Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification.

ABSTRACT:

Background

Over the recent years there has been a growing debate regarding the extent and nature of the overlap in neuropathology between schizophrenia (SZ) and autism spectrum disorder (ASD). Dynamic functional network connectivity (dFNC) is a recent analysis method that explores temporal patterns of functional connectivity (FC). We compared resting-state dFNC in SZ, ASD and healthy controls (HC), characterized the associations between temporal patterns and symptoms, and performed a three-way classification analysis based on dFNC indices.

Methods

Resting-state fMRI was collected from 100 young adults: 33 SZ, 33 ASD, 34 HC. Independent component analysis (ICA) was performed, followed by dFNC analysis (window?=?33?s, step?=?1TR, k-means clustering). Temporal patterns were compared between groups, correlated with symptoms, and classified via cross-validated three-way discriminant analysis.

Results

Both clinical groups displayed an increased fraction of time (FT) spent in a state of weak, intra-network connectivity [p?ConclusionsResults indicate a severe and pervasive pattern of temporal aberrations in SZ (specifically, being "stuck" in a state of weak connectivity), that distinguishes SZ participants from both ASD and HC, and is associated with clinical symptoms.

SUBMITTER: Rabany L 

PROVIDER: S-EPMC6700449 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature