Dynamic cooperation and competition between brain systems during cognitive control

Cocchi, Luca; Zalesky, Andrew; Fornito, Alex; Mattingley, Jason B.

doi:10.1016/j.tics.2013.08.006

Cited by 390 publications

(355 citation statements)

References 82 publications

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“…Although most studies examining connectivity during motorrelated tasks focus on within-network organization (Biswal et al, 1995;Jiang et al, 2004;Rissman et al, 2004;Sun et al, 2004Sun et al, , 2007Zhuang et al, 2005;Bardouille and Boe, 2012), many studies probing working memory focus on internetwork integration. Our findings are consistent with previous studies demonstrating increased integration during tasks presumed to tap working memory with increasing load or compared to rest (Kitzbichler et al, 2011;Gordon et al, 2012;Cohen et al, 2014;Liang et al, 2016) and that increased integration is related to increased behavioral performance (Bassett et al, 2009;Cohen et al, 2014;Stanley et al, 2014;Braun et al, 2015). We extend this literature by using several measures of segregation and integration to provide convergent evidence for increased integration during working memory.…”

Section: Discussionsupporting

confidence: 92%

“…It is additionally theorized that a highly interconnected group of regions, the "rich club," is critical for integration across distinct networks (van den Heuvel et al, 2012). Further, patterns of integration alter based on current cognitive demands, with distinct profiles of cooperative and competitive connections underlying different aspects of cognition (Cocchi et al, 2013). Extant literature makes it clear that it is critical to understand both network segregation and network integration and how they relate to different aspects of cognition.…”

Section: Introductionmentioning

confidence: 99%

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

2016

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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.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

2016

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“…This finding indicates that adolescent control performance was specifically associated with context-dependent alterations of network dynamics, rather than an intrinsic property of spontaneous brain activity as putatively captured by resting-state fMRI (but see Bullmore, 2010 andHarrison et al, 2011 for caveats). It also highlights the importance of distinguishing between resting-state and task-related functional connectivity measures for understanding individual differences in cognition (Fornito et al, 2012; for review, see Cocchi et al, 2013). One notable result was the heavy involvement of visual regions in both the activation and functional connectivity correlates of control performance.…”

Section: Discussionmentioning

confidence: 99%

Large-Scale Brain Network Dynamics Supporting Adolescent Cognitive Control

et al. 2014

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Adolescence is a time when the ability to engage cognitive control is linked to crucial life outcomes. Despite a historical focus on prefrontal cortex functioning, recent evidence suggests that differences between individuals may relate to interactions between distributed brain regions that collectively form a cognitive control network (CCN). Other research points to a spatially distinct and functionally antagonistic system-the default-mode network (DMN)-which typically deactivates during performance of control tasks. This literature implies that individual differences in cognitive control are determined either by activation or functional connectivity of CCN regions, deactivation or functional connectivity of DMN regions, or some combination of both. We tested between these possibilities using a multilevel fMRI characterization of CCN and DMN dynamics, measured during performance of a cognitive control task and during a task-free resting state, in 73 human adolescents. Better cognitive control performance was associated with (1) reduced activation of CCN regions, but not deactivation of the DMN; (2) variations in task-related, but not resting-state, functional connectivity within a distributed network involving both the CCN and DMN; (3) functional segregation of core elements of these two systems; and (4) task-dependent functional integration of a set of peripheral nodes into either one network or the other in response to prevailing stimulus conditions. These results indicate that individual differences in adolescent cognitive control are not solely attributable to the functioning of any single region or network, but are instead dependent on a dynamic and context-dependent interplay between the CCN and DMN.

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“…Nevertheless, several new methods are now available (for a short overview see Cocchi et al (2013)). The importance of studying context-driven changes in the functional interplay between neural assemblies is highlighted by the fact that pathological network activity may only emerge when patients are engaged in specific tasks (e.g., Cocchi et al, 2012;McLaren et al, 2014).…”

Section: Insert Figure 1 Near Herementioning

confidence: 99%

Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation

Sale¹,

Mattingley²,

Zalesky³

et al. 2015

Neuroscience & Biobehavioral Reviews

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Dynamic cooperation and competition between brain systems during cognitive control

Cited by 390 publications

References 82 publications

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

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

Large-Scale Brain Network Dynamics Supporting Adolescent Cognitive Control

Imaging human brain networks to improve the clinical efficacy of non-invasive brain stimulation

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