Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning

• DOI: 10.1523/jneurosci.0485-17.2017
• Publication Date: 2017-08-01T00:50:16Z
• Authors: Luke J. Hearne; Luca Cocchi; Andrew Zalesky; Jason B. Mattingley
• Keywords: Control reconfiguration; Modularity

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's flexibly reconfigure interactions between spatially distributed networks. Recent work, however, 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 examine how flexible, task-specific associated increasing demands integrated within stable brain topology. Human participants (21 males 28 females) underwent an initial scan, followed by task involving different levels complexity, second scan. The required deduce identity missing element in 4 × matrix, item difficulty was scaled parametrically as determined relational complexity theory. Analyses revealed external engagement characterized significant change modules. Specifically, null-task demand conditions were more segregated brain-network topology, whereas increases resulted merging Further increments did not established modular architecture, but affected selective patterns connectivity frontoparietal, subcortical, cingulo-opercular, default-mode Larger efficiency newly modules accuracy. results shed light on architectures underlie engagement, highlight changes supporting complexity.SIGNIFICANCE STATEMENT Humans have clear limits their ability solve complex problems. It such limitations moment-to-moment less adaptive relate stable, behavioral performance. We found increased rely cortical emerged addition general, task-induced architecture reverted both immediately before after task. These findings reveal flexibility human integral achieving successful performance across demand.