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Record W1973576997 · doi:10.1097/wnr.0b013e32832f4da3

Brain activation to favorite music in healthy controls and depressed patients

2009· article· en· W1973576997 on OpenAlexafffund
Elizabeth Osuch, Robyn Bluhm, Peter Williamson, Jean Théberge, Maria Densmore, Richard W. J. Neufeld

Bibliographic record

VenueNeuroreport · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsLawson Health Research InstituteWestern University
FundersSchulich School of Medicine and DentistrySchulich School of Medicine and Dentistry, Western University
KeywordsPsychologyNucleus accumbensGlobus pallidusPrefrontal cortexPleasureNeuroscienceVentral striatumAmygdalaStriatumDorsolateral prefrontal cortexAudiologyCognitive psychologyCognitionBasal gangliaMedicineCentral nervous systemDopamine

Abstract

fetched live from OpenAlex

Reward-processing neurocircuitry has been delineated using verbal or visual processing and/or decision-making tasks. We examined more basic processes of listening to enjoyable music in healthy and depressed patients. The paradigm was passive, individualized, and brief. Sixteen depressed and 15 control individuals provided favorite music and identified neutral music from selections provided. In the fMRI scanner, individuals heard their neutral and their favorite music for 3 min each. Favorite versus neutral music-listening contrasts showed greater activation in controls than depressed patients in medial orbital frontal cortex and nucleus accumbens/ventral striatum. Left medial prefrontal cortex activity was positively correlated with pleasure scores, whereas middle temporal cortex and globus pallidus were negatively correlated with pleasure. This paradigm activated neurocircuitry of reward processing and showed clinically meaningful alterations in depression.

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 score0.002
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.302
Teacher spread0.261 · 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

Citations102
Published2009
Admission routes2
Has abstractyes

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