P1‐442: WHITE MATTER HYPERINTENSITIES AND COGNITIVE DECLINE IN FRONTO‐TEMPORAL DEMENTIA VARIANTS
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".