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Front Neurosci. 2019 Jun 27;13:634. doi: 10.3389/fnins.2019.00634. eCollection 2019.

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

Author information

1
Mind Research Network, Albuquerque, NM, United States.
2
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
3
Department of Mathematics and Statistics, The University of New Mexico, Albuquerque, NM, United States.
4
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States.
5
Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, United States.
6
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
7
Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States.
8
San Francisco VA Medical Center, San Francisco, CA, United States.
9
Pacific Neuroscience Institute, Santa Monica, CA, United States.
10
John Wayne Cancer Institute, Department of Translational Neurosciences and Neurotherapeutics, Santa Monica, CA, United States.
11
Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States.
12
Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States.
13
Department of Psychiatry, The University of Iowa, Iowa City, IA, United States.
14
Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States.
15
Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States.

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.

KEYWORDS:

derivatives; functional network connectivity; group independent component analysis; resting state fMRI; windowed correlation

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