Impact of 5‐Hz <scp>rTMS</scp> over the primary sensory cortex is related to white matter volume in individuals with chronic stroke
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique that may facilitate mechanisms of motor learning. In a recent single-blind, pseudo-randomized study, we showed that 5-Hz rTMS over ipsilesional primary somatosensory cortex followed by practice of a skilled motor task enhanced motor learning compared with sham rTMS + practice in individuals with chronic stroke. However, the beneficial effect of stimulation was inconsistent. The current study examined how differences in sensorimotor cortex morphology might predict rTMS-related improvements in motor learning in these individuals. High-resolution T1-weighted magnetic resonance images were acquired and processed in FreeSurfer using a newly developed automated, whole brain parcellation technique. Gray matter and white matter volumes of the ipsilesional primary somatosensory and motor cortices were extracted. A significant positive association was observed between the volume of white matter in the primary somatosensory cortex and motor learning-related change, exclusively in the group that received active 5-Hz rTMS. A regression model with age, gray matter and white matter volumes as predictors was significant for predicting motor learning-related change in individuals who received active TMS. White matter volume predicted the greatest amount of variance (47.6%). The same model was non-significant when volumes of the primary motor cortex were considered. We conclude that white matter volume in the cortex underlying the TMS coil may be a novel predictor for behavioral response to 5-Hz rTMS over the ipsilesional primary somatosensory followed by motor practice.
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 | 0.001 |
| 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) | 0.001 | 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".