Reconfiguration of brain network architectures between resting-state and complexity-dependent cognitive reasoning

Hearne, Luke J., Cocchi, Luca, Zalesky, Andrew and Mattingley, Jason B. (2017) Reconfiguration of brain network architectures between resting-state and complexity-dependent cognitive reasoning. Journal of Neuroscience, 37 35: 8399-8411. doi:10.1523/JNEUROSCI.0485-17.2017

Author Hearne, Luke J.
Cocchi, Luca
Zalesky, Andrew
Mattingley, Jason B.
Title Reconfiguration of brain network architectures between resting-state and complexity-dependent cognitive reasoning
Journal name Journal of Neuroscience   Check publisher's open access policy
ISSN 1529-2401
Publication date 2017-08-30
Sub-type Article (original research)
DOI 10.1523/JNEUROSCI.0485-17.2017
Open Access Status Not yet assessed
Volume 37
Issue 35
Start page 8399
End page 8411
Total pages 13
Place of publication Washington, DC, United States
Publisher Society for Neuroscience
Language eng
Abstract Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity.SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting-state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand.
Keyword Complexity
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
School of Psychology Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
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