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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.

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.

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.

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

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.