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Record W7144450421

Gray Matter Volume Changes in the Apathetic Elderly

2016· dissertation· en· W7144450421 on OpenAlexfundno aff

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2016
Typedissertation
Languageen
FieldMedicine
TopicMedical Case Reports and Studies
Canadian institutionsnot available
FundersCouncil for Science, Technology and InnovationJapan Society for the Promotion of ScienceNanchang UniversityYork UniversityCabinet Office, Government of JapanSchool of Medicine, New York University
KeywordsVolume (thermodynamics)Gray (unit)Brain size
DOInot available

Abstract

fetched live from OpenAlex

This study is to test the hypothesis that apathy in healthy participants is closely related to the prefrontal-basal-ganglia circuit and associated structural changes.We selected 36 healthy aged participants with (n = 18) or without apathy (n = 18) from our database.Participants underwent structural MRI scanning, providing data for voxel-based morphometric analysis to explore gray matter changes associated with apathy.Compared to the non-apathy group, the apathy group showed reduced gray matter volume of the right putamen, whereas volumes of the bilateral inferior frontal gyri and left inferior occipital gyrus showed increase.When depression scores were included in a regression model as a covariate, apathetic participants showed decreased gray matter volume in the right precentral gyrus compared to the non-apathetic participants.These findings suggest that apathy is associated with the gray matter volume in the prefrontal-basal-ganglia network, and may have a neuroanatomical basis distinct from depression in healthy elderly.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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
Published2016
Admission routes1
Has abstractyes

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