The neural basis of time-varying resting-state functional connectivity.

Abstract

Dynamic network analysis based on resting-state magnetic resonance imaging (rsMRI) is a fairly new and potentially powerful tool for neuroscience and clinical research. Dynamic analysis can be sensitive to changes that occur in psychiatric or neurologic disorders and can detect variations related to performance on individual trials in healthy subjects. However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies. Researchers have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals and in humans. In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in interpretation of these studies. The literature suggests that multiple brain processes may contribute to the dynamics observed, and we speculate that it may be possible to separate particular aspects of the rsMRI signal to enhance sensitivity to certain types of neural activity, providing new tools for basic neuroscience and clinical research.