Dynamic functioning of transient resting‐state coactivation networks in the Human Connectome Project

Abstract

Resting-state analyses evaluating large-scale brain networks have largely focused on static correlations in brain activity over extended time periods, however emerging approaches capture time-varying or dynamic patterns of transient functional networks. In light of these new approaches, there is a need to classify common transient network states (TNS) in terms of their spatial and dynamic properties. To fill this gap, two independent resting state scans collected in 462 healthy adults from the Human Connectome Project were evaluated using coactivation pattern analysis to identify (eight) TNS that recurred across participants and over time. These TNS spatially overlapped with prototypical resting state networks, but also diverged in notable ways. In particular, analyses revealed three TNS that shared cortical midline overlap with the default mode network (DMN), but these "complex" DMN states also encompassed distinct regions that fall beyond the prototypical DMN, suggesting that the DMN defined using static methods may represent the average of distinct complex-DMN states. Of note, dwell time was higher in "complex" DMN states, challenging the idea that the prototypical DMN, as a single unit, is the dominant resting-state network as typically defined by static resting state methods. In comparing the two resting state scans, we also found high reliability in the spatial organization and dynamic activities of network states involving DMN or sensorimotor regions. Future work will determine whether these TNS defined by coactivation patterns are in other samples, and are linked to fundamental cognitive properties.

Reproducible Coactivation Patterns of Functional Brain Networks Reveal the Aberrant Dynamic State Transition in Schizophrenia

“…For instance, when DMN was activated, the FPN and DAN were deactivated (State 3), and vice versa is true for State 6. The phenomenon of opposite CAP pairs has also been found in previous studies (Huang et al, 2020;Janes et al, 2020;Zhang et al, 2020), suggesting these regions tend to be activated in an opposite manner that the activation of region A would suppress the activity of another region B, and vice versa.…”

“…Both voxel-level and ROI-level (Janes et al, 2020) were studied in previous CAP analysis studies. Using ROI could reduce the dimension and save a lot of time and computational resources , while it could also decrease the spatial resolution and ignore spatial details.…”

Temporal Dynamics of Large-Scale Networks Predict Neural Cue Reactivity and Cue-Induced Craving

“…The analysis is described in more details in a published manuscript (21) and further descriptions are provided in the supplementary material of this study. Briefly, we applied the maps of the DMN and SN coactivation patterns (CAPs) or "states" derived from a sample of 462 individuals from the Human Connectome Project (HCP) in (21). The DMN state (Supplementary Fig.…”

“…To address this gap, we evaluated nicotine-dependent participants using fMRI and collected data first at rest and then during a smoking cue-reactivity task. For the resting-state data, we applied a novel co-activation pattern analysis, which we used previously in a sample of healthy individuals (N = 462) to parcellate the brain into distinct networks-of-interest including the DMN and SN (21). We applied these previously-identified states to calculate the total time spent in the DMN and SN across the entire resting timeseries.…”

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

“…Several studies (Allen et al, 2014;Jafri et al, 2008;Pervaiz et al, 2020;Smith et al, 2012) have demonstrate that time course of spatial independent components can identify intrinsic brain networks. The performance of TCA with different templates (Glasser et al, 2016;Janes et al, 2019;Shen et al, 2013) was also compared and ICA template performs best. However, several studies have demonstrated that the model order can greatly impact on the estimated components (Abou-Elseoud et al, 2010;Beckmann, 2012;Kuang et al, 2018).…”