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

L. Rabany, S. Brocke, Vince D. Calhoun, B. Pittman, Silvia Corbera, Bruce E. Wexler, Morris D. Bell, K. Pelphrey, Godfrey D. Pearlson, Michal Assaf

Research output: Contribution to journalArticlepeer-review

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 < .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.

Original languageEnglish (US)
Article number101966
JournalNeuroImage: Clinical
Volume24
DOIs
StatePublished - 2019

Keywords

  • Autism spectrum disorder
  • Classification
  • Connectivity dynamics
  • Dynamic functional connectivity (dFNC)
  • Resting state fMRI
  • Schizophrenia
  • Social cognition

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience

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