Qunfang Long1, Suchita Bhinge2, Vince D Calhoun3, Tülay Adali2. 1. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA. Electronic address: qunfang1@umbc.edu. 2. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA. 3. The Mind Research Network, Albuquerque, NM, 87131, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
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.
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.
Authors: Armin Iraji; Thomas P Deramus; Noah Lewis; Maziar Yaesoubi; Julia M Stephen; Erik Erhardt; Aysneil Belger; Judith M Ford; Sarah McEwen; Daniel H Mathalon; Bryon A Mueller; Godfrey D Pearlson; Steven G Potkin; Adrian Preda; Jessica A Turner; Jatin G Vaidya; Theo G M van Erp; Vince D Calhoun Journal: Hum Brain Mapp Date: 2019-03-18 Impact factor: 5.399
Authors: Mustafa S Çetin; Fletcher Christensen; Christopher C Abbott; Julia M Stephen; Andrew R Mayer; José M Cañive; Juan R Bustillo; Godfrey D Pearlson; Vince D Calhoun Journal: Neuroimage Date: 2014-04-13 Impact factor: 6.556