Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated...

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Published in:	Frontiers in neuroscience Vol. 13; p. 634
Main Authors:	Espinoza, Flor A, Vergara, Victor M, Damaraju, Eswar, Henke, Kyle G, Faghiri, Ashkan, Turner, Jessica A, Belger, Aysenil A, Ford, Judith M, McEwen, Sarah C, Mathalon, Daniel H, Mueller, Bryon A, Potkin, Steven G, Preda, Adrian, Vaidya, Jatin G, van Erp, Theo G M, Calhoun, Vince D
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Research Foundation 27-06-2019 Frontiers Media S.A
Subjects:	Brain mapping Brain research Clustering derivatives Functional magnetic resonance imaging functional network connectivity group independent component analysis Mental disorders Neural networks Neuroscience Neurosciences Psychiatry resting state fMRI Schizophrenia Temporal variations windowed correlation Atlanta Georgia San Francisco California Georgia California New Mexico United States > US derivatives resting state fMRI functional network connectivity group independent component analysis windowed correlation
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Summary:	Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Joseph Kambeitz, Ludwig Maximilian University of Munich, Germany; Hyunjin Park, Sungkyunkwan University, South Korea This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience Edited by: Ofer Pasternak, Harvard Medical School, United States
ISSN:	1662-4548 1662-453X 1662-453X
DOI:	10.3389/fnins.2019.00634