Characterizing whole brain temporal variation of functional connectivity via zero and first order derivatives of sliding window correlations

Flor A. Espinoza, Victor M. Vergara, Eswar Damaraju, Kyle G. Henke, Ashkan Faghiri, Jessica A. Turner, Aysenil A. Belger, Judith M. Ford, Sarah C. McEwen, Daniel H. Mathalon, Bryon A. Mueller, Steven G. Potkin, Adrian Preda, Jatin G. Vaidya, Theo G.M. Van Erp, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article number634
JournalFrontiers in Neuroscience
Volume13
Issue numberJUN
DOIs
StatePublished - 2019

Bibliographical note

Funding Information:
This work was supported by grants from the National Institutes of Health (R01EB020407, P20GM103472, and P30GM122734) and the National Science Foundation (1539067).

Publisher Copyright:
Copyright © 2019 Espinoza, Vergara, Damaraju, Henke, Faghiri, Turner, Belger, Ford, McEwen, Mathalon, Mueller, Potkin, Preda, Vaidya, van Erp and Calhoun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Keywords

  • Derivatives
  • Functional network connectivity
  • Group independent component analysis
  • Resting state fMRI
  • Windowed correlation

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