Therapeutic benefits of music for three types of neurological disorders: an fNIRS study protocol for controlled trial in Dalian, China, Part 1 – Parkinson’s Disease
Bibliographic record
Abstract
Abstract Background: Music therapy improves neuronal activity and connectivity of healthy persons and patients with clinical symptoms of neurological diseases like Parkinson’s Disease, Alzheimer’s Disease, and Major Depression. Despite the plethora of publications that have reported the positive effects of music interventions, little is known about how music improves neuronal activity and connectivity in afflicted patients. In this three-part series of studies, each of the three neurological disorders – (1) Parkinson’s Disease, (2) Alzheimer’s Disease, and (3) Major Depression – will be treated individually in detail. Methods: For patients suffering from Parkinson’s Disease (PD), we propose a daily 25-minute music-based synchronous finger tapping (SFT) intervention for 8-weeks. Participants will be each split into two groups: an intervention group and a control arm. Assessment of finger tapping performances, the Unified Parkinson’s Disease Rating Scale (UPDRS), an n-back test, the Montreal Cognitive Assessment (MoCA), as well as oxygenated- (HbO 2 ), deoxygenated- hemoglobin (HbR), and total hemoglobin activation collected by functional near-infrared spectroscopy (fNIRS) will be measured at baseline, week 4 (during), week 8 (post), and week 12 (retention) of the study. Data collected from the two PD groups will be compared to baseline performances from healthy controls.Discussion: The trial study investigates the cortical neuronal activity and therapeutic effects associated with an auditory external cue used to induce automatic and implicit synchronous finger tapping in patients diagnosed with PD. The extent to which the intervention is effective may be dependent on the severity of the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".