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.

Significance: It was proposed that individuals with attention-deficit/hyperactivity disorder (ADHD) exhibit delays in brain maturation. In the last decade, resting state functional imaging has enabled detailed investigation of neural connectivity patterns and has revealed that the human brain is functionally organized into large-scale connectivity networks. In this study, we demonstrate that the developing relationships between default mode network (DMN) and task positive networks (TPNs) exhibit significant and specific maturational lag in ADHD. Previous research has found that individuals with ADHD exhibit abnormalities in DMN–TPN relationships. Our results provide strong initial evidence that these alterations arise from delays in typical maturational patterns. Our results invite further investigation into the neurobiological mechanisms in ADHD that produce delays in development of large-scale networks.

Normative growth charts (eg, pediatric growth charts for height, weight, and head circumference) have been produced for physical or other biometric characteristics for at least 200 years and have proved valuable in the early identification of clinically relevant states and conditions.¹ With the advent of resting-state functional neuroimaging,² the potential now exists to extend growth charting methods to maturing functional relationships in the brain. The human brain is organized into several large-scale intrinsic connectivity networks (ICNs), each associated with distinct neurocognitive functions.^3,4 Moreover, relationships within and between ICNs exhibit clear trajectories of change from childhood to young adulthood.^5-9 It is plausible, then, that deviations from normative trajectories of network maturation might be predictive of a range of clinically important psychological characteristics and conditions.

Resting state functional connectomes are massive and complex. It is an open question, however, whether connectomes differ across individuals in a correspondingly massive number of ways, or whether most differences take a small number of characteristic forms. We systematically investigated this question and found clear evidence of low-rank structure in which a modest number of connectomic components, around 50–150, account for a sizable portion of inter-individual connectomic variation. This number was convergently arrived at with multiple methods including estimation of intrinsic dimensionality and assessment of reconstruction of out-of-sample data. In addition, we show that these connectomic components enable prediction of a broad array of neurocognitive and clinical symptom variables at levels comparable to a leading method that is trained on the whole connectome. Qualitative observation reveals that these connectomic components exhibit extensive community structure reflecting interrelationships between intrinsic connectivity networks. We provide quantitative validation of this observation using novel stochastic block model-based methods. We propose that these connectivity components form an effective basis set for quantifying and interpreting inter-individual connectomic differences, and for predicting behavioral/clinical phenotypes.