Topological and Graph Theoretical Analysis of Dynamic Functional Connectivity for Autism Spectrum Disorder

TL;DR

This study applies topological data analysis and graph theory to brain activity data, revealing distinct network features in ASD patients that could serve as biomarkers for diagnosis.

Contribution

It introduces a novel combination of TDA and graph theory techniques to analyze brain connectivity in ASD, providing new quantitative biomarkers.

Findings

ASD brains show decreased modularity in network structure

Higher von Neumann entropy observed in ASD brain networks

Increased Betti-0 and decreased Betti-1 numbers in ASD

Abstract

Autism Spectrum Disorder (ASD) is a prevalent neurological disorder. However, the multi-faceted symptoms and large individual differences among ASD patients are hindering the diagnosis process, which largely relies on subject descriptions and lacks quantitative biomarkers. To remediate such problems, this paper explores the use of graph theory and topological data analysis (TDA) to study brain activity in ASD patients and normal controls. We employ the Mapper algorithm in TDA and the distance correlation graphical model (DCGM) in graph theory to create brain state networks, then innovatively adopt complex network metrics in Graph signal processing (GSP) and physical quantities to analyze brain activities over time. Our findings reveal statistical differences in network characteristics between ASD and control groups. Compared to normal subjects, brain state networks of ASD patients tend to have decreased modularity, higher von Neumann entropy, increased Betti-0 numbers, and decreased Betti-1 numbers. These findings attest to the biological traits of ASD, suggesting less organized and more variable brain dynamics. These findings offer potential biomarkers for ASD diagnosis and deepen our understanding of its neural correlations.