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

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 < .001 and decreased FT in a highly-connected state p < .001. SZ further showed decreased number of transitions between states p < .001, decreased FT in a widely-connected state p < .001, increased dwell time (DT) in the weakly-connected state p < .001, and decreased DT in the highly-connected state p = .001. Social behavior scores correlated with DT in the widely-connected state in SZ r = 0.416, p = .043, but not ASD. Classification correctly identified SZ at high rates (81.8%), while ASD and HC at lower rates. CONCLUSIONS: Results 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.