Coupling neurologic music therapy with immersive virtual reality to improve executive functions in individuals with Parkinson’s disease: A Quasi-Randomized Clinical Trial
Bibliographic record
Abstract
Introduction: Parkinson's disease (PD) is one the most common neurodegenerative movement disorder, leading to motor and non-motor symptoms, including deficits in executive functions (EF), memory, visuospatial abilities, and psychomotor speed. Current treatments are primarily symptomatic, involving pharmacological, surgical strategies. Neurologic Music Therapy (NMT) has gained recognition for its effectiveness in neurorehabilitation of PD patients and improving motor and cognitive domains, such as EF. This study combines NMT with the virtual reality (VR) platform Computer-Assisted Rehabilitation Environment (CAREN), offering customizable environments for rhythmic cue practice to provide an innovative approach to Parkinson's rehabilitation. Methods: In our single-blind quasi-randomized controlled trial, forty patients were assigned to either an experimental group (EG = 20) or a control group (CG = 20). Both groups underwent two months of training with CAREN scenarios (three times a week for 24 sessions). The experimental group additionally received NMT applied to the selected scenarios. Participants were evaluated by a neuropsychologist at baseline and immediately after training. Results: Intra-group analysis showed significant improvements in the EG for MOCA (p = 0.007), FAB (p = 0.008), Stroop Error (p = 0.003), Stroop Time (p < 0.001), and Visual Search (p < 0.001). The CG showed a significant difference only in Stroop Error (p = 0.02). Conclusions: This pilot study is the first to combine NMT with CAREN in PD patients. Our findings suggest that NMT, within an immersive VR environment, effectively improves cognitive and EF in PD. Music structured within NMT techniques, coupled with advanced audio-visual feedback from VR, offers an innovative and potentially more effective approach for managing cognitive and executive deficits associated with PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".