Format

Send to

Choose Destination
Neuroimage Clin. 2019 Aug 1;24:101966. doi: 10.1016/j.nicl.2019.101966. [Epub ahead of print]

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

Author information

1
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA. Electronic address: lironrb@gmail.com.
2
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA.
3
Mind Research Network, Albuquerque, NM, USA; University of New Mexico, Department of ECE, Albuquerque, NM, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA.
4
Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA.
5
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Central Connecticut State University, Department of Psychological Science, New Britain, CT, USA.
6
Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA; VA Connecticut Healthcare System West Haven, CT, USA.
7
Autism and Neurodevelopment Disorders Institute, George Washington University and Children's National Medical Center, DC, USA.
8
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA; Yale University School of Medicine, Department of Neuroscience, New Haven, CT, USA.
9
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA.

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.

KEYWORDS:

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

PMID:
31401405
DOI:
10.1016/j.nicl.2019.101966
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center