Using Brain Connectivity Measure of EEG Synchrostates for Discriminating Typical and Autism Spectrum Disorder

TL;DR

This study leverages EEG synchrostates and graph theory to differentiate brain connectivity patterns between children with autism spectrum disorder and typically developing children, potentially aiding non-invasive ASD diagnosis.

Contribution

Introduces a novel EEG-based brain connectivity network using synchrostates and graph measures to distinguish ASD from typical development.

Findings

Children with ASD show different network modularity from controls.

EEG synchrostates can be used to form discriminative brain connectivity networks.

Potential for non-invasive ASD identification using EEG data.

Abstract

In this paper we utilized the concept of stable phase synchronization topography - synchrostates - over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data.