Neurophysiological Changes Induced by Music-Supported Therapy for Recovering Upper Extremity Function after Stroke: A Case Series
Bibliographic record
Abstract
Music-supported therapy (MST) follows the best practice principles of stroke rehabilitation and has been proven to instigate meaningful enhancements in motor recovery post-stroke. The existing literature has established that the efficacy and specificity of MST relies on the reinforcement of auditory-motor functional connectivity in related brain networks. However, to date, no study has attempted to evaluate the underlying cortical network nodes that are key to the efficacy of MST post-stroke. In this case series, we evaluated changes in connectivity within the auditory-motor network and changes in upper extremity function following a 3-week intensive piano training in two stroke survivors presenting different levels of motor impairment. Connectivity was assessed pre- and post-training in the α- and the β-bands within the auditory-motor network using magnetoencephalography while participants were passively listening to a standardized melody. Changes in manual dexterity, grip strength, movement coordination, and use of the upper extremity were also documented in both stroke survivors. After training, an increase in the clinical measures was accompanied by enhancements in connectivity between the auditory and motor network nodes for both the α- and the β-bands, especially in the affected hemisphere. These neurophysiological changes associated with the positive effects of post-stroke MST on motor outcomes delineate a path for a larger scale clinical trial.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".