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Record W2981258327 · doi:10.1016/j.jalz.2019.06.1047

P1‐442: WHITE MATTER HYPERINTENSITIES AND COGNITIVE DECLINE IN FRONTO‐TEMPORAL DEMENTIA VARIANTS

2019· article· en· W2981258327 on OpenAlexaff
Mahsa Dadar, Ana L. Manera, Simon Ducharme, D. Louis Collins

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldNeuroscience
TopicBrain Tumor Detection and Classification
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsFrontotemporal dementiaHyperintensityCohortPsychologySemantic dementiaDementiaBoston Naming TestNeuroimagingFluid-attenuated inversion recoveryAudiologyCognitive declineNeuropsychologyFrontotemporal lobar degenerationCognitionCardiologyMedicineNeuroscienceMagnetic resonance imagingInternal medicineDiseaseRadiology

Abstract

fetched live from OpenAlex

White matter hyperintensities (WMHs) are areas of increased signal on FLAIR images which indicate the presence of small-vessel disease in the brain. They are associated with increased cognitive deficits in aging as well as Alzheimer's disease. We investigated the differences in WMH burden between variants of frontotemporal dementia (i.e. behavioral, semantic, and progressive non-fluent aphasia abbreviated as bv-FTD, sv-FTD, and pnfa-FTD) and their relation to cognitive decline. Longitudinal imaging/clinical data were obtained from the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset for normal controls, individuals with bv-FTD, sv-FTD, and pnfa-FTD (NControl=120,Nbv-FTD=59,Nsv-FTD=35,Npnfa-FTD=30). WMHs were segmented using a previously validated pipeline based on location and intensity features from T1w and FLAIR images [Dadar et al., NeuroImage 2017]. WMH volumes were calculated per each brain lobe and hemisphere. Mixed-effects models were used to assess the WMH differences between controls and FTD patients (Model 1: WMH∼1+Cohort+Age+1|ID+1|Site) and their effect on cognition, as measured by the Mini-Mential State Examination (MMSE) scores (Model 2: MMSE∼1+ WMH+Cohort+WMH:Cohort+Age+1|ID+1|Site). In Model 1, the variable of interest was Cohort (i.e. Control vs bv-FTD, sv-FTD, and pnfa-FTD). In Model 2, the variable of interest was the interaction between Cohort and WMH load, denoted by WMH:Cohort. ID and Site were considered as categorical random effects. WMHs increased significantly with age for all lobes (p<0.01). Compared to controls, bv-FTD patients had significantly higher WMH loads in the frontal (βRight=0.805,pRight<0.0001, βLeft=0.796,pLeft<0.0001), parietal (βRight=0.647,pRight<0.0001, βLeft=0.411,pLeft=0.002), and occipital lobes (βRight=0.345,pRight=0.01, βLeft=0.302,pLeft=0.02), sv-FTD patients had higher WMH loads in the parietal (βRight=0.465,pRight=0.002, βLeft=0.373,pLeft=0.02) and left frontal (βLeft=0.325,pLeft=0.03) and temporal (βRight=0.288,pRight=0.05, βLeft=0.469,pLeft=0.003) lobes, and pnfa-FTD patients had higher WMH loads in the right parietal lobe (βRight=0.423,pRight=0.009) (Fig.1). Compared to controls, decrease in MMSE was associated with increased WMH load in frontal lobe in bv-FTD (βRight=−0.309,pRight=0.001, βLeft=−0.373,pLeft=0.0001) and pnfa-FTD patients (βRight=−0.686,pRight<0.0001, βLeft=−0.685,pLeft<0.0001), as well as parietal lobe in pnfa-FTD patients (βRight=−0.443,pRight=0.02, βLeft=−0.449,pLeft=0.001), and right parietal lobe in bv-FTD patients (βRight=−0.215,pRight=0.02) (Fig.2).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.259
Teacher spread0.229 · 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; both teacher heads agree on what is shown here.

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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Citations0
Published2019
Admission routes1
Has abstractyes

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