The Influence of Brain State on Functional Connectivity in Autism.

that the disorder involves atypical development of brain connectivity, several open questions remain with regard to the precise nature of these atypicalities. A limitation of the current neuroimaging literature is a focus on adulthood autism, with recent work beginning to examine late childhood and adolescence (Uddin et al., 2013). In this issue of EBioMedicine, Buckley and colleagues contribute to this emerging literature by analyzing EEG data collected from a large sample of young children (age 2–6) with autism spectrum disorder (ASD), developmental delay without autism (DD), and typical development (TYP) (Buckley et al., 2015). Previous studies of functional connectivity in autism have examined either task-related or resting-state connectivity, with mixed findings of overand under-compared with neurotypical populations (see Just et al., 2012; Muller et al., 2011 for reviews). Very few have examined differential brain connectivity as a function of brain state, with the exception of some recent functional MRI work examining task-rest differentiation in autism (Barttfeld et al., 2012; Uddin et al., 2014; You et al., 2013). The current study by Buckley and colleagues took the unique approach of examining functional brain connectivity using EEG spectral power, coherence, phase lag, Pearson and partial correlations. As EEG data can more easily be collected from young children, this allowed the researchers to examine a very young and low functioning sample compared with studies reporting data acquired using MRI, which is much more susceptible to motion artifact and requires more patient compliance. The authors were further able to examine functional connectivity differences in autism as a function of brain state, as they collected data during the awake, slow wave sleep (SWS), and rapid eye movement sleep (REM) states. A final innovation was the use of machine learning to classify children with autism based on correlation