In the context of increasing prevalence the brain bases of autism spectrum disorder (ASD) remain incompletely understood. There is growing consensus that ASD is a disorder of brain connectivity, but functional connectivity findings are inconsistent. Resting state functional MRI (rs-fMRI) data, considered ideal for the study of intrinsic functional connectivity, are being acquired by numerous groups and are now available in a large public database. However, known differences in behavioral and cognitive traits may significantly affect fMRI data collected in the 'resting state' and little is known about potential confounds that may result. This lack of knowledge is troubling in view of the ill-defined nature of the 'resting state', which largely eludes experimental control. Specifically, rs-fMRI connectivity has been solely examined using static approaches. The current proposal will use dynamic fMRI sliding window analyses in combination with EEG to investigate the dynamic variability of functional connectivity during the resting state. Samples of adolescents with ASD and matched typically developing (TD) participants from existing cohorts will be scanned using rs-fMRI with concurrent EEG. In two aims we will (1) test dynamic changes in connectivity across time, using a sliding window rs-fMRI analysis; and (2) use EEG data to examine dynamic changes in high temporal frequency bands and characterize temporal patterns observed in Aim 1 with respect to electrophysiological changes. We hypothesize that ASD participants will show greater variability of connectivity across time in and between several networks of interest (default mode, dorsal attention, saliency), accompanied by variability in electrophysiological states. The proposed project will be the first to combine fMRI and EEG for an in-depth investigation of dynamic changes during the 'resting state' in ASD. A better understanding of potentially systematic differences between ASD and TD participants in response to the resting state is a prerequisite for the adequate interpretation of group differences detected in rs-fMRI functional connectivity studies and is therefore urgently needed.
This exploratory project will use combined functional MRI and EEG to examine dynamic changes in brain connectivity during the 'resting state', which is commonly used in autism studies, but not well understood. Findings will be important for an adequate interpretation of connectivity findings from resting state fMRI data.
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