the TAND Checklist in 90 toddlers with TSC (12 to 23 months n = 60; 24 to 36 months n = 30) through data collected during baseline visits across two TSC early intervention studies., Results: Over 90% of caregivers reported at least one behavioral concern related to TAND. The number of concerns increased with age. Delayed language was the most frequently reported concern across ages (12 to 23 months: 58.3%, 24 to 36 months: 86.7%). Questions related to behavioral concerns were largely relevant in this age range, but questions in other areas, such as neuropsychological or academic function, were not., Conclusions: TAND symptoms are very common in toddlers with TSC, and these symptoms may increase with age. The TAND Checklist is a useful tool for identifying behavioral concerns efficiently, but several items and sections are not suited to younger children. Results support the development of an abbreviated form of the TAND Checklist for toddlers., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nicole McDonald reports a relationship with TSC Alliance that includes travel reimbursement. Shafali Jeste reports a relationship with TSC Alliance that includes board membership and travel reimbursement. Connie Kasari reports a relationship with TSC Alliance that includes travel reimbursement. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Epilepsy is one of the most common comorbidities in individuals with autism spectrum disorders (ASDs). Risk factors include the presence of developmental delay/intellectual disability, female sex, age, and an underlying genetic condition. Due to higher prevalence of epilepsy in ASD, it is important to have a high index of suspicion for seizures and refer to a neurologist if there are concerns. Genetic testing is recommended for all children with ASD but it becomes more high yield in children with epilepsy and ASD., Competing Interests: Disclosure J.K. Capal has no financial or commercial conflicts of interest. She receives funding from the Department of Defense, United States and National Institute of Health (NIH), United States for unrelated projects. She receives grant funding from NIH and the Simons Foundation, United States. S.S. Jeste serves on the data safety monitoring board for Ionis Pharmaceuticals. She previously consulted for Roche pharmaceuticals. She receives funding from the NIH, Roche pharmaceuticals, the Saban Research Institute, United States, the Autism Science Foundation, United States, and the Simons Foundation for unrelated projects., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Recent insights into the frequency of occurrence and the genetic and mechanistic basis of nervous system disease have demonstrated that neurologic disorders occur as a spectrum across all ages. To meet future needs of patients with neurologic disease of all ages and prepare for increasing implementaton of precision therapies, greater integration of child and adult neurology residency training is needed. ANN NEUROL 2023;94:1005-1007., (© 2023 American Neurological Association.)

The Selective Social Attention (SSA) task is a brief eye-tracking task involving experimental conditions varying along socio-communicative axes. Traditionally the SSA has been used to probe socially-specific attentional patterns in infants and toddlers who develop autism spectrum disorder (ASD). This current work extends these findings to preschool and school-age children. Children 4- to 12-years-old with ASD (N = 23) and a typically-developing comparison group (TD; N = 25) completed the SSA task as well as standardized clinical assessments. Linear mixed models examined group and condition effects on two outcome variables: percent of time spent looking at the scene relative to scene presentation time (%Valid), and percent of time looking at the face relative to time spent looking at the scene (%Face). Age and IQ were included as covariates. Outcome variables' relationships to clinical data were assessed via correlation analysis. The ASD group, compared to the TD group, looked less at the scene and focused less on the actress' face during the most socially-engaging experimental conditions. Additionally, within the ASD group, %Face negatively correlated with SRS total T-scores with a particularly strong negative correlation with the Autistic Mannerism subscale T-score. These results highlight the extensibility of the SSA to older children with ASD, including replication of between-group differences previously seen in infants and toddlers, as well as its ability to capture meaningful clinical variation within the autism spectrum across a wide developmental span inclusive of preschool and school-aged children. The properties suggest that the SSA may have broad potential as a biomarker for ASD., (© 2023 International Society for Autism Research and Wiley Periodicals LLC.)

Visual exploration paradigms involving object arrays have been used to examine salience of social stimuli such as faces in ASD. Recent work suggests performance on these paradigms may associate with clinical features of ASD. We evaluate metrics from a visual exploration paradigm in 4-to-11-year-old children with ASD (n = 23; 18 males) and typical development (TD; n = 23; 13 males). Presented with arrays containing faces and nonsocial stimuli, children with ASD looked less at (p = 0.002) and showed fewer fixations to (p = 0.022) faces than TD children, and spent less time looking at each object on average (p = 0.004). Attention to the screen and faces correlated positively with social and cognitive skills in the ASD group (ps < .05). This work furthers our understanding of objective measures of visual exploration in ASD and its potential for quantifying features of ASD., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Despite growing evidence implicating thalamic functional connectivity atypicalities in autism spectrum disorder (ASD), it remains unclear how such alterations emerge early in human development. Because the thalamus plays a critical role in sensory processing and neocortical organization early in life, its connectivity with other cortical regions could be key for studying the early onset of core ASD symptoms. Here, we investigated emerging thalamocortical functional connectivity in infants at high (HL) and typical (TL) familial likelihood for ASD in early and late infancy. We report significant thalamo-limbic hyperconnectivity in 1.5-month-old HL infants, and thalamo-cortical hypoconnectivity in prefrontal and motor regions in 9-month-old HL infants. Importantly, early sensory over-responsivity (SOR) symptoms in HL infants predicted a direct trade-off in thalamic connectivity whereby stronger thalamic connectivity with primary sensory regions and basal ganglia was inversely related to connectivity with higher order cortices. This trade-off suggests that ASD may be characterized by early differences in thalamic gating. The patterns reported here could directly underlie atypical sensory processing and attention to social vs. nonsocial stimuli observed in ASD. These findings lend support to a theoretical framework of ASD whereby early disruptions in sensorimotor processing and attentional biases early in life may cascade into core ASD symptomatology., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

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., (© 2023 International Society for Autism Research and Wiley Periodicals LLC.)

Insomnia is a common, impairing, and difficult-to-treat comorbidity in children with neurodevelopmental disorders (NDDs). Behavioral interventions can be challenging because of developmental and behavioral features that interfere with treatment. Medication management also can be difficult due to a high burden of side effects, a high rate of paradoxical responses, and frequent treatment resistance. Therefore, new treatment options for insomnia in children with NDDs are needed. Dual orexin receptor antagonists (DORAs) are a relatively new class of pharmacotherapeutics that induce sleep by inhibiting the orexin signaling pathway. To date, there is little safety or efficacy data on the use of DORAs in children with NDDs. We present four patients with NDDs and insomnia that we treated with the DORA, suvorexant. We found that patients had a wide range of responses, with one patient displaying a robust improvement in sleep onset and maintenance, while another had significant improvement in insomnia symptoms on combination therapy with trazodone. Our final two patients had mild or no benefit from suvorexant therapy. Further research is necessary to establish the safety and efficacy of DORAs in this population and to identify predictive factors, such as specific neurogenetic diagnoses or clinical features, of a positive treatment response., (© 2021. The Author(s).)

What is the common denominator of consciousness across divergent regimes of cortical dynamics? Does consciousness show itself in decibels or in bits? To address these questions, we introduce a testbed for evaluating electroencephalogram (EEG) biomarkers of consciousness using dissociations between neural oscillations and consciousness caused by rare genetic disorders. Children with Angelman syndrome (AS) exhibit sleep-like neural dynamics during wakefulness. Conversely, children with duplication 15q11.2-13.1 syndrome (Dup15q) exhibit wake-like neural dynamics during non-rapid eye movement (NREM) sleep. To identify highly generalizable biomarkers of consciousness, we trained regularized logistic regression classifiers on EEG data from wakefulness and NREM sleep in children with AS using both entropy measures of neural complexity and spectral (i.e., neural oscillatory) EEG features. For each set of features, we then validated these classifiers using EEG from neurotypical (NT) children and abnormal EEGs from children with Dup15q. Our results show that the classification performance of entropy-based EEG biomarkers of conscious state is not upper-bounded by that of spectral EEG features, which are outperformed by entropy features. Entropy-based biomarkers of consciousness may thus be highly adaptable and should be investigated further in situations where spectral EEG features have shown limited success, such as detecting covert consciousness or anesthesia awareness., (© 2022. The Author(s).)

Background: There is a gap in the literature regarding genetic underpinnings of pediatric autoimmune CNS diseases. This study explored rare gene variants implicated in immune dysregulation within these disorders., Methods: This was a single-center observational study of children with inflammatory CNS disorder who had genetic testing through next generation focused exome sequencing targeting 155 genes associated with innate or adaptive immunity. For in silico prediction of functional effects of single-nucleotide variants, Polymorphism Phenotyping v2, and Sorting Intolerant from Tolerant were used, and Combined Annotation Dependent Depletion (CADD) scores were calculated. Identified genes were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis., Results: Of 54 patients, 42 (77.8%) carried variant(s), among which 12 (22.2%) had 3-8 variants. Eighty-eight unique single-nucleotide variants of 55 genes were identified. The most variants were detected in UNC13D, LRBA, LYST, NOD2, DOCK8, RNASEH2A, STAT5B, and AIRE. The majority of variants (62, 70.4%) had CADD > 10. KEGG pathway analysis revealed seven genes associated with primary immunodeficiency (Benjamini 1.40E - 06), six genes with NOD-like receptor signaling (Benjamini 4.10E - 04), five genes with Inflammatory Bowel Disease (Benjamini 9.80E - 03), and five genes with NF-kappa B signaling pathway (Benjamini 1.90E - 02)., Discussion: We observed a high rate of identification of rare and low-frequency variants in immune regulatory genes in pediatric neuroinflammatory CNS disorders. We identified 88 unique single-nucleotide variants of 55 genes with pathway analysis revealing an enrichment of NOD2-receptor signaling, consistent with involvement of the pathway within other autoinflammatory conditions and warranting further investigation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: While the cerebellum is traditionally known for its role in sensorimotor control, emerging research shows that particular subregions, such as right Crus I (RCrusI), support language and social processing. Indeed, cerebellar atypicalities are commonly reported in autism spectrum disorder (ASD), a neurodevelopmental disorder characterized by socio-communicative impairments. However, the cerebellum's contribution to early socio-communicative development remains virtually unknown., Methods: Here, we characterized functional connectivity within cerebro-cerebellar networks implicated in language/social functions in 9-month-old infants who exhibit distinct 3-year socio-communicative developmental profiles. We employed a data-driven clustering approach to stratify our sample of infants at high (n = 82) and low (n = 37) familial risk for ASD into three cohorts-Delayed, Late-Blooming, and Typical-who showed unique socio-communicative trajectories. We then compared the cohorts on indices of language and social development. Seed-based functional connectivity analyses with RCrusI were conducted on infants with fMRI data (n = 66). Cohorts were compared on connectivity estimates from a-priori regions, selected on the basis of reported coactivation with RCrusI during language/social tasks., Results: The three trajectory-based cohorts broadly differed in social communication development, as evidenced by robust differences on numerous indices of language and social skills. Importantly, at 9 months, the cohorts showed striking differences in cerebro-cerebellar circuits implicated in language/social functions. For all regions examined, the Delayed cohort exhibited significantly weaker RCrusI connectivity compared to both the Late-Blooming and Typical cohorts, with no significant differences between the latter cohorts., Conclusions: We show that hypoconnectivity within distinct cerebro-cerebellar networks in infancy predicts altered socio-communicative development before delays overtly manifest, which may be relevant for early detection and intervention. As the cerebellum is implicated in prediction, our findings point to probabilistic learning as a potential intermediary mechanism that may be disrupted in infancy, cascading into alterations in social communication., (© 2021 Association for Child and Adolescent Mental Health.)

Background: Eye tracking (ET) is a powerful methodology for studying attentional processes through quantification of eye movements. The precision, usability, and cost-effectiveness of ET render it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder (ASD)., Methods: The autism biomarkers consortium for clinical trials conducted a multisite, observational study of 6-11-year-old children with ASD (n = 280) and typical development (TD, n = 119). The ET battery included: Activity Monitoring, Social Interactive, Static Social Scenes, Biological Motion Preference, and Pupillary Light Reflex tasks. A priori, gaze to faces in Activity Monitoring, Social Interactive, and Static Social Scenes tasks were aggregated into an Oculomotor Index of Gaze to Human Faces (OMI) as the primary outcome measure. This work reports on fundamental biomarker properties (data acquisition rates, construct validity, six-week stability, group discrimination, and clinical relationships) derived from these assays that serve as a base for subsequent development of clinical trial biomarker applications., Results: All tasks exhibited excellent acquisition rates, met expectations for construct validity, had moderate or high six-week stabilities, and highlighted subsets of the ASD group with distinct biomarker performance. Within ASD, higher OMI was associated with increased memory for faces, decreased autism symptom severity, and higher verbal IQ and pragmatic communication skills., Limitations: No specific interventions were administered in this study, limiting information about how ET biomarkers track or predict outcomes in response to treatment. This study did not consider co-occurrence of psychiatric conditions nor specificity in comparison with non-ASD special populations, therefore limiting our understanding of the applicability of outcomes to specific clinical contexts-of-use. Research-grade protocols and equipment were used; further studies are needed to explore deployment in less standardized contexts., Conclusions: All ET tasks met expectations regarding biomarker properties, with strongest performance for tasks associated with attention to human faces and weakest performance associated with biological motion preference. Based on these data, the OMI has been accepted to the FDA's Biomarker Qualification program, providing a path for advancing efforts to develop biomarkers for use in clinical trials., (© 2022. The Author(s).)

Aim: To examine parental concerns about children at increased familial risk (i.e. high risk) of developing autism spectrum disorder (ASD) in early infancy., Method: ASD-related and general parental concerns were prospectively collected for 76 infants at ages 1.5, 3, 6, 9, 12, and 18 months. Outcome classification was determined at 36 months. Analyses included generalized linear mixed models and qualitative evaluation of parental concerns in relation to risk status (high vs low risk) and outcome classification within the high-risk group (atypically developing vs typically developing) over time., Results: Most parents had no concerns at 1.5 (high risk 71%, low risk 87%) and 3 months (high risk 77%, low risk 86%). Beginning at 6 months, parents of high-risk infants reported more ASD-related (p<0.001) and general concerns (p=0.003) than parents of low-risk infants. Beginning at 12 months, parents of high-risk atypically developing infants reported more ASD-related concerns than parents of high-risk typically developing infants (p=0.013)., Interpretation: Clinicians should elicit parental concerns and provide support, as parents are worried about their high-risk infants by age 6 months. Additionally, parents' abilities to identify concerns that are suggestive of ASD by age 12 months may aid in earlier screening and intervention. What this paper adds Most parents did not report concerns during early infancy. By 6 months, parents of high-risk infants reported autism spectrum disorder (ASD)-related and general concerns. By 12 months, parents of high-risk atypically developing infants identified ASD-related concerns., (© 2021 Mac Keith Press.)

Background: Sleep disturbances in autism spectrum disorder (ASD) represent a common and vexing comorbidity. Clinical heterogeneity amongst these warrants studies of the mechanisms associated with specific genetic etiologies. Duplications of 15q11.2-13.1 (Dup15q syndrome) are highly penetrant for neurodevelopmental disorders (NDDs) such as intellectual disability and ASD, as well as sleep disturbances. Genes in the 15q region, particularly UBE3A and a cluster of GABA A receptor genes, are critical for neural development, synaptic protein synthesis and degradation, and inhibitory neurotransmission. During awake electroencephalography (EEG), children with Dup15q syndrome demonstrate increased beta band oscillations (12-30 Hz) that likely reflect aberrant GABAergic neurotransmission. Healthy sleep rhythms, necessary for robust cognitive development, are also highly dependent on GABAergic neurotransmission. We therefore hypothesized that sleep physiology would be abnormal in children with Dup15q syndrome., Methods: To test the hypothesis that elevated beta oscillations persist in sleep in Dup15q syndrome and that NREM sleep rhythms would be disrupted, we computed: (1) beta power, (2) spindle density, and (3) percentage of slow-wave sleep (SWS) in overnight sleep EEG recordings from a cohort of children with Dup15q syndrome (n = 15) and compared them to age-matched neurotypical children (n = 12)., Results: Children with Dup15q syndrome showed abnormal sleep physiology with elevated beta power, reduced spindle density, and reduced or absent SWS compared to age-matched neurotypical controls., Limitations: This study relied on clinical EEG where sleep staging was not available. However, considering that clinical polysomnograms are challenging to collect in this population, the ability to quantify these biomarkers on clinical EEG-routinely ordered for epilepsy monitoring-opens the door for larger-scale studies. While comparable to other human studies in rare genetic disorders, a larger sample would allow for examination of the role of seizure severity, medications, and developmental age that may impact sleep physiology., Conclusions: We have identified three quantitative EEG biomarkers of sleep disruption in Dup15q syndrome, a genetic condition highly penetrant for ASD. Insights from this study not only promote a greater mechanistic understanding of the pathophysiology defining Dup15q syndrome, but also lay the foundation for studies that investigate the association between sleep and cognition. Abnormal sleep physiology may undermine healthy cognitive development and may serve as a quantifiable and modifiable target for behavioral and pharmacological interventions., (© 2021. The Author(s).)

Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6 weeks predicted atypical social development between 9 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Word segmentation is a fundamental aspect of language learning, since identification of word boundaries in continuous speech must occur before the acquisition of word meanings can take place. We previously used functional magnetic resonance imaging (fMRI) to show that youth with autism spectrum disorder (ASD) are less sensitive to statistical and speech cues that guide implicit word segmentation. However, little is known about the neural mechanisms underlying this process during infancy and how this may be associated with ASD risk. Here, we examined early neural signatures of language-related learning in 9-month-old infants at high (HR) and low familial risk (LR) for ASD. During natural sleep, infants underwent fMRI while passively listening to three speech streams containing strong statistical and prosodic cues, strong statistical cues only, or minimal statistical cues to word boundaries. Compared to HR infants, LR infants showed greater activity in the left amygdala for the speech stream containing statistical and prosodic cues. While listening to this same speech stream, LR infants also showed more learning-related signal increases in left temporal regions as well as increasing functional connectivity between bilateral primary auditory cortex and right anterior insula. Importantly, learning-related signal increases at 9 months positively correlated with expressive language outcome at 36 months in both groups. In the HR group, greater signal increases were additionally associated with less severe ASD symptomatology at 36 months. These findings suggest that early differences in the neural networks underlying language learning may predict subsequent language development and altered trajectories associated with ASD risk., (© 2020 John Wiley & Sons Ltd.)

Purpose of Review: Much of our understanding of early development in children with autism spectrum disorder (ASD) comes from studies of children with a family history of autism. We reviewed the current literature on neurodevelopmental profiles and autism prevalence from other high-risk infant groups to expose gaps and inform next steps. We focused on infants with early medical risk (e.g., preterm birth) and genetic risk (tuberous sclerosis complex [TSC])., Recent Findings: About 7% of very preterm infants are later diagnosed with ASD. Prospective studies of early development outside of familial-risk infants are rare; however, recent work within preterm and TSC infants suggests interesting similarities and differences from infants with a family history of ASD. It is essential that we extend our knowledge of early markers of ASD beyond familial-risk infants to expand our knowledge of autism as it emerges in order to develop better, more individualized early interventions.

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.

Prior studies have demonstrated that infants and toddlers who later go on to develop autism spectrum disorder (ASD) show atypical functional connectivity as well as altered neural processing of language and other auditory stimuli, but the timeline underlying the emergence of these altered developmental trajectories is still unclear. Here we used resting-state fMRI (rsfMRI) during natural sleep to examine the longitudinal development of functional connectivity in language-related networks from 1.5 to 9 months of age. We found that functional connectivity of networks that underlie the integration of sensory and motor representations, which is crucial for language development, is disrupted in infants at high familial risk (HR) for developing ASD as early as 1.5 months of age. By 9 months of age, HR infants showed hyperconnectivity between auditory and somatosensory regions whereas low risk (LR) infants displayed greater intrahemispheric connectivity between auditory cortex and higher-order temporal regions as well as the hippocampus. Furthermore, while LR infants showed robust changes in functional connectivity during the first year of life with increasing long-range connectivity accompanied by decreasing short-range connectivity over time, HR infants displayed limited developmental changes. Our findings demonstrate that early disruptions in the development of language-related network connectivity may provide an early marker for the later emergence of ASD symptomatology., (Copyright © 2020. Published by Elsevier Ltd.)

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., (© 2020 Wiley Periodicals, Inc.)

Background: Duplications of 15q11.2-q13.1 (Dup15q syndrome) are highly penetrant for autism, intellectual disability, hypotonia, and epilepsy. The 15q region harbors genes critical for brain development, particularly UBE3A and a cluster of gamma-aminobutyric acid type A receptor (GABA A R) genes. We recently described an electrophysiological biomarker of the syndrome, characterized by excessive beta oscillations (12-30 Hz), resembling electroencephalogram (EEG) changes induced by allosteric modulation of GABA A Rs. In this follow-up study, we tested a larger cohort of children with Dup15q syndrome to comprehensively examine properties of this EEG biomarker that would inform its use in future clinical trials, specifically, its (1) relation to basic clinical features, such as age, duplication type, and epilepsy; (2) relation to behavioral characteristics, such as cognition and adaptive function; (3) stability over time; and (4) reproducibility of the signal in clinical EEG recordings., Methods: We computed EEG power and beta peak frequency (BPF) in a cohort of children with Dup15q syndrome (N = 41, age range 9-189 months). To relate EEG parameters to clinical (study 1) and behavioral features (study 2), we examined age, duplication type, epilepsy, cognition, and daily living skills (DLS) as predictors of beta power and BPF. To evaluate stability over time (study 3), we derived the intraclass correlation coefficients (ICC) from beta power and BPF computed from children with multiple EEG recordings (N = 10, age range 18-161 months). To evaluate reproducibility in a clinical setting (study 4), we derived ICCs from beta power computed from children (N = 8, age range 19-96 months), who had undergone both research EEG and clinical EEG., Results: The most promising relationships between EEG and clinical traits were found using BPF. BPF was predicted both by epilepsy status (R 2 = 0.11, p = 0.038) and the DLS component of the Vineland Adaptive Behavior Scale (R 2 = 0.17, p = 0.01). Beta power and peak frequency showed high stability across repeated visits (beta power ICC = 0.93, BPF ICC = 0.92). A reproducibility analysis revealed that beta power estimates are comparable between research and clinical EEG (ICC = 0.94)., Conclusions: In this era of precision health, with pharmacological and neuromodulatory therapies being developed and tested for specific genetic etiologies of neurodevelopmental disorders, quantification and examination of mechanistic biomarkers can greatly improve clinical trials. To this end, the robust beta oscillations evident in Dup15q syndrome are clinically reproducible and stable over time. With future preclinical and computational studies that will help disentangle the underlying mechanism, it is possible that this biomarker could serve as a robust measure of drug target engagement or a proximal outcome measure in future disease modifying intervention trials.

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., (© 2020 International Society for Autism Research, Wiley Periodicals, Inc.)

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., (Copyright © 2020 McPartland, Bernier, Jeste, Dawson, Nelson, Chawarska, Earl, Faja, Johnson, Sikich, Brandt, Dziura, Rozenblit, Hellemann, Levin, Murias, Naples, Platt, Sabatos-DeVito, Shic, Senturk, Sugar, Webb and the Autism Biomarkers Consortium for Clinical Trials.)

Tuberous sclerosis complex (TSC) is a rare genetic syndrome that confers risk for neurodevelopmental disorders, including autism spectrum disorder and intellectual disability. Delays in social communication and early cognitive abilities are observable as early as 9 months of age in children with TSC; however, there have been no studies of early behavioral intervention in TSC. We conducted a pilot study of an evidence-based, parent-mediated behavioral intervention focused on improving early social communication and play skills in 5 children with TSC (aged 1-3 years). Participants showed maintenance and sometimes gains in developmental abilities, relative to peers, following intervention. Parents generally found the intervention to be helpful and were able to administer the intervention with fidelity. Preliminary results demonstrate initial feasibility of an early play-based, parent-mediated intervention and support the need for a large-scale, randomized clinical trial in TSC., Competing Interests: S.S.J. serves as a consultant for Roche Pharmaceuticals and on the professional advisory board for the Tuberous Sclerosis Alliance. The remaining authors (N.M.M., C.H., A.B.C., A.G., C.K., and C.A.N.) do not have any conflicts of interest associated with this study.

Background: Research in rare genetic syndromes associated with ASD is often hampered by the wide geographic distribution of families and the presence of medical comorbidities, such as epilepsy, that may preclude travel to clinical sites. These challenges can limit the sample size and generalizability of the cohorts included in both natural history studies and clinical trials. Tuberous sclerosis complex (TSC) is a rare genetic syndrome that confers an elevated risk for autism spectrum disorder (ASD), with social communication delays identified in this population as early as 12 months of age. Early identification of risk necessitates parallel testing of early intervention, prompting the first randomized controlled clinical trial of behavioral intervention for infants with TSC (NCT03422367). However, considerable early recruitment challenges have mandated the systematic identification of enrollment barriers followed by modification of the study design to address these barriers., Methods: Caregivers were interviewed regarding barriers to enrollment (phase 1). Adaptations to the intervention were made to address these barriers (phase 2). Outcomes based on this modification to the study design were defined by enrollment rate and participant demographics., Results: Qualitative reports from caregivers indicated that distance and time were the primary barriers to clinical trial enrollment. The intervention was then modified to a remote model, with at-home, parent-delivered intervention, and weekly video conferencing with interventionists at the study sites. Enrollment increased 10-fold (from 3 to 30 participants) within 1 year and included a more diverse and clinically representative cohort of infants., Conclusion: The design and implementation of more scalable methods to disseminate research remotely can substantially improve access to clinical trials in rare neurodevelopmental disorders. The lessons learned from this trial can serve as a model for future studies not only in rare conditions, but in other populations that lack adequate access, such as families with limited financial or clinical resources. Continued efforts will further refine delivery methods to enhance efficiency and ease of these delivery systems for families.

Importance: Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with different genetic etiologies. Prospective examination of familial-risk infants informs understanding of developmental trajectories preceding ASD diagnosis, potentially improving early detection., Objective: To compare outcomes and trajectories associated with varying familial risk for ASD across the first 3 years of life., Design, Setting, and Participants: This longitudinal, prospective cohort study used data from 11 sites in the Baby Siblings Research Consortium database. Data were collected between 2003 and 2015. Infants who were younger siblings of children with ASD were followed up for 3 years. Analyses were conducted in April 2018. Of the initial 1008 infants from the database, 573 were removed owing to missing necessary data, diagnostic discrepancies, or only having 1 older sibling., Exposures: Number of siblings with ASD., Main Outcomes and Measures: Outcomes included ASD symptoms, cognitive abilities, and adaptive skills. Diagnosis (ASD or no ASD) was given at 36-month outcome. The no-ASD group was classified as atypical (developmental delays and/or social-communication concerns) or typical for some analyses. Generalized linear mixed models examined developmental trajectories by ASD outcome and familial-risk group., Results: In the 435 analyzed participants (age range at outcome, 32-43 months; 246 male [57%]), 355 (82%) were from single-incidence families (1 sibling with ASD and ≥1 sibling without ASD) and 80 (18%) were from multiplex families (≥2 siblings with ASD). There were no significant group differences in major demographics. Children from multiplex families were more likely than those from single-incidence families to be classified as having ASD (29 of 80 [36%] vs 57 of 355 [16%]; 95% CI, 9%-31%; P < .001) and less likely as typical (26 of 80 [33%] vs 201 of 355 [57%]; 95% CI, -36% to -13%; P < .001), with similar rates of atypical classifications (25 of 80 [31%] vs 97 of 355 [27%]; 95% CI, -7% to 15%; P = .49). There were no differences in ASD symptoms between multiplex and single-incidence groups after controlling for ASD outcome (95% CI, -0.02 to 0.20; P = .18). During infancy, differences in cognitive and adaptive abilities were observed based on ASD outcome in the single-incidence group only. At 36 months, the multiplex/no-ASD group had lower cognitive abilities than the single-incidence/no-ASD group (95% CI, -11.89 to -2.20; P = .02), and the multiplex group had lower adaptive abilities than individuals in the single-incidence group after controlling for ASD outcome (95% CI, -9.01 to -1.48; P = .02)., Conclusions and Relevance: Infants with a multiplex family history of ASD should be monitored early and often and referred for early intervention at the first sign of concern. Direct examination of genetic contributions to neurodevelopmental phenotypes in infants with familial risk for ASD is needed.

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., (© 2019 International Society for Autism Research, Wiley Periodicals, Inc.)

Animal models of autism spectrum disorders (ASD) contribute to understanding of the role of genetics and the biological mechanisms underlying behavioral phenotypes and inform the development of potential treatments. Translational biomarkers are needed that can both validate these models and facilitate behavioral testing paradigms for ASD in humans. Automated video tracking of movement patterns and positions recorded from overhead cameras is routinely applied in behavioral paradigms designed to elicit core behavioral manifestations of ASD in rodent models. In humans, laboratory-based observations are a common semi-naturalistic context for assessing a variety of behaviors relevant to ASD such as social engagement, play, and attention. We present information learned and suggest guidelines for designing, recording, acquiring, and evaluating video tracking data of human movement patterns based on our experience in a multi-site video tracking study of children with ASD in the context of a parent-child, laboratory-based play interaction., (Copyright © 2019. Published by Elsevier B.V.)

Background: Duplications of 15q11.2-q13.1 (Dup15q syndrome), including the paternally imprinted gene UBE3A and three nonimprinted gamma-aminobutyric acid type-A (GABA A ) receptor genes, are highly penetrant for neurodevelopmental disorders such as autism spectrum disorder (ASD). To guide targeted treatments of Dup15q syndrome and other forms of ASD, biomarkers are needed that reflect molecular mechanisms of pathology. We recently described a beta EEG phenotype of Dup15q syndrome, but it remains unknown which specific genes drive this phenotype., Methods: To test the hypothesis that UBE3A overexpression is not necessary for the beta EEG phenotype, we compared EEG from a reference cohort of children with Dup15q syndrome ( n = 27) to (1) the pharmacological effects of the GABA A modulator midazolam ( n = 12) on EEG from healthy adults, (2) EEG from typically developing (TD) children ( n = 14), and (3) EEG from two children with duplications of paternal 15q (i.e., the UBE3A -silenced allele)., Results: Peak beta power was significantly increased in the reference cohort relative to TD controls. Midazolam administration recapitulated the beta EEG phenotype in healthy adults with a similar peak frequency in central channels ( f = 23.0 Hz) as Dup15q syndrome ( f = 23.1 Hz). Both paternal Dup15q syndrome cases displayed beta power comparable to the reference cohort., Conclusions: Our results suggest a critical role for GABAergic transmission in the Dup15q syndrome beta EEG phenotype, which cannot be explained by UBE3A dysfunction alone. If this mechanism is confirmed, the phenotype may be used as a marker of GABAergic pathology in clinical trials for Dup15q syndrome., Competing Interests: Competing interestsJoel Frohlich is a former employee of F. Hoffmann-La Roche Ltd. (October 2016 – July 2017). Stormy Chamberlain has received research funding from Levo Therapeutics. Joerg F. Hipp is an employee of F. Hoffmann-La Roche Ltd. Shafali Jeste: serves as a consultant for and has received funding from F. Hoffmann-La Roche Ltd. and Yamo Pharmaceuticals. All the other authors declare that they have no competing interests.

Altered structural connectivity has been identified as a possible biomarker of autism spectrum disorder (ASD) risk in the developing brain. Core features of ASD include impaired social communication and early language delay. Thus, examining white matter tracts associated with language may lend further insight into early signs of ASD risk and the mechanisms that underlie language impairments associated with the disorder. Evidence of altered structural connectivity has previously been detected in 6-month-old infants at high familial risk for developing ASD. However, as language processing begins in utero, differences in structural connectivity between language regions may be present in the early infant brain shortly after birth. Here we investigated key white matter pathways of the dorsal language network in 6-week-old infants at high (HR) and low (LR) risk for ASD to identify atypicalities in structural connectivity that may predict altered developmental trajectories prior to overt language delays and the onset of ASD symptomatology. Compared to HR infants, LR infants showed higher fractional anisotropy (FA) in the left superior longitudinal fasciculus (SLF); in contrast, in the right SLF, HR infants showed higher FA than LR infants. Additionally, HR infants showed more rightward lateralization of the SLF. Across both groups, measures of FA and lateralization of these pathways at 6 weeks of age were related to later language development at 18 months of age as well as ASD symptomatology at 36 months of age. These findings indicate that early differences in the structure of language pathways may provide an early predictor of future language development and ASD risk., (© 2018 John Wiley & Sons Ltd.)

Background: Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by either disruptions of the gene UBE3A or deletion of chromosome 15 at 15q11-q13, which encompasses UBE3A and several other genes, including GABRB3, GABRA5, GABRG3, encoding gamma-aminobutyric acid type A receptor subunits (β3, α5, γ3). Individuals with deletions are generally more impaired than those with other genotypes, but the underlying pathophysiology remains largely unknown. Here, we used electroencephalography (EEG) to test the hypothesis that genes other than UBE3A located on 15q11-q13 cause differences in pathophysiology between AS genotypes., Methods: We compared spectral power of clinical EEG recordings from children (1-18 years of age) with a deletion genotype (n = 37) or a nondeletion genotype (n = 21) and typically developing children without Angelman syndrome (n = 48)., Results: We found elevated theta power (peak frequency: 5.3 Hz) and diminished beta power (peak frequency: 23 Hz) in the deletion genotype compared with the nondeletion genotype as well as excess broadband EEG power (1-32 Hz) peaking in the delta frequency range (peak frequency: 2.8 Hz), shared by both genotypes but stronger for the deletion genotype at younger ages., Conclusions: Our results provide strong evidence for the contribution of non-UBE3A neuronal pathophysiology in deletion AS and suggest that hemizygosity of the GABRB3-GABRA5-GABRG3 gene cluster causes abnormal theta and beta EEG oscillations that may underlie the more severe clinical phenotype. Our work improves the understanding of AS pathophysiology and has direct implications for the development of AS treatments and biomarkers., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2019. Published by Elsevier Ltd.)

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., Competing Interests: Conflicts of Interest The authors have no conflicts of interest to declare.

Background: Motor delays and impairments in autism spectrum disorders (ASD) are extremely common and often herald the emergence of pervasive atypical development. Clinical accounts of ASD and standardized measures of motor function have identified deficits in multiple motor domains. However, literature describing frequently used standardized motor assessments in children with ASD, their test properties, and their limitations are sparse., Methods: We systematically reviewed the literature to identify the most frequently used standardized motor assessments used to evaluate children with ASD from infancy to early childhood. All assessments included were required to possess reference norms, evaluate more than one motor domain, and have undergone some degree of validation., Results: We identified six frequently used standardized measures of motor function per our inclusion and exclusion criteria. We investigated and described in detail the psychometric properties of these assessments, their utility for use with children with ASD, and their individual and overall strengths and limitations. The global strengths of these assessments are the ability to identify early development delays and differences in fine and gross motor function in children with ASD. Global limitations of these studies are lack of validation in individuals with ASD and scoring systems that often miss specific and subtle abnormalities., Conclusions: Standardized assessments of motor function have provided valuable information on motor impairments in ASD. However, significant limitations remain in the use of these measures in children with ASD. Moving forward, it is imperative that standardized measures of motor function receive greater validation testing in children with ASD to assess their potential application given the clinical heterogeneity of this condition. In addition, utilizing quantitative measures of motor function should allow for evaluation and comparison of individuals with ASD across the lifespan with varying cognitive and behavioral abilities.

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., (© 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)