Cortical microstructure is associated with disease severity and clinical progression in genetic frontotemporal dementia: a GENFI study
Bibliographic record
Abstract
The study of genetic frontotemporal dementia (FTD) allows investigating its earliest presymptomatic stages. Using cross-sectional T1-weighted and diffusion-weighted MRI, we test the hypothesis that cortical microstructural alterations, quantified as cortical mean diffusivity (cMD), are detectable earlier and are more strongly associated with clinical progression than cortical thickness (CTh). The sample comprised n = 710 individuals (47.8 ± 13.5 years, 56.6% female, 14.1 ± 3.3 years of education), including 118 symptomatic carriers and 305 presymptomatic carriers with mutations in C9orf72, GRN or MAPT genes, and 287 non-carriers, collected from 24 GENFI sites. A subset of n = 453 individuals (289 carriers, 164 non-carriers) were investigated across Clinical Dementia Rating (CDR) = 0, 0.5 and ≥1 stages. Two subsets had longitudinal clinical outcome measures, including n = 403 individuals (239 carriers, 164 non-carriers) with Cambridge Behavioural Inventory-Revised scores during 2.8 ± 1.6 years, and n = 261 individuals (164 carriers, 97 non-carriers) with CDR Sum-of-Boxes scores during 2.0 ± 0.8 years. Regional cMD and CTh were entered into linear mixed-effects models incorporating age, sex and education as covariates; site, and individual nested within site were random intercepts. The results demonstrated that cMD is more sensitive than CTh to track early cortical injury, with elevated cMD first observed at CDR = 0 in C9orf72 carriers, followed by MAPT carriers (from CDR = 0.5 stage), and by GRN carriers (beginning at CDR ≥ 1). At all stages, cortical microstructural injury had stronger effect size and was more widespread than cortical thinning. In all mutation carrier types, cMD was more strongly associated than CTh with subsequent clinical progression. Cortical microstructure is a promising biomarker to identify at-risk individuals before atrophy and clinical progression, with utility in therapeutic trials.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".