The Use of Neurologic Music Therapy in Post-Stroke Aphasia Recovery: A Case Report on Linguistic Improvements and fMRI Correlates
Bibliographic record
Abstract
Background and Objectives: Post-stroke aphasia (PSA) severely limits communication and quality of life. This case study explores the impact of an integrated Neurologic Music Therapy (NMT) approach, combining Melodic Intonation Therapy (MIT) and Therapeutic Singing (TS), on language recovery and brain reorganization in a 59-year-old woman with non-fluent motor aphasia following an ischemic stroke. Methods: Over 8 weeks, the patient underwent 24 sessions of MIT alongside standard speech therapy. Language abilities were assessed using the Western Aphasia Battery-Revised, while fMRI scans captured neurophysiological changes pre- and post-intervention. Results: The results showed significant language improvements: spontaneous speech increased by 68.9%, auditory comprehension by 30.8%, and naming by 83.3%. The Aphasia Quotient rose from 39.3 to 61.4, marking a shift from severe to moderate aphasia. Neuroimaging revealed heightened activation in both hemispheres, especially in the superior frontal and parietal regions, supplementary motor area, and superior temporal gyrus. Increased engagement of the limbic system, particularly the paracingulate gyrus, pointed to emotional involvement and widespread cortical reorganization. Conclusions: These findings highlight the effectiveness of integrating MIT and TS with emotionally meaningful music, supporting language recovery and neural plasticity in PSA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".