Brain Network Adaptability Across Task States

TL;DR

This study uses hypergraph-based network analysis to reveal common dynamic processes underlying brain state transitions across different cognitive tasks, highlighting the brain's adaptable functional integration.

Contribution

It introduces hypergraph formalism to analyze brain network reconfigurations, uncovering shared mechanisms across diverse cognitive states.

Findings

Brain networks show coherent fluctuations in functional interactions during tasks.

Hypergraphs effectively characterize brain dynamics across states.

Common processes drive the integration of cognitive systems during task transitions.

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.