Brain Network Adaptability Across Task States

Abstract

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adapt- ability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.

22 pages, 9 figures, 1 table

References in corpus (11)

• Link communities reveal multiscale complexity in networks
• Dynamic reconfiguration of human brain networks during learning
• Weak pairwise correlations imply strongly correlated network states in a neural population
• Line Graphs, Link Partitions and Overlapping Communities
• Robust Detection of Dynamic Community Structure in Networks
• Memory in network flows and its effects on spreading dynamics and community detection
• Task-Based Core-Periphery Organisation of Human Brain Dynamics
• Dynamic Network Centrality Summarizes Learning in the Human Brain
• Cross-Linked Structure of Network Evolution
• Learning-Induced Autonomy of Sensorimotor Systems
• Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series

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