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

Abstract

Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

Deep Linear Modeling of Hierarchical Functional Connectivity in the Human Brain

“…Whereas many widely used methods for performing time-varying fMRI analysis are heuristic rather than data-driven, such as those with arbitrary time windows (Iraji et al, 2020), advances in fMRI temporal resolution can be combined with deep linear models that perform joint spatiotemporal decomposition for principled unsupervised dynamic functional connectivity mapping that reveals ever more of the human brain's hierarchical organization. Zhang, W., Jiang, X., Zhang, S., Howell, B. R., Zhao, Y., Zhang, T., ... & Liu, T. (2017).…”

Multi-Spatial Scale Dynamic Interactions between Functional Sources Reveal Sex-Specific Changes in Schizophrenia

“…A window-based dFNC approach (Allen et al, 2014;Iraji et al, 2020a) was adopted to characterize the multi-spatial scale dynamic functional interactions. To our best knowledge, this is the first study that looks at sFNC/dFNC across multiple mode orders.…”

Brain-Environment Alignment during Movie Watching Predicts Cognitive-Affective Function in Adulthood

“…Third, although behavioural event segmentation is largely preserved in healthy ageing (Kurby & Zacks, 2018;Reagh et al, 2020;Sargent et al, 2013), future studies probing the link between event segmentation performance and its neural substrates across the lifespan would be critical in furthering our understanding of developmental differences in information processing. Fourth, research employing alternate methods for estimating dynamic brain Brain-Environment Alignment 17 reconfiguration, including data-driven approaches, would shed light on the boundary conditions of the effects herein documented (Gonzalez-Castillo & Bandettini, 2018;Iraji et al, 2020).…”

“…These template space dimensions were selected ROIs across the Cam-Can adult lifespan sample (cf. Iraji et al, 2020), we used an approach that is conceptually similar to those recently used in the literature (Gordon et al, 2016;Siegel et al, 2016). Specifically, we used the CONN toolbox to compute the radial similarity contrast (RSC) for each voxel in the Power atlas.…”

“…To characterize individual differences in dynamic network structure, we used a combined window and clustering based approach (Iraji et al, 2020;Lurie et al, 2020). Thus, we broke down the movie into partially overlapping 40 s long windows for a total of 177 windows.…”