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Eichenbaum A, Pappas I, Lurie D, et al. Differential contributions of static and time-varying functional connectivity to human behavior. Netw Neurosci. 2021;5(1):145-165doi: 10.1162/netn_a_00172.
Eichenbaum, A., Pappas, I., Lurie, D., Cohen, J. R., & D'Esposito, M. (2021). Differential contributions of static and time-varying functional connectivity to human behavior. Network neuroscience (Cambridge, Mass.), 5(1), 145-165. https://doi.org/10.1162/netn_a_00172
Eichenbaum, Adam, et al. "Differential contributions of static and time-varying functional connectivity to human behavior." Network neuroscience (Cambridge, Mass.) vol. 5,1 (2021): 145-165. doi: https://doi.org/10.1162/netn_a_00172
Eichenbaum A, Pappas I, Lurie D, Cohen JR, D'Esposito M. Differential contributions of static and time-varying functional connectivity to human behavior. Netw Neurosci. 2021 Feb 01;5(1):145-165. doi: 10.1162/netn_a_00172. eCollection 2021. PMID: 33688610; PMCID: PMC7935045.
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Netw Neurosci. 2021 Feb 01;5(1):145-165. doi: 10.1162/netn_a_00172. eCollection 2021.

Differential contributions of static and time-varying functional connectivity to human behavior.

Network neuroscience (Cambridge, Mass.)

Adam Eichenbaum, Ioannis Pappas, Daniel Lurie, Jessica R Cohen, Mark D'Esposito

Affiliations

  1. Helen Wills Neuroscience Institute, University of California, Berkeley.
  2. Department of Psychology, University of California, Berkeley.
  3. Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill.

PMID: 33688610 PMCID: PMC7935045 DOI: 10.1162/netn_a_00172

Abstract

Measures of human brain functional connectivity acquired during the resting-state track critical aspects of behavior. Recently, fluctuations in resting-state functional connectivity patterns-typically averaged across in traditional analyses-have been considered for their potential neuroscientific relevance. There exists a lack of research on the differences between traditional "static" measures of functional connectivity and newly considered "time-varying" measures as they relate to human behavior. Using functional magnetic resonance imagining (fMRI) data collected at rest, and a battery of behavioral measures collected outside the scanner, we determined the degree to which each modality captures aspects of personality and cognitive ability. Measures of time-varying functional connectivity were derived by fitting a hidden Markov model. To determine behavioral relationships, static and time-varying connectivity measures were submitted separately to canonical correlation analysis. A single relationship between static functional connectivity and behavior existed, defined by measures of personality and stable behavioral features. However, two relationships were found when using time-varying measures. The first relationship was similar to the static case. The second relationship was unique, defined by measures reflecting trialwise behavioral variability. Our findings suggest that time-varying measures of functional connectivity are capable of capturing unique aspects of behavior to which static measures are insensitive.

© 2020 Massachusetts Institute of Technology.

Keywords: Canonical correlation analysis; Functional connectivity; Static functional connectivity; Time-varying functional connectivity

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

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