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Author Notes:

Correspondence: qunfang1@umbc.edu

Author contributions: Qunfang Long: Methodology, Software, Validation, Formal analysis, Investigation, Writing of the Original draft, Visualization. Suchita Bhinge: Methodology, Investigation. Vince Calhoun: Resources, Data Curation, Writing-Reviewing and Editing, Funding Acquisition. Tulay Adali: Conceptualization, Resources, Supervision, Writing-Reviewing and Editing, Project Administration, Funding Acquisition.

Acknowledgements: The authors thank the research staff from the Mind Research Network COBRE study who collected, preprocessed and shared the data.

The authors appreciate valuable feedback provided by the members of Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County.

Disclosures: Authors have no competing interests to declare.

Subjects:

Research Funding:

This work was supported by NSF grants CCF 1618551 and NCS 1631838, and NIH grants R01MH 118695 and R01EB 020407.

Keywords:

  • Adaptively constrained independent vector analysis
  • Dynamic functional network connectivity
  • Dynamic study
  • Functional magnetic resonance imaging
  • Graph-theoretical analysis
  • Brain
  • Brain Mapping
  • Humans
  • Magnetic Resonance Imaging
  • Schizophrenia

Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia

Tools:

Journal Title:

Journal of Neuroscience Methods

Volume:

Volume 350

Publisher:

, Pages 109039-109039

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Background: Dynamic functional network connectivity (dFNC) summarizes associations among time-varying brain networks and is widely used for studying dynamics. However, most previous studies compute dFNC using temporal variability while spatial variability started receiving increasing attention. It is hence desirable to investigate spatial variability and the interaction between temporal and spatial variability. New method: We propose to use an adaptive variant of constrained independent vector analysis to simultaneously capture temporal and spatial variability, and introduce a goal-driven scheme for addressing a key challenge in dFNC analysis---determining the number of transient states. We apply our methods to resting-state functional magnetic resonance imaging data of schizophrenia patients (SZs) and healthy controls (HCs). Results: The results show spatial variability provides more features discriminative between groups than temporal variability. A comprehensive study of graph-theoretical (GT) metrics determines the optimal number of spatial states and suggests centrality as a key metric. Four networks yield significantly different levels of involvement in SZs and HCs. The high involvement of a component that relates to multiple distributed brain regions highlights dysconnectivity in SZ. One frontoparietal component and one frontal component demonstrate higher involvement in HCs, suggesting a more efficient cognitive control system relative to SZs. Comparison with existing methods: Spatial variability is more informative than temporal variability. The proposed goal-driven scheme determines the optimal number of states in a more interpretable way by making use of discriminative features. Conclusion: GT analysis is promising in dFNC analysis as it identifies distinctive transient spatial states of dFNC and reveals unique biomedical patterns in SZs.

Copyright information:

© 2020 Elsevier B.V. All rights reserved.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/rdf).
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