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Record W4390197060 · doi:10.1002/alz.081853

Amyloid and tau pathology are associated with white matter properties in cognitively unimpaired older adults at risk of AD dementia

2023· article· en· W4390197060 on OpenAlexaff
Ting Qiu, François Rheault, Jean‐Paul Soucy, R. Nathan Spreng, Alexa Pichet Binette, Maxime Descoteaux, Sylvia Villeneuve

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsMcGill Genome CentreUniversité de SherbrookeMcGill University
Fundersnot available
KeywordsWhite matterFractional anisotropyDementiaPsychologyPathologyFasciclePosterior cingulateMedicineNeuroscienceMagnetic resonance imagingCortex (anatomy)DiseaseAnatomy

Abstract

fetched live from OpenAlex

Abstract Background White matter changes are hypothesized to be among the earliest changes occurring in the course of Alzheimer’s disease (AD). We investigated associations between AD pathology, namely amyloid and tau, and white matter microstructural changes in cognitively unimpaired individuals at risk of AD dementia. We expected higher levels of pathology to be related to lower free‐water‐corrected fractional anisotropy. Method We studied 223 cognitively normal older adults with PET and multi‐shell diffusion MRI from the PREVENT‐AD cohort. We first measured standardized uptake value ratios (SUVRs) in 40 brain regions for amyloid‐ß (Aß, i.e., lateral and medial prefrontal, parietal, lateral temporal, and cingulate cortical regions in both hemispheres) and 26 brain regions for tau (i.e., Braak stages I, III, and IV). We then used TractSeg, a deep learning framework, to segment 49 well‐established white matter bundles. We mapped free‐water‐corrected fractional anisotropy (FAT) in those bundles. Lastly, to explore the relationships between AD pathology and white matter microstructure across bundles, we applied two separate partial least squares analyses: one for Aß pathology and one for tau (Figure 1). Result We found one significant latent variable relating Aß pathology to FAT. Specifically, higher levels of Aß pathology were associated with the combination of higher levels of FAT within the rostral body of the uncinate fascicle and superior longitudinal fascicle (Figure 2A). We found two significant latent variables relating tau pathology to FAT for which higher levels of tau pathology were associated with elevated FAT within the uncinate fascicle, thalamo‐occipital bundle, optic radiation, inferior occipitofrontal fascicle, inferior longitudinal fascicle, middle longitudinal fascicle, etc. (Figure 2B). Conclusion In cognitively unimpaired individuals at risk of AD, increased Aß and tau pathology were associated with higher levels of FAT in AD‐related bundles. These unexpected findings may suggest an inverted U‐shape pattern between AD pathology and FAT in the preclinical phase of AD for which FAT values would start by increasing before decreasing later in the course of the disease. This first increase might be a marker of early pathological processes such as neuroinflammation or swelling.

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)0.003
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.039
GPT teacher head0.277
Teacher spread0.238 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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