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 journalArticle

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 - Jan 1 2019
Externally publishedYes

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Schizophrenia
Autism Spectrum Disorder
Social Behavior
Discriminant Analysis
Cluster Analysis
Young Adult
Magnetic Resonance Imaging

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

Cite this

Dynamic functional connectivity in schizophrenia and autism spectrum disorder : Convergence, divergence and classification. / Rabany, L.; Brocke, S.; Calhoun, Vince D.; Pittman, B.; Corbera, Silvia; Wexler, Bruce E.; Bell, Morris D.; Pelphrey, K.; Pearlson, Godfrey D.; Assaf, Michal.

In: NeuroImage: Clinical, Vol. 24, 101966, 01.01.2019.

Research output: Contribution to journalArticle

Rabany, L, Brocke, S, Calhoun, VD, Pittman, B, Corbera, S, Wexler, BE, Bell, MD, Pelphrey, K, Pearlson, GD & Assaf, M 2019, 'Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification', NeuroImage: Clinical, vol. 24, 101966. https://doi.org/10.1016/j.nicl.2019.101966
Rabany, L. ; Brocke, S. ; Calhoun, Vince D. ; Pittman, B. ; Corbera, Silvia ; Wexler, Bruce E. ; Bell, Morris D. ; Pelphrey, K. ; Pearlson, Godfrey D. ; Assaf, Michal. / Dynamic functional connectivity in schizophrenia and autism spectrum disorder : Convergence, divergence and classification. In: NeuroImage: Clinical. 2019 ; Vol. 24.
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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.",
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T2 - Convergence, divergence and classification

AU - Rabany, L.

AU - Brocke, S.

AU - Calhoun, Vince D.

AU - Pittman, B.

AU - Corbera, Silvia

AU - Wexler, Bruce E.

AU - Bell, Morris D.

AU - Pelphrey, K.

AU - Pearlson, Godfrey D.

AU - Assaf, Michal

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KW - Schizophrenia

KW - Social cognition

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