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Record W4383199504 · doi:10.1212/wnl.0000000000207514

White Matter Hyperintensity Trajectories in Patients With Progressive and Stable Mild Cognitive Impairment

2023· article· en· W4383199504 on OpenAlexaff
Farooq Kamal, Cassandra Morrison, Josefina Maranzano, Yashar Zeighami, Mahsa Dadar

Bibliographic record

VenueNeurology · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité du Québec à Trois-RivièresMcGill UniversityDouglas Mental Health University InstituteUniversité du Québec à Montréal
FundersNational Institute on Aging
KeywordsHyperintensityDementiaAmyloid (mycology)White matterCognitive declinePsychologyInternal medicineAlzheimer's diseasePittsburgh compound BNeuroimagingApolipoprotein EMedicineCognitionPathologyMagnetic resonance imagingDiseasePsychiatryRadiology

Abstract

fetched live from OpenAlex

Background and Objectives: White matter hyperintensities are pathological brain changes that are associated with increased age and cognitive decline. However, the association of white matter hyperintensity burden with amyloid positivity and conversion to dementia in people with mild cognitive impairment (MCI) is unclear. The aim of the current study was to expand on this research by examining whether change in white matter hyperintensity burden over time differs in amyloid-negative (Aβ-) and amyloid-positive (Aβ+) people with MCI who either remain stable or convert to dementia. To examine this question, we compared regional white matter hyperintensity burden in four groups: amyloid positive (Aβ+) progressor, amyloid negative (Aβ–) progressor, amyloid positive (Aβ+) stable, and amyloid negative (Aβ–) stable. Methods: Participants with MCI from the Alzheimer’s Disease Neuroimaging Initiative were included if they had APOE ɛ4 status and if amyloid measures were available to determine amyloid status (i.e., amyloid positive, or amyloid negative). Participants with a baseline diagnosis of MCI, had APOE ɛ4 information and amyloid measures were included. An average of 5.7 follow-up timepoints per participant were included, with a total of 5054 follow-up timepoints with a maximum follow-up duration of 13 years. Differences in total and regional white matter hyperintensity burden were examined using linear mixed-effects models. Results: A total of 820 participants (55-90 years of age) were included in the study (Aß+ Progressor, n= 239; Aß– Progressor, n= 22; Aß+ Stable, n= 343; Aß– Stable, n= 216). People who were Aß– stable exhibited reduced baseline white matter hyperintensities compared to Aß+ progressors and Aß+ stable at all regions of interest (β belongs to [.20 –.33], CI belongs to [.03 –.49], p<.02), except Deep white matter hyperintensities. When examining longitudinal results, compared to Aß– stable, all groups had steeper accumulation in white matter hyperintensity burden with Aß+ progressors (β belongs to [-.03–.06], CI belongs to [-.05–.09], p<.01) having the largest increase (i.e., largest increase in white matter hyperintensity accumulation over time). Discussion: These results indicate that white matter hyperintensity accumulation contributes to conversion to dementia in older adults with mild cognitive impairment who are amyloid-positive and negative people.

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)0.003
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.266
Teacher spread0.255 · 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

Citations34
Published2023
Admission routes1
Has abstractyes

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