Lesion location changes the association between brain excitability and motor skill acquisition post-stroke
Bibliographic record
Abstract
Abstract Background The capacity to reacquire motor skills lost after a stroke is crucial to promote upper-limb motor recovery but the impact of lesion location on motor skill acquisition and the underlying neurophysiological mechanisms remain uncertain. Methods We used transcranial magnetic stimulation to investigate associations between excitatory and inhibitory cortico-spinal excitability measures and the capacity to acquire a novel motor skill with the most affected hand in 103 individuals with cortical (n=34) or subcortical (n=69) lesions. Results Both groups showed similar motor skill acquisition, but subcortical lesions exhibited more impairment in the most affected hand and lower excitability in the ipsilesional hemisphere. In cortical lesions, motor skill acquisition was associated with lower motor thresholds (β=-0.25, 95% CI [−0.47,-0.03]; p=0.024) and higher intracortical inhibition (β=-3.93, 95% CI [−6.89,-0.98]; p=0.011) in the ipsilesional hemisphere. In contrast, in subcortical lesions motor skill acquisition was associated with smaller motor evoked potentials (β=-4.46, 95% CI [−8.54,-0.38]; p=0.033), less intracortical inhibition (β=3.45, 95% CI [0.34,6.56]; p=0.030) and higher facilitation (β=1.34,95% CI [0.15,2.54]; p= 0.028) ipsilesionally. Sensitivity analyses revealed that associations with intracortical inhibition and facilitation in the subcortical group were driven by lesions affecting the corticospinal tract. No associations were found in the contralesional hemisphere. Conclusions Reinforcing the existence of lesion-specific neurophysiological patterns, individuals with cortical and subcortical lesions show divergent associations between cortico-spinal excitability and motor skill acquisition. The use of cortico-spinal excitability as a biomarker to predict upper-limb recovery post-stroke or guide motor recovery interventions such as non-invasive brain stimulation should consider lesion location.
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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 | 0.001 | 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".