Journal article
Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations
Frontiers in neuroscience, Vol.13, pp.634-634
06/27/2019
DOI: 10.3389/fnins.2019.00634
PMCID: PMC6611425
PMID: 31316333
Abstract
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.
Details
- Title: Subtitle
- Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations
- Creators
- Flor A Espinoza - Mind Research NetworkVictor M Vergara - Mind Research NetworkEswar Damaraju - Mind Research NetworkKyle G Henke - Mind Research NetworkAshkan Faghiri - Mind Research NetworkJessica A Turner - Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory UniversityAysenil A Belger - Department of Psychiatry, University of North Carolina at Chapel HillJudith M Ford - Department of Psychiatry, University of California, San FranciscoSarah C McEwen - Pacific Neuroscience InstituteDaniel H Mathalon - Department of Psychiatry, University of California, San FranciscoBryon A Mueller - Department of Psychiatry, University of MinnesotaSteven G Potkin - Department of Psychiatry and Human Behavior, University of California, IrvineAdrian Preda - Department of Psychiatry and Human Behavior, University of California, IrvineJatin G Vaidya - Department of Psychiatry, The University of IowaTheo G. M van Erp - Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, IrvineVince D Calhoun - Mind Research Network
- Resource Type
- Journal article
- Publication Details
- Frontiers in neuroscience, Vol.13, pp.634-634
- DOI
- 10.3389/fnins.2019.00634
- PMID
- 31316333
- PMCID
- PMC6611425
- NLM abbreviation
- Front Neurosci
- ISSN
- 1662-4548
- eISSN
- 1662-453X
- Publisher
- Frontiers Media S.A
- Grant note
- R01EB020407; P20GM103472; P30GM122734 / National Institutes of Health
- Language
- English
- Date published
- 06/27/2019
- Academic Unit
- Psychiatry; Iowa Neuroscience Institute; University College Courses
- Record Identifier
- 9984070376502771
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