Dissemin is shutting down on January 1st, 2025

Published in

Massachusetts Institute of Technology Press, Network Neuroscience, 1(4), p. 30-69, 2020

DOI: 10.1162/netn_a_00116

Links

Tools

Export citation

Search in Google Scholar

Questions and controversies in the study of time-varying functional connectivity in resting fMRI

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Orange circle
Preprint: archiving restricted
Orange circle
Postprint: archiving restricted
Green circle
Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.