  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.
