The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition

By Jessica R. Cohen, Mark D'Esposito. Journal of Neuroscience, 2016

698 citations recorded by the source corpus.

A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition. SIGNIFICANCE STATEMENT The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain's ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/fba8fe36475509612cd829a253a276e5166ff42a

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In Cognitive Neuroscience

Citations in the corpus

The role of prefrontal cortex in cognitive control and executive function (2021, background): “Moreover, the extent to which brain network connectivity changes in response to cognitive demands is associated with better performance on different CC tasks [154, 155], suggesting that task-based neural flexibility may facilitate Common CC through adaptive control [156].”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “There is robust evidence that static BOLD FC patterns ﬂexibly reconﬁgure across a range of cognitive and behavioral states (Cohen & D’Esposito, 2016; Cole, Bassett, Power, Braver, & Petersen, 2014; Cole et al., 2013; Gratton et al., 2018; Mattar, Cole, Thompson-Schill, & Bassett, 2015; Shirer,…”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Both effects in tandem are crucially involved in cognitive control (Dwyer et al., 2014) and executive functioning (Reineberg et al., 2015), and are differentially associated with performance on simple motor tasks versus complex working memory tasks (Cohen and D'Esposito, 2016).”
GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (2019, methodology): “Recent findings have shown that some ROIs are most related to cognition [5, 7], suggesting that some edges are more indicative of predicting cognitive performance.”
Neural interactions in the human frontal cortex dissociate reward and punishment learning (2024, methodology): “In order to study redundancy-and synergy-dominated interactions, we used formal definitions from Partial Information Decomposition (PID; Williams and Beer, 2010; Wibral et al., 2017; Lizier et al., 2018).”
Resting-state EEG connectome topology and social norm processing in healthy young adults (2026, unknown): “These findings do not imply that more integrated network configurations (i.e., shorter path lengths) more uniformly support cognitive performance (Langer et al. 2012; Cohen and D’Esposito 2016 ).”
Task‐specific topology of brain networks supporting working memory and inhibition (2024, background): “According to research, the brain functions as a small-world network with high clustering and short path lengths, which allow for both specialized (segregated) and holistic (integrated) processing (Cohen & D'Esposito, 2016; Deco et al., 2015).”