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

dc.contributor.authorLong, Qunfang
dc.contributor.authorBhinge, Suchita
dc.contributor.authorCalhoun, Vince D.
dc.contributor.authorAdali, Tulay
dc.date.accessioned2021-02-12T19:54:44Z
dc.date.available2021-02-12T19:54:44Z
dc.date.issued2020-12-25
dc.description.abstractBackground 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.en
dc.description.sponsorshipThis work was supported by NSF grants CCF 1618551 and NCS 1631838, and NIH grants R01MH118695 and R01EB 020407. 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.en
dc.description.urihttps://www.sciencedirect.com/science/article/abs/pii/S0165027020304623?dgcid=rss_sd_all#!en
dc.format.extent21 pagesen
dc.genrejournal articles preprintsen
dc.identifierdoi:10.13016/m24xym-jxd7
dc.identifier.citationQunfang Long, Suchita Bhinge, Vince D. Calhoun, Tülay Adali, Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia, Journal of Neuroscience Methods, Volume 350, 2021, 109039, DOI: https://doi.org/10.1016/j.jneumeth.2020.109039.en
dc.identifier.urihttps://doi.org/10.1016/j.jneumeth.2020.109039
dc.identifier.urihttp://hdl.handle.net/11603/21011
dc.language.isoenen
dc.publisherElsevieren
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleGraph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophreniaen
dc.typeTexten

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