Neural plasticity: The substratum of music-based interventions in neurorehabilitation
Bibliographic record
Abstract
BACKGROUND: The plastic nature of the human brain lends itself to experience and training-based structural changes leading to functional recovery. Music, with its multimodal activation of the brain, serves as a useful model for neurorehabilitation through neuroplastic changes in dysfunctional or impaired networks. Neurologic Music Therapy (NMT) contributes to the field of neurorehabilitation using this rationale. OBJECTIVE: The purpose of this article is to present a discourse on the concept of neuroplasticity and music-based neuroplasticity through the techniques of NMT in the domain of neurological rehabilitation. METHODS: The article draws on observations and findings made by researchers in the areas of neuroplasticity, music-based neuroplastic changes, NMT in neurological disorders and the implication of further research in this field. RESULTS: A commentary on previous research reveal that interventions based on the NMT paradigm have been successfully used to train neural networks using music-based tasks and paradigms which have been explained to have cross-modal effects on sensorimotor, language and cognitive and affective functions. CONCLUSIONS: Multimodal gains using music-based interventions highlight the brain plasticity inducing function of music. Individual differences do play a predictive role in neurological gains associated with such interventions. This area deserves further exploration and application-based studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".