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Record W3107526775 · doi:10.1101/2020.11.30.20240879

Ventricular expansion, white matter hyperintensities, and global cognition in Alzheimer’s disease and normal aging

2020· preprint· en· W3107526775 on OpenAlexafffund
Sabrina Adamo, Joel Ramirez, Melissa F. Holmes, Fuqiang Gao, Ljubica Zotovic, Mario Masellis, Sandra E. Black

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHeart and Stroke FoundationToronto Dementia Research AllianceHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersUniversity of TorontoCanadian Institutes of Health ResearchLinda C. Campbell FoundationSunnybrook Research InstituteOntario Brain Institute
KeywordsHyperintensityDementiaCardiologyClinical Dementia RatingCognitionMedicineWhite matterInternal medicineCognitive declineMini–Mental State ExaminationPsychologyAlzheimer's diseaseDiseaseAudiologyMagnetic resonance imagingPsychiatryRadiology

Abstract

fetched live from OpenAlex

ABSTRACT Background The progression of Alzheimer’s Disease (AD) may be tracked by measuring the growth of the ventricular cerebrospinal fluid (vCSF) over time. AD is commonly comorbid with markers of cerebral small vessel disease (SVD), viewed on MRI as white matter hyperintensities (WMH). Larger WMH volumes are correlated with poorer cognitive test scores. Additionally, periventricular WMHs have a proposed relationship to the vCSF. Purpose This study will examine ventricular expansion and its associations between periventricular/deep WMH and cognition in AD and normal aging. Methods Baseline and 1-year follow-up data were collected from AD (n=117) and cognitively normal control (NCs; n=49) participants taking part in the Sunnybrook Dementia Study. MRI (1.5T) and scores from both the Mini-Mental State Examination (MMSE) and the Dementia Rating Scale (DRS) were assessed at each time point. Volumetric data was generated using a semi-automated pipeline and each individual’s vCSF and WMHs were transformed to an intermediate space to determine volumetric growth. Regressions were used to determine relationships between vCSF growth measures, SVD burden, and cognition, accounting for demographics and individual interscan intervals. Results The AD group displayed 14.6% annual ventricular growth as opposed to NC who had only 11.8% annual growth. AD showed significant growth in vCSF (p < 0.001), a trend toward greater pWMH growth (p = 0.06) and no difference in dWMH growth volumes compared to NC. vCSF growth was positively associated with pWMH (β = 0.32, p < 0.001) but not dWMH growth in AD while in NC it was associated with both pWMH (β = 0.48, p < 0.001) and dWMH growth (β = 0.35, p = 0.02). In AD, vCSF growth was associated with the both the MMSE (β = -0.30, p < 0.001) and the DRS (β = -0.31, p < 0.001) in separate models. Conclusions The findings from this study suggest that in just under 1.5 years, the significantly rapid ventricular expansion observed in AD may be closely related to periventricular small vessel disease. As vCSF growth rates are an important biomarker of AD neurodegeneration that corresponds with cognitive decline, future research should further explore atrophy associated with periventricular vasculopathy. Trial Registration ClinicalTrials.gov , NCT01800214 . Registered on 27 February 2013.

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 score0.004
Threshold uncertainty score0.009

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 science0.0000.000
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 head0.026
GPT teacher head0.295
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

Citations2
Published2020
Admission routes2
Has abstractyes

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