Cofluctuation analysis reveals aberrant default mode network patterns in adolescents and youths with autism spectrum disorder

Abstract

Resting‐state functional connectivity (rsFC) approaches provide informative estimates of the functional architecture of the brain, and recently‐proposed cofluctuation analysis temporally unwraps FC at every moment in time, providing refined information for quantifying brain dynamics. As a brain network disorder, autism spectrum disorder (ASD) was characterized by substantial alteration in FC, but the contribution of moment‐to‐moment‐activity cofluctuations to the overall dysfunctional connectivity pattern in ASD remains poorly understood. Here, we used the cofluctuation approach to explore the underlying dynamic properties of FC in ASD, using a large multisite resting‐state functional magnetic resonance imaging (rs‐fMRI) dataset (ASD = 354, typically developing controls [TD] = 446). Our results verified that the networks estimated using high‐amplitude frames were highly correlated with the traditional rsFC. Moreover, these frames showed higher average amplitudes in participants with ASD than those in the TD group. Principal component analysis was performed on the activity patterns in these frames and aggregated over all subjects. The first principal component (PC1) corresponds to the default mode network (DMN), and the PC1 coefficients were greater in participants with ASD than those in the TD group. Additionally, increased ASD symptom severity was associated with the increased coefficients, which may result in excessive internally oriented cognition and social cognition deficits in individuals with ASD. Our finding highlights the utility of cofluctuation approaches in prevalent neurodevelopmental disorders and verifies that the aberrant contribution of DMN to rsFC may underline the symptomatology in adolescents and youths with ASD.

Higher‐order functional connectivity analysis of resting‐state functional magnetic resonance imaging data using multivariate cumulants

“…Thus, edge connectivity measures the correlation between two pairs of brain regions. It has been applied to study higher‐order connectivity between cortical and sub‐cortical structures (Chumin et al, 2022; Korponay & Ph, 2022) and in several clinical populations, including autism spectrum disorder (Esfahlani et al, 2022; Li et al, 2022), auditory processing disorder (Alvand et al, 2022), and stroke patients (Idesis et al, 2022). A related, but more general measure for higher‐order connectivity, was introduced in (Santoro et al, 2022) and quantifies the strength of the connectivity between $$$ k $$$ brain regions (i.e., $$$ k $$$‐th order connectivity) for k < 2.…”