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Record W7037230174

Differences in Cortical Thickness between Cognitively Impaired Persons with and without Apathy May Reflect Discrete Mechanisms of Neuropathophysiology

2021· dissertation· W7037230174 on OpenAlexfundno aff

Bibliographic record

VenueTSpace · 2021
Typedissertation
Language
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNorthern California Institute for Research and EducationBioClinicaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeUniversity of Southern CaliforniaBristol-Myers SquibbEli Lilly and CompanyBiogenEisaiAlzheimer's Association
KeywordsApathyFrontotemporal dementiaDementiaAnterior cingulate cortexOrbitofrontal cortexNeuroimagingCingulate cortexAtrophyCortex (anatomy)
DOInot available

Abstract

fetched live from OpenAlex

Apathy increases the risk of dementia and is associated with worse outcomes. Deficits in frontostriatal circuits and frontotemporal association areas are associated with apathy in dementia. However, the specific brain regions mediating apathy remain unclear given concomitant neurodegenerative processes. Cognitively impaired (CI) participants with apathy and without apathy from the Alzheimer’s Disease Neuroimaging Initiative matched by demographic, genetic, and cognitive markers were investigated. Differences in region-wise cortical thicknesses were examined by stratified mixed-effects analyses. The right medial orbitofrontal cortex (mOFC) and left rostral anterior cingulate cortex (rACC) were thinner and the left middle temporal cortex (MTC) was thicker in CI participants with apathy compared to matched CI participants without apathy. Supplementary analyses with cognitively normal participants showed that apathy among CI participants was associated with thinner right mOFC and left rACC. Meanwhile, CI participants with apathy had spared atrophy in the left MTC relative to matched CI participants without apathy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.284
Teacher spread0.242 · 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 teacher head, not a consensus.

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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