Activity flow over resting-state networks shapes cognitive task activations

By Michael W. Cole, Takuya Ito, D. Bassett, D. Schultz. Nature Neuroscience, 2016

489 citations recorded by the source corpus.

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/18ee19f7ef75fc0785083848f9545593859b7cc2

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Citations in the corpus

Advancing functional connectivity research from association to causation (2019, methodology): “…FC measures that, unlike pairwise correlation and coherence, take confounding into account via fitting all measured time series simultaneously, such as partial correlation, multiple regression 91 , dynamic causal modeling 40 , multivariate Granger causality 92 , and Bayesian search approaches 88 .”
Emotion and the Prefrontal Cortex: An Integrative Review (2017, methodology): “The framework provided here, in conjunction with recent advances in network neuroscience (Bassett et al., 2014; Calhoun, Miller, Pearlson, & Adali, 2014; Cole et al., 2016; Davison et al., 2015; Hutchison et al., 2013; Zalesky et al., 2014), may prove helpful in elucidating the dynamic nature of emo”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “…Hebbian-like connections between regions and thereby stronger spontaneous correlations (Dosenbach et al., 2007), and/or (2) evoked signals directly contributing to functional network measurements, as activations ‘‘flow’’ through a network, influencing correlation structure (Cole et al., 2016).”
Communication dynamics in complex brain networks (2017, background): “On the one hand, nodes with many connections are well positioned to facilitate integration of information both within and between specialized modules 29,40,49,86,99,100 .”