Experience-dependent learning and myelin plasticity in individuals with stroke
Bibliographic record
Abstract
Abstract Background Injury to brain myelin disrupts motor performance and learning, however it is not clear if myelination is modulated by skilled motor practice or by recovery after stroke. Multi-component relaxation imaging can be used to measure water trapped between myelin bilayers which is expressed as myelin water fraction. The purpose of this study was to examine the effect of experience-dependent learning on myelin plasticity using multi-component relaxation imaging in individuals with stroke. Methods Thirty-two individuals with chronic stroke (>6 months) and twenty-seven healthy controls completed 4 weeks of skilled motor practice using a complex, gamified reaching task. Multi-component relaxation imaging-derived myelin water fraction was obtained before and after training. Seven brain regions associated with motor learning and sensorimotor function were investigated. Results All participants improved task-specific reaching movements after training. In individuals with stroke: 1) pre-training myelin water fraction was lower in motor brain regions but higher in the cingulum compared to controls, 2) pre-training myelin water fraction in motor and sensorimotor regions was positively associated with learning rate, and 3) myelin water fraction was increased in the ipsilesional (contralateral to the trained arm) superior longitudinal fasciculus following skilled motor practice. Conclusions Findings indicate that after stroke, myelin water fraction is related to measures of motor learning and modulated by 4 weeks of skilled motor practice with the paretic limb. Myelin water fraction can be enhanced in the chronic stage of stroke and may be an important target for upper-limb motor recovery.
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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 | 0.001 |
| 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) | 0.002 | 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".