purpose. The application of fluid flow (dynamic) for the physiological nutrition of the tissues and the creation of microenvironmental biomolecular gradients and relevant mechanical cues (e.g., shear stress) is a major aspect of these systems, differentiating them from conventional (static) cell and tissue cultures. This review uses the term MPS exclusively for microfluidic sys- Introduction Definitions and terminologyMicrophysiological systems (MPS) are microfluidic devices capable of emulating human (or any other animal species') biology in vitro at the smallest biologically acceptable scale, defined by t 4 Workshop Report*

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event‐related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non‐verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non‐verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non‐verbal cognition and adaptive social function.

Synopsis While the diagnosis of Autism Spectrum Disorder 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. Using chromosomal microarray and whole exome sequencing technology, more than 25% of children with ASD have an identifiable, causative genetic variant or syndrome, and this rate continues to increase with improved methods and widespread use of genetic testing. The identification of genetic variants has been accompanied by a concerted effort to define more homogeneous clinical syndromes that are informed by the underlying genetic etiology of a child’s ASD. In the future, such characterization will facilitate targeted treatments based on mechanisms of disease and common clinical features. 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.

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.

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