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

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.

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

“…In this manuscript, we review some research on the potential association of fMRI-dFC with cognitive demands and behavioral performance. We also present examples of how dFC patterns are disrupted in various brain disorders and the relationship of atypical dFC patterns with both cognitive impairments and the outcomes of the disorders (for reviews see Keilholz et al, 2017;Cohen, 2018;Thompson, 2018;Lurie et al, 2020)). Alterations in dFC patterns across a wide range of conditions have been observed even in the absence of sFC differences.…”

“…However, spontaneous brain activity is rich with dynamic properties which are disregarded in such a method. Therefore, research into whole brain dynamic functional connectivity (dFC) has become a burgeoning field of study since initial work on the topic (Sakoglu et al, 2010;Lurie et al, 2020). Although one can distinguish between approaches that leverage the time-varying signal and those that more explicitly model/capture dynamics over time (Lurie et al, 2020), we refer to both as dFC for convenience.…”

“…Therefore, research into whole brain dynamic functional connectivity (dFC) has become a burgeoning field of study since initial work on the topic (Sakoglu et al, 2010;Lurie et al, 2020). Although one can distinguish between approaches that leverage the time-varying signal and those that more explicitly model/capture dynamics over time (Lurie et al, 2020), we refer to both as dFC for convenience. dFC is defined as time-varying functional connectivity and contains information regarding the temporal reconfiguration of functional entities (also called sources).…”

“…It should be noted that because fMRI is an indirect measurement of neural activity, fluctuations in FC estimated by fMRI are also indirect representations of dFC. There is ongoing discussion regarding how well these fluctuations capture the underlying brain dynamism, but previous studies provide a significant amount of evidence to support the potential relationship between the fluctuations in FC obtained from fMRI and neural dynamics in the brain (for review see (Lurie et al, 2020)). For instance, simultaneous fMRI and EEG imaging studies show that fluctuations in FC obtained from rsfMRI are associated with electrophysiological signatures of EEG Allen et al, 2018).…”

Core and Matrix Thalamic Sub-Populations Relate to Spatio-Temporal Cortical Connectivity Gradients

“…To quantify the relationship between CPC and time-varying network topology, we used a sliding-window Pearson's correlation design to estimate windowed patterns of undirected functional connectivity between all 400 cerebral cortical. In contrast to typical sliding-window approaches (Lurie et al, 2020), we split regional time series data into 20 non-overlapping windows (each of 50 TRs). This approach was adopted so as to avoid artificial smoothing that occurs with strongly overlapping windows.…”