INVESTIGATING THE NEURAL CORRELATES OF SOCIAL AND INDIVIDUAL SINGING IN PERSONS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Music interventions for persons with Alzheimer’s Disease and related dementias (PwADRD) have documented psychological benefits; however, the neurological correlates underlying these advantages remain less certain. Using functional near infrared spectroscopy (fNIRS), the present study i) investigated within-person patterns of cortical oxygenation during choral vs. individual singing; ii) explored how singing context (choral vs. individual) modulated patterns of functional connectivity (FC) within and across frontal and parietal cortices; and iii) leveraged these functional activations as predictors of cognitive status (degree of impairment) in a series of discriminant function analyses (DFA). Participants were 13 PwADRD who volunteered from a larger, ongoing social-cognitive choir intervention. fNIRS data were collected using a TechEn Cw6 system during both choral and individual singing conditions. Paired sample t-tests evaluated differences in activation patterns between singing conditions, with DFA used to determine whether these activations and neuropsychological function were predictive of between-person differences in cognitive impairment. Significant differences in cerebral oxygenation were identified in the right anterior PFC, with individual singing associated with significantly greater cortical oxygenation relative to group singing. Although not significant, individual singing was also associated with bilateral increases in cortical oxygenation across the majority of fNIRS channels, as well as increased FC, relative to group singing. The DFA analyses were not significant. This novel study is the first to examine differences in music-cognition correlates across environmental contexts for PwADRD. Patterns of functional activation suggest that choral singing in particular may represent an optimal lifestyle activity, placing comparatively fewer demands on cognitive function.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".