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

Abstract

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.

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

“…It remains a fundamental challenge to understand what other network properties help integrate disparate data streams in order to meet complex task demands (Cohen and D’Esposito, 2016; Petersen and Sporns, 2015). As discussed throughout this review, the themes of segregation and integration are core concepts in understanding the brain’s maturational processes.…”

Characterisation of Functional Brain Networks underlying Cognitive Reasoning and Intelligence

“…Our finding that increased demands on cognitive reasoning are paralleled by a reduction in network modularity and increased efficiency has now been reported in several different task contexts (Bola and Sabel, 2015;Cohen and D'Esposito, 2016;Godwin et al, 2015;Kitzbichler et al, 2011;Shine et al, 2016;Vatansever et al, 2015;Westphal et al, 2017;Yue et al, 2017). Using the network-based statistic we found that these global changes were supported by increases and decreases in functional connectivity across multiple large-scale networks.…”

“…There is now considerable evidence that more complex cognitive tasks rely on more spatially distributed processing than simple tasks (Cohen and D'Esposito, 2016;Hearne et al, 2017;Yue et al, 2017). …”

“…The parallel effect of decreased modularity and increased network efficiency is not specific to relational reasoning, and has now been reported in several different cognitive domains, including selective attention, working memory and visual discrimination (Bola and Sabel, 2015;Cohen and D'Esposito, 2016;Godwin et al, 2015;Kitzbichler et al, 2011;Shine et al, 2016;Vatansever et al, 2015;Westphal et al, 2017;Yue et al, 2017). It has been theorized that such reconfigurations arise to service increased information transfer (Achard and Bullmore, 2007;Shine et al, 2016;Shine and Poldrack, 2017).…”

“…Higher cognitive functions are supported by the adaptive reconfiguration of large-scale functional networks (Bassett et al, 2011;Braun et al, 2015;Cohen and D'Esposito, 2016;Cole et al, 2013;Yue et al, 2017). Previous empirical and theoretical work suggests that a multitude of complex tasks are related to activity and communication within and between select fronto-parietal, cingulo-opercular, and default-mode networks (Bolt et al, 2017;Cocchi et al, 2014a;Crittenden et al, 2016;Hearne et al, 2015;Knowlton et al, 2012).…”