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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.

Published: May 29, 2018, There is a current interest in quantifying time-varying connectivity (TVC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brain’s dynamics. However, given that the ground truth for TVC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exist many TVC methods it is difficult to assess differences in time-varying connectivity between studies. In this paper, we present tvc_benchmarker, which is a Python package containing four simulations to test TVC methods. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating TVC (sliding window, tapered sliding window, multiplication of temporal derivatives, spatial distance and jackknife correlation). These simulations were designed to test each method’s ability to track changes in covariance over time, which is a key property in TVC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future TVC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. Using tvc_benchmarker researchers can easily add, compare and submit their own TVC methods to evaluate its performance.

Studying the brain’s structure and function as a network has provided insight about the brain’s activity in health and disease. Networks in the brain are often averaged over trials, frequency and time and this is called functional connectivity. This thesis aims to extend the analyses beyond these assumptions and simplifications. Connectivity that varies over time has been called dynamic functional connectivity. This thesis considers different ways to derive a dynamic functional connectivity representation of the brain and subsequently quantify this using temporal network theory. Paper I: discusses different interpretations about what can be considered “interesting” or “high” dynamic functional connectivity. The choices made here can prioritize different edges. Paper II: discusses how the stability of the variance of dynamic connectivity time series can be achieved. This is an important preprocessing step in dynamic functional connectivity as it can bias the subsequent analysis if done incorrectly. Paper III: quantify the degree of burstiness, the distribution of temporal connections, between different edges in fMRI data. Paper IV: provides an introduction and application of metrics from temporal network theory onto fMRI activity. Paper V: multi-layer network analysis of resting state networks over different frequencies of the BOLD response. This work shows that a full analysis of the network structure of the brain in fMRI may require considering networks over frequency. Paper VI: Investigates whether the functional connectivity at time of trauma for patient with traumatic brain injury (TBI) correlates with features related to long term cognitive outcome. Paper VII: is a mass meta-analysis using Neurosynth to cluster different brain networks from different tasks into a hierarchical network structure. This provides the start of a data driven hierarchical network atlas for different tasks. Paper VIII: is a conceptual overview of the different assumptions made in many p