Structural Pathways Supporting Swift Acquisition of New Visuo-Motor Skills

TL;DR

This study shows that individual differences in brain structural connectivity, especially between visual and motor regions, can predict how quickly people learn new visuo-motor skills over several weeks.

Contribution

It demonstrates that white matter pathways and network connectivity measures can forecast individual learning rates in visuo-motor skill acquisition.

Findings

Increased visual region connectivity correlates with faster learning.

Multi-edge paths between motor and visual modules predict learning rate.

Diffusion imaging can forecast individual learning differences.

Abstract

Human skill learning requires fine-scale coordination of distributed networks of brain regions that are directly linked to one another by white matter tracts to allow for effective information transmission. Yet how individual differences in these anatomical pathways may impact individual differences in learning remains far from understood. Here, we test the hypothesis that individual differences in the organization of structural networks supporting task performance predict individual differences in the rate at which humans learn a visuo-motor skill. Over the course of 6 weeks, twenty-two healthy adult subjects practiced a discrete sequence production task, where they learned a sequence of finger movements based on discrete visual cues. We collected structural imaging data during four MRI scanning sessions spaced approximately two weeks apart, and using deterministic tractography, structural networks were generated for each participant to identify streamlines that connect cortical and sub-cortical brain regions. We observed that increased white matter connectivity linking early visual (but not motor) regions was associated with a faster learning rate. Moreover, we observed that the strength of multi-edge paths between motor and visual modules was also correlated with learning rate, supporting the role of polysynaptic connections in successful skill acquisition. Our results demonstrate that the combination of diffusion imaging and tractography-based connectivity can be used to predict future individual differences in learning capacity, particularly when combined with methods from network science and graph theory.