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

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. 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.

BACKGROUND: Chromosome 15q11-q13 duplication syndrome (Dup15q) is a neurogenetic disorder caused by duplications of the maternal copy of this region. In addition to hypotonia, motor deficits, and language impairments, Dup15q patients commonly meet the criteria for autism spectrum disorder (ASD) and have a high prevalence of seizures. It is known from mouse models that synaptic impairments are a strong component of Dup15q pathophysiology, however, cellular phenotypes that relate to seizures are less clear. The development of patient-derived induced pluripotent stem cells (iPSCs) provides a unique opportunity to study human neurons with the exact genetic disruptions that cause Dup15q. METHODS: Here, we explored electrophysiological phenotypes in iPSC-derived neurons from four Dup15q patients compared to six unaffected controls, one patient with a 15q11-q13 paternal duplication, and three Angelman syndrome patients. RESULTS: We identified several properties of Dup15q neurons that could contribute to neuronal hyperexcitability and seizure susceptibility. Compared to controls, Dup15q neurons had increased excitatory synaptic event frequency and amplitude and increased density of dendritic protrusions, along with increased action potential firing and decreased inhibitory synaptic transmission. Dup15q neurons also showed impairments in activity-dependent synaptic plasticity and homeostatic synaptic scaling. Finally, Dup15q neurons showed an increased frequency of spontaneous action potential firing compared to control neurons, in part due to disruption of KCNQ2 potassium channels. CONCLUSIONS: Together these data point to multiple electrophysiological mechanisms of hyperexcitability that may provide new targets for the treatment of seizures and other phenotypes associated with Dup15q.

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.

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

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

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

Background COVID-19 restrictions have significantly limited access to in-person educational and healthcare services for all, including individuals with intellectual and developmental disabilities (IDDs). The objectives of this online survey that included both national and international families were to capture changes in access to healthcare and educational services for individuals with IDDs that occurred shortly after restrictions were initiated and to survey families on resources that could improve services for these individuals. Methods This was an online survey for caregivers of individuals with (1) a genetic diagnosis and (2) a neurodevelopmental diagnosis, including developmental delay, intellectual disability, autism spectrum disorder or epilepsy. The survey assessed (1) demographics, (2) changes in access to educational and healthcare services and (3) available and preferred resources to help families navigate the changes in service allocation. Results Of the 818 responses (669 within the USA and 149 outside of the USA), most families reported a loss of at least some educational or healthcare services. Seventy-four per cent of parents reported that their child lost access to at least one therapy or education service, and 36% of respondents lost access to a healthcare provider. Only 56% reported that their child received at least some continued services through tele-education. Those that needed to access healthcare providers did so primarily through telemedicine. Telehealth (both tele-education and telemedicine) was reported to be helpful when available, and caregivers most often endorsed a need for an augmentation of these remote delivery services, such as 1:1 videoconference sessions, as well as increased access to 1:1 aides in the home. Conclusions COVID-19 restrictions have greatly affected access to services for individuals with syndromic IDDs. Telehealth may provide opportunities for delivery of care and education in a sustainable way, not only as restrictions endure but also after they have been lifted.

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.

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

Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. The high-dimensional data capture underlying neural dynamics and it is of clinical interest to model differences in neurodevelopmental trajectories between diagnostic groups, for example typically developing (TD) children and children with autism spectrum disorder (ASD). In such cases, valid group-level inference requires characterization of the complex EEG dependency structure as well as covariate-dependent heteroscedasticity, such as changes in variation over developmental age. In our motivating study, resting state EEG is collected on both TD and ASD children aged two to twelve years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we model patterns of alpha spectral variation, rather than just the peak location, regionally across the scalp and chronologically across development for both the TD and ASD diagnostic groups. We propose a covariate-adjusted hybrid principal components analysis (CA-HPCA) for region-referenced functional EEG data, which utilizes both vector and functional principal components analysis while simultaneously adjusting for covariate-dependent heteroscedasticity. CA-HPCA assumes the covariance process is weakly separable conditional on observed covariates, allowing for covariate-adjustments to be made on the marginal covariances rather than the full covariance leading to stable and computationally efficient estimation. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group-level inference. The proposed methodology provides novel insights into neurodevelopmental differences between TD and ASD children.

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

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

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

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

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

Background Duplications of 15q11.2-q13.1 (Dup15q syndrome), including the paternally imprinted gene UBE3A and three nonimprinted gamma-aminobutyric acid type-A (GABAA) 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 GABAA 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. Electronic supplementary material The online version of this article (10.1186/s13229-019-0280-6) contains supplementary material, which is available to authorized users.

Following publication of the original article [1], we have been notified that the Ethics statement of the articles should be changed. The Ethics statement now reads

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 EEG data retention and increased variability in 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 ASD (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.

While a large number of preschool age children with autism spectrum disorder (ASD) use no or little spoken language, only a portion will remain minimally verbal into elementary school. Determining whether a child is likely to remain minimally verbal past the preschool years is of great importance to families and clinicians, and can inform intervention efforts. Evidence from both behavioral and neuroimaging studies provide support for the idea that minimally verbal children with ASD have specific and unique expressive language deficits and are not simply at an earlier stage of language development. Intervention that focuses on pre-linguistic skills, such as joint attention, gestures, and vocalizations can be effective to increase language in pre- and minimally verbal children with ASD. The use of speech generating devices (SGD) has also been shown to support spoken language development in this population. Although many preschool aged children with ASD are using no or very little language, many of these children are in fact pre-verbal, and will continue to develop language skills. Targeted intervention, including a focus on pre-linguistic communication and SGD, will help support their language development.

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

Little is known about language development in children with Autism Spectrum Disorders (ASD) who remain minimally verbal past age 5. While there is evidence that children can develop language after age 5, we lack detailed information. Studies of this population generally focus on discrete language skills without addressing broader social-communication abilities. As communication and social deficits are both inherent to ASD, an examination of not only what language skills are acquired, but how those skills are used in interactions is relevant. Research in typical development has examined how communication interchanges (unbroken back-and-forth exchanges around a unified purpose) develop, which can be used as a framework for studying minimally verbal children. This study examined the interchange use by 55 children with ASD over the course of a 6-month play and engagement-based communication intervention. Half of the children received intervention sessions that also incorporated a speech-generating device (SGD). Interchanges were coded by: frequency, length, function, and initiator (child or adult). Results indicated that children initiated a large proportion of interchanges and this proportion increased over time. The average length and number of interchanges increased over time, with children in the SGD group showing even greater growth. Finally, children's total number of interchanges at baseline was positively associated with their spoken language gains over the course of intervention. This study supports the crucial relationship between social engagement and expressive language development, and highlights the need to include sustained communication interchanges as a target for intervention with this population. Autism Res 2016, 9: 1093-1102. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

• Diverse genetic and environmental etiologies converge onto circuit level brain dysfunction in autism spectrum disorder (ASD), manifesting at a macroscopic level as aberrant neural connectivity. Previous studies have described atypical patterns of decreased short range and increased long range connectivity in ASD [ 1 ]. However, it remains unclear whether group level features of circuit dysfunction are consistently present across the range of cognitive function seen in the autism spectrum. • The dynamics of neural oscillations in the alpha range (6–12 Hz) are exquisitely sensitive to healthy development of functional and structural connectivity. Alpha-band coherence, measured with high temporal-precision electroencephalography (EEG) therefore represents an ideal tool for studying neural connectivity in developmental populations. • Here we examined spontaneous alpha phase coherence in a heterogeneous sample of 59 children with ASD and 39 age matched typically developing children. Using a data driven approach, we conducted an unbiased examination of all possible atypical connectivity patterns across all cortical regions. • Long-range hypoconnectivity was present in children with ASD compared to typically developing children, with temporal interhemispheric connectivity showing the largest difference between the two groups. • Decreased long range alpha coherence distinguishes a heterogeneous group of ASD children from typically developing children. Interhemispheric temporal hypoconnectivity represents a fundamental functional difference in children with ASD across a wide cognitive and age range that may reflect white matter disturbances or increased signal variability at temporal sites in ASD.

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 (6-12Hz) 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.

Notably absent from the intervention literature are parent training programs targeting school-aged children with autism who have limited communication skills (Tager-Flusberg and Kasari in Autism Res 6:468–478, 2013). Sixty-one children with autism age 5–8 with minimal spontaneous communication received a 6-month social communication intervention including parent training. Parent–child play interactions were coded for parents' strategy implementation and children's time jointly engaged (Adamson et al. in J Autism Dev Disord 39:84–96, 2009). Parents mastered an average of 70 % of the strategies. Further analyses indicated some gains in implementation occurred from mere observation of sessions, while the greatest gains occurred in the first month of active coaching and workshops. Children's joint engagement was associated with parents' implementation success across time demonstrating parents' implementation was relevant to children's social engagement.

Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.

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

BackgroundOne of the most common genetic variants associated with autism spectrum disorder (ASD) are duplications of chromosome 15q11.2-q13.1 (Dup15q syndrome). To identify distinctive developmental and behavioral features in Dup15q syndrome, we examined the social communication, adaptive, and cognitive skills in clinic-referred subjects and compared the characteristics of children with Dup15q syndrome to age/IQ-matched children with non-syndromic ASD. Behavior and development were also analyzed within the Dup15q group for differences related to copy number or epilepsy.MethodsParticipants included 13 children with Dup15q syndrome and 13 children with non-syndromic ASD, matched on chronological and mental age, ages 22months-12years. In the Dup15q group, ten participants had isodicentric and three had interstitial duplications. Four children had active epilepsy (all isodicentric). Participants were assessed for verbal and non-verbal cognition, ASD characteristics based on the Autism Diagnostic Observation Schedule (ADOS), and adaptive function based on the Vineland Adaptive Behavior Scales (VABS). Group comparisons were performed between Dup15q and ASD participants, as well as within the Dup15q group based on duplication type and epilepsy status.ResultsAll children with Dup15q syndrome met the criteria for ASD; ASD severity scores were significantly lower than children in the non-syndromic ASD group. ADOS profiles demonstrated a relative strength in items related to social interest. Children with Dup15q syndrome also demonstrated significantly more impairment in motor and daily living skills. Within the Dup15q group, children with epilepsy demonstrated significantly lower cognitive and adaptive function than those without epilepsy.ConclusionsThe relative strength observed in social interest and responsiveness in the context of impaired motor skills represents an important avenue for intervention, including aggressive treatment of epilepsy, early and consistent focus on motor skills, and intervention targeting joint attention and language within a play context, in order to build on social interest to further develop social communication abilities. Longitudinal research beginning in early development will elucidate the temporal relationships between developmental domains and neurological comorbidities in these children at high risk for neurodevelopmental disorders.

Little is known about language development in children with Autism Spectrum Disorders (ASD) who remain minimally verbal past age 5. While there is evidence that children can develop language after age 5, we lack detailed information. Studies of this population generally focus on discrete language skills without addressing broader social-communication abilities. As communication and social deficits are both inherent to ASD, an examination of not only what language skills are acquired, but how those skills are used in interactions is relevant. Research in typical development has examined how communication interchanges (unbroken back-and-forth exchanges around a unified purpose) develop, which can be used as a framework for studying minimally verbal children. This study examined the interchange use by 55 children with ASD over the course of a 6-month play and engagement-based communication intervention. Half of the children received intervention sessions that also incorporated a speech-generating device (SGD). Interchanges were coded by: frequency, length, function, and initiator (child or adult). Results indicated that children initiated a large proportion of interchanges and this proportion increased over time. The average length and number of interchanges increased over time, with children in the SGD group showing even greater growth. Finally, children's total number of interchanges at baseline was positively associated with their spoken language gains over the course of intervention. This study supports the crucial relationship between social engagement and expressive language development, and highlights the need to include sustained communication interchanges as a target for intervention with this population. Autism Res 2016, 9: 1093-1102. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Background Duplications of 15q11.2-q13.1 (Dup15q syndrome) are highly penetrant for autism spectrum disorder (ASD). A distinct electrophysiological (EEG) pattern characterized by excessive activity in the beta band has been noted in clinical reports. We asked whether EEG power in the beta band, as well as in other frequency bands, distinguished children with Dup15q syndrome from those with non-syndromic ASD and then examined the clinical correlates of this electrophysiological biomarker in Dup15q syndrome. Methods In the first study, we recorded spontaneous EEG from children with Dup15q syndrome (n = 11), age-and-IQ-matched children with ASD (n = 10) and age-matched typically developing (TD) children (n = 9) and computed relative power in 6 frequency bands for 9 regions of interest (ROIs). Group comparisons were made using a repeated measures analysis of variance. In the second study, we recorded spontaneous EEG from a larger cohort of individuals with Dup15q syndrome (n = 27) across two sites and examined age, epilepsy, and duplication type as predictors of beta power using simple linear regressions. Results In the first study, spontaneous beta1 (12–20 Hz) and beta2 (20–30 Hz) power were significantly higher in Dup15q syndrome compared with both comparison groups, while delta (1–4 Hz) was significantly lower than both comparison groups. Effect sizes in all three frequency bands were large (|d| > 1). In the second study, we found that beta2 power was significantly related to epilepsy diagnosis in Dup15q syndrome. Conclusions Here, we have identified an electrophysiological biomarker of Dup15q syndrome that may facilitate clinical stratification, treatment monitoring, and measurement of target engagement for future clinical trials. Future work will investigate the genetic and neural underpinnings of this electrophysiological signature as well as the functional consequences of excessive beta oscillations in Dup15q syndrome.