Motor Skill Learning Induces Changes in White Matter Microstructure and Myelination
Bibliographic record
Abstract
Learning a novel motor skill is associated with well characterized structural and functional plasticity in the rodent motor cortex. Furthermore, neuroimaging studies of visuomotor learning in humans have suggested that structural plasticity can occur in white matter (WM), but the biological basis for such changes is unclear. We assessed the influence of motor skill learning on WM structure within sensorimotor cortex using both diffusion MRI fractional anisotropy (FA) and quantitative immunohistochemistry. Seventy-two adult (male) rats were randomly assigned to one of three conditions (skilled reaching, unskilled reaching, and caged control). After 11 d of training, postmortem diffusion MRI revealed significantly higher FA in the skilled reaching group compared with the control groups, specifically in the WM subjacent to the sensorimotor cortex contralateral to the trained limb. In addition, within the skilled reaching group, FA across widespread regions of WM in the contralateral hemisphere correlated significantly with learning rate. Immunohistological analysis conducted on a subset of 24 animals (eight per group) revealed significantly increased myelin staining in the WM underlying motor cortex in the hemisphere contralateral (but not ipsilateral) to the trained limb for the skilled learning group versus the control groups. Within the trained hemisphere (but not the untrained hemisphere), myelin staining density correlated significantly with learning rate. Our results suggest that learning a novel motor skill induces structural change in task-relevant WM pathways and that these changes may in part reflect learning-related increases in myelination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".