Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks

By Manfred G. Kitzbichler, Richard N. Henson, Marie L. Smith, Pradeep J. Nathan, E. Bullmore. Journal of Neuroscience, 2011

386 citations recorded by the source corpus.

Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16–32 Hz) and γ-band (32–63 Hz) networks, compared with both lower (α-band; 8–16 Hz) and higher (high γ-band; 63–125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5054139b40eb3036b7c04ef7dbc401782527dcfb

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In Cognitive Neuroscience

Citations in the corpus

Cognitive Network Neuroscience (2015, background): “…for a network perspective was found within this organization: increases in brain-wide small-world organization support increased working memory capacity (Stevens et al., 2012), and increased network efficiency results in better memory performance (Kitzbichler et al., 2011; Bassett et al., 2009).”
Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation (2017, neutral): “…performance of a cognitively demanding working memory task was associated with greater topological efficiency (shorter path length) of MEG networks (Kitzbichler et al., 2011); and that the rich club of interconnected hubs in a meta-analysis of task-related fMRI data was co-activated by executive…”
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2015, positive): “Irrespective of the precise mechanism driving global fluctuations, our results suggest that system-wide alterations in network topology facilitate more effective behavioral performance, a hypothesis that has already garnered support from studies both in network dynamics (Kitzbichler et al., 2011) an”
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule (2013, neutral): “Experiments have shown that during memory tasks requiring large cognitive efforts, magnetoencephalography networks show high efficiency while including a large proportion of long-distance edges (Kitzbichler et al., 2011).”
Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain (2013, background): “Engaging greater cognitive effort in task performance has been shown to be associated with a more efficient network topology (34); our findings of increased blood supply at high task loads suggest that extra metabolism would be needed to support the reorganized functional connectivity architecture.”
Cognitive relevance of the community structure of the human brain functional coactivation network (2013, background): “This is also compatible with an economical interpretation: integrative network features, such as long-range connections, incur a premium in terms of connection cost, emerging only under conditions where they can add value behaviorally in response to a cognitively challenging environment (10, 40).”
Cortical High-Density Counterstream Architectures (2013, background): “These changes in local efficiency indicate flexibility of network modularity and long-distance functional interactions—for example, in response to changes in cognitive load (13, 87).”
Network attributes for segregation and integration in the human brain. (2013, background): “Numerous studies have documented reconfigurations of functional connectivity with changing conditions of sensory input, task or cognitive load [69] and in the course of learning [70].”
On the use of correlation as a measure of network connectivity (2012, background): “Densities as low as 1-5% (e.g. Achard et al., 2006; Kitzbichler et al., 2011) and as high as 50% (e.”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “For instance, intensifying cognitive loads during a working memory task is associated with increased integration between diﬀerent modules of the brain network (Kitzbichler et al., 2011; Braun et al., 2015; Liang et al., 2016).”
Time-resolved resting-state brain networks (2014, background): “This “transitional”module (29) may therefore be more flexible than the others, possibly supporting transient psychological states (19) and complementing frontoparietal regions in supporting adaptive, context-dependent control (28, 54).”
The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, background): “…functional MRI data) is positively correlated with better cognitive performance (van den Heuvel et al., 2009; Giessing et al., 2013); and that cognitively more demanding tasks lead to the appearance of long-range integrative connections in task-related MEG data (Kitzbichler et al., 2011).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “…and functional networks has been linked to higher IQ, attentional capacity, and working memory in healthy young adults (Cohen and D'Esposito, 2016; Kitzbichler et al., 2011; Li et al., 2009; Stanley et al., 2015; van den Heuvel et al., 2009), supporting the notion that it confers advantages for…”
A Brain-Wide Study of Age-Related Changes in Functional Connectivity. (2015, background): “, high local efficiency) along with sparse long range connections which are more costly but greatly increase the speed of information transfer (Buzsáki et al. 2004; Kitzbichler et al. 2011; Bullmore and Sporns 2012).”
Simple models of human brain functional networks (2012, background): “…integrated processing, and several studies have shown that greater ef ﬁ ciency of network topology is associated with higher intelligence quotient (29, 30), greater accuracy of working memory task performance (31), or successful performance of more dif ﬁ cult versions of a working memory task (32).”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “Greater cognitive effort was found to be associated with MEG functional networks that were less clustered and less modular, owing to the emergence of functional connections linking distributed brain regions 58 .”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “We note however, that graph theory can be used to characterize data acquired using other modalities, such as EEG/MEG (Stam, 2004a; Bassett, et al., 2006b; Stam, et al., 2007b; Rubinov, Knock, et al., 2009; Brookes, et al., 2011; Hipp, et al., 2011; Kitzbichler, et al., 2011; Zalesky, Cocchi, et al.,”