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Record W3030044213 · doi:10.1101/2020.05.27.119586

The Temporal Relationships between White Matter Hyperintensities, Neurodegeneration, Amyloid β, and Cognition

2020· preprint· en· W3030044213 on OpenAlexafffund
Mahsa Dadar, Richard Camicioli, Simon Duchesne, D. Louis Collins

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversité LavalUniversity of AlbertaCentres Intégré Universitaires de Santé et de Services Sociaux
FundersNational Institutes of HealthH. Lundbeck A/SNorthern California Institute for Research and EducationCanadian Institutes of Health ResearchUniversity of Southern CaliforniaNovartis Pharmaceuticals CorporationMeso Scale DiagnosticsServierPfizerFoundation for the National Institutes of Health
KeywordsHyperintensityNeurodegenerationCognitive declineWhite matterAtrophyAlzheimer's diseasePsychologyContext (archaeology)NeuroscienceCognitionDementiaMedicinePathologyInternal medicineDiseaseMagnetic resonance imagingBiology

Abstract

fetched live from OpenAlex

ABSTRACT INTRODUCTION Cognitive decline in Alzheimer’s disease is associated with amyloid-β accumulation, neurodegeneration and cerebral small vessel disease, but the temporal relationships between these factors is not well established. METHODS Data included white matter hyperintensity (WMH) load, grey matter (GM) atrophy and Alzheimer’s Disease Assessment Scale-Cognitive-Plus (ADAS13) scores for 720 participants and cerebrospinal fluid amyloid (Aβ1-42) for 461 participants from the Alzheimer’s Disease Neuroimaging Initiative. Linear regressions were used to assess the relationships between baseline WMH, GM, and Aβ1-42 to changes in WMH, GM, Aβ1-42, and cognition at one-year follow-up. RESULTS Baseline WMHs and Aβ1-42 predicted WMH increase and GM atrophy. Baseline WMHs, GM, and Aβ1-42 predicted worsening cognition. Only baseline Aβ1-42 predicted change in Aβ1-42. DISCUSSION Baseline WMHs lead to greater future GM atrophy and cognitive decline, suggesting that WM damage precedes neurodegeneration and cognitive decline. Baseline Aβ1-42 predicted WMH increase, suggesting a potential role of amyloid in WM damage. Research in Context Systematic Review : Both amyloid β and neurodegeneration are primary pathologies in Alzheimer’s disease. White matter hyperintensities (indicative of presence of cerebrovascular disease) might also be part of the pathological changes in Alzheimer’s. However, the temporal relationship between white matter hyperintensities, amyloid β, neurodegeneration, and cognitive decline is still unclear. Interpretation : Our results establish a potential temporal order between white matter hyperintensities, amyloid β, neurodegeneration, and cognitive decline, showing that white matter hyperintensities precede neurodegeneration and cognitive decline. The results provide some evidence that amyloid β deposition, in turn, precedes accumulation of white matter hyperintensities. Future Directions : The current findings reinforce the need for future longitudinal investigations of the mechanisms through which white matter hyperintensities impact the aging population in general and Alzheimer’s disease patients, in particular.

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)0.008
metaresearch head score (Gemma)0.044
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0030.005
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.250
Teacher spread0.217 · 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
Published2020
Admission routes2
Has abstractyes

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