# Brain State Flexibility Accompanies Motor-Skill Acquisition

## TL;DR

This study introduces a graph theory-based method to measure and analyze brain state dynamics during motor skill learning, revealing increased flexibility and state switching as learning progresses.

## Contribution

The paper presents a novel graph-based approach to identify and track brain states over time, linking state flexibility to learning stages and individual differences.

## Key findings

- Brain switches between sensorimotor and frontal-subcortical states during learning.
- Increased state switching correlates with faster learning.
- Brain dynamics become more complex as learning advances.

## Abstract

Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging -- and to assess their dynamics during learning -- remain limited. Here, we describe an approach based on a novel application of graph theory in which points in time are represented by network nodes, and similarities in brain states between two different time points are represented as network edges. We use a graph-based clustering technique to identify clusters of time points representing canonical brain states, and to assess the manner in which the brain moves from one state to another as learning progresses. We observe the presence of two primary states characterized by either high activation in sensorimotor cortex or high activation in a frontal-subcortical system. Flexible switching among these primary states and other less common states becomes more frequent as learning progresses, and is inversely correlated with individual differences in learning rate. These results are consistent with the notion that the development of automaticity is associated with a greater freedom to use cognitive resources for other processes. Taken together, our work offers new insights into the constrained, low dimensional nature of brain dynamics characteristic of early learning, which give way to less constrained, high-dimensional dynamics in later learning.