Are Changes in Corticomotor Excitability Associated with Improved Arm Functional Performance Following a Tailored Strength Training Intervention in Chronic Stroke Survivors?
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: We showed that a tailored strengthening intervention based on the size of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) in the affected hemisphere resulted in an improved affected arm function, regardless of stroke severity. Also, adding anodal transcranial direct stimulation (atDCS) during training did not alter the results as participants receiving real or sham stimulation showed similar gains. The goal of this study was to report on the changes in basic measures of corticomotor excitability in response to the intervention and to determine whether these changes were influenced by tDCS and correlated with those measured in arm function. METHODS: The TMS measures consisted of the resting motor threshold (rMT), MEP amplitude at rest, and the silent period (SP) duration. Clinical outcomes included the Box and Block test (BBT) and grip strength (GS). RESULTS: > 0.06). CONCLUSIONS: As observed for clinical measures, atDCS did not influence corticomotor excitability. The absence of an increase in the excitability of the affected hemisphere and important associations between changes in corticomotor excitability and clinical gains suggest that factors other than brain plasticity could mediate gains in arm function. Further investigations are required regarding the role of tDCS in stroke rehabilitation.
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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".