Dynamic reconfiguration of human brain networks during learning

TL;DR

This study investigates how the modular organization of human brain networks dynamically changes during learning, revealing that flexibility in modular allegiance predicts future learning success and providing a general framework for analyzing evolving networks.

Contribution

The paper introduces a new framework for identifying modular architectures in dynamic systems and demonstrates its application to understanding brain network reconfiguration during learning.

Findings

Flexibility in brain modules predicts future learning performance.

Dynamic modular changes occur across multiple temporal scales.

A general statistical method for analyzing evolving network modularity.

Abstract

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.