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Record W4392916101 · doi:10.1002/gps.6080

Sex‐specific neuropsychological correlates of apathy and depression across neurodegenerative disorders

2024· article· en· W4392916101 on OpenAlexafffund
Daniel Kapustin, Shankar Tumati, Melissa H. Wong, Nathan Herrmann, Roger A. Dixon, Dallas Seitz, Mark Rapoport, Krista L. Lanctôt

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of CalgaryOntario Brain InstituteWomen and Children’s Health Research InstituteUniversity of AlbertaHotchkiss Brain InstituteSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
FundersCanadian Institutes of Health ResearchAlberta InnovatesConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsApathyDepression (economics)PsychologyNeuropsychologyVerbal fluency testDementiaPsychiatryCohortNeuropsychological testVerbal memoryClinical psychologyExecutive functionsExecutive dysfunctionCognitionDiseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Apathy and depression are common neuropsychiatric symptoms across neurodegenerative disorders and are associated with impairment in several cognitive domains, yet little is known about the influence of sex on these relationships. Objectives We examined the relationship between these symptoms with neuropsychological performance across a combined cohort with mild or major neurodegenerative disorders, then evaluated the impact of sex. Design, Setting and Participants We conducted a cohort analysis of participants in the COMPASS‐ND study with mild cognitive impairment (MCI), vascular MCI, Alzheimer's disease, mixed dementia, Parkinson's disease, frontotemporal dementia, and cognitively unimpaired (CU) controls. Measurements Participants with neurodegenerative disease and CU controls were stratified by the presence (severity ≥1 on Neuropsychiatric Inventory Questionnaire) of either depressive symptoms alone, apathy symptoms alone, both symptoms, or neither. A neuropsychological battery evaluated executive function, verbal fluency, verbal learning, working memory, and visuospatial reasoning. Analysis of covariance was used to assess group differences with age, sex, and education as covariates. Results Groups included depressive symptoms only (n = 70), apathy symptoms only (n = 52), both (n = 68), or neither (n = 262). The apathy and depression + apathy groups performed worse than the neither group on tests of working memory (t(312) = −2.4, p = 0.02 and t(328) = −3.8, p = 0.001, respectively) and visuospatial reasoning (t(301) = −2.3, p = 0.02 and t(321) = −2.6, p = 0.01, respectively). The depression, apathy, and depression + apathy groups demonstrated a similar degree of impairment on tests of executive function, processing speed, verbal fluency, and verbal learning when compared to participants without apathy or depression. Sex‐stratified analyses revealed that compared to the male neither group, the male apathy and depression + apathy groups were impaired broadly across all cognitive domains except for working memory. Females with depression alone showed deficits on tests of executive function (t(166) = 2.4, p = 0.01) and verbal learning (t(167) = −4.3, p = 0.001) compared to the female neither group. Conclusions This study demonstrated that in neurodegenerative diseases, apathy with or without depression in males was associated with broad cognitive impairments. In females, depression was associated with deficits in executive function and verbal learning. These findings highlight the importance of effectively treating apathy and depression across the spectrum of neurodegenerative disorders with the goal of optimizing neuropsychological outcomes.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.011
GPT teacher head0.332
Teacher spread0.321 · 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".

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Citations1
Published2024
Admission routes2
Has abstractyes

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