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Record W4200222577 · doi:10.1093/geroni/igab046.498

Investigating the Cortical Correlates of Singing: Potential Neural Benefits of Choir for Persons With Dementia

2021· article· en· W4200222577 on OpenAlexaff
Nicholas Tamburri, Debra Sheets, Drew Halliday, André Smıth, Stuart MacDonald

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSingingPsychologyChoirFunctional near-infrared spectroscopyNeural correlates of consciousnessPsychological interventionDementiaCognitionDevelopmental psychologyCognitive psychologyAudiologyNeuroscienceMedicinePsychiatryPrefrontal cortex

Abstract

fetched live from OpenAlex

Abstract Through leveraging the known advantages of musical engagement and socialization, choir interventions are known to facilitate psychological and cognitive benefits for persons with dementia (PwD). Surprisingly, no research has explored whether social singing may also confer neurological advantages. In this study, we employed functional near infrared spectroscopy (fNIRS) to investigate the cortical correlates of both social and solo singing in PwD (n=13). Paired-sample t-tests were used to evaluate within-person differences in frontal cortical activation between the social vs solo singing. Results showed significant activation differences in three frontal channels, with social singing requiring comparatively less frontocortical activation. These findings indicate potential neural benefits of social singing – with less frontal activation being a proxy for greater reliance on intact proceduralized systems – and serve to highlight the utility of fNIRS in better understanding the neural correlates underlying the benefits of social singing interventions for PwD.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.053
GPT teacher head0.334
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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