Task-Based Core-Periphery Organisation of Human Brain Dynamics

TL;DR

This study reveals that human brain dynamics during motor learning are organized into a core-periphery structure, with a stable sensorimotor-visual core and a flexible association-region periphery, predicting individual learning success.

Contribution

It introduces a novel method to analyze time-evolving brain networks, identifying a core-periphery organization linked to learning performance.

Findings

Core-periphery organization predicts learning success

Stable core regions show dense connectivity

Flexible periphery regions change connectivity frequently

Abstract

As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.