P.050 Mapping motor pathways with transcranial magnetic stimulation to predict functional outcomes in non-human primate models of chronic stroke
Bibliographic record
Abstract
Background: Ischemic stroke often results in long-term motor impairments due to disrupted corticospinal pathways. Transcranial magnetic stimulation (TMS) motor mapping is a non-invasive technique used to assess corticospinal integrity by measuring motor evoked potentials (MEPs). This study investigates whether MEP amplitudes can predict impairment severity and functional performance in chronic stroke. Methods: Four non-human primates (NHPs) with chronic stroke (> six months) following transient right middle cerebral artery occlusion underwent TMS motor mapping using neuronavigation under ketamine anesthesia. Single pulses of TMS (50-70% of maximum stimulator output) were applied to the affected and contralesional primary motor cortices to elicit MEPs and assess cortical excitability. Intramuscular electromyography recorded muscle responses from the biceps, extensor digitorum longus, and abductor pollicis brevis. Neurological dysfunction was evaluated daily for three weeks using the NHP Stroke Scale, NHP Upper Extremity Motor Dysfunction Scale, and the primate Rankin Scale. Results: MEPs were present in NHP1, NHP3, and NHP4 but absent in NHP2. Stronger MEPs correlated with lower impairment severity and better functional performance, while NHP2 exhibited higher impairment and poorer performance. Conclusions: MEP presence and strength can serve as biomarkers of motor recovery potential, highlighting their role in assessing corticospinal integrity and functional outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".