The impact of music-based intervention on cognitive function and brain functional magnetic resonance imaging in people with mild Alzheimer's disease
Bibliographic record
Abstract
BackgroundWith the accelerating global aging population, the incidence of Alzheimer's disease (AD) continues to rise, while current pharmacological treatments remain limited in efficacy. Music intervention, as a safe and feasible non-pharmacological approach, has gained increasing clinical attention, though its mechanisms of action remain unclear.ObjectiveThis study aims to evaluate the effects of music intervention on cognitive function and brain network connectivity in people with mild AD, and to elucidate its neural mechanisms and provide evidence for clinical practice.MethodsA total number of 50 AD patients with mild dementia participated in the study. Participants were randomized to music-based intervention group (music-based intervention, 20 min, 3 times/week for 6 months) or control group (standard care). Assessments included Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Neuropsychiatric Inventory (NPI), Word Fluency Test (WFT), World Health Organization-University of California, the Los Angeles Auditory Verbal Learning Test (WHO-UCLA-AVLT), and functional magnetic resonance imaging (fMRI). Data were analyzed using SPSS 20.0.Results47 participants completed the study. The music-based intervention group showed significant improvements in MoCA, GDS, NPI, WFT, and WHO-UCLA-AVLT scores (p < 0.05), with no change in MMSE. fMRI revealed enhanced frontal-temporal connectivity and increased angular gyrus activity.ConclusionsMusic-based intervention improves cognitive and neuropsychiatric outcomes in people with mild AD, likely through enhanced brain connectivity. This approach is feasible, and it supports the optimization of music-based intervention in clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".