Sex differences in the executive and behavioral reserve of autosomal dominant frontotemporal dementia
Bibliographic record
Abstract
INTRODUCTION: Self-reported sex influences brain resilience, but its role in genetic frontotemporal dementia (FTD) remains unclear. METHODS: We analyzed 394 genetic-FTD patients and 279 controls from the ALLFTD consortium, assessing annual neuropsychological performance and MRI-based cortical thickness. Clinical characteristics and cortical thickness were compared between sexes. We used the residuals of linear regression models, which predict each participant's cognitive and behavioral performance levels relative to cortical thickness, as a proxy for reserve. We then modeled sex differences in longitudinal trajectories with linear mixed-effects models. RESULTS: Symptomatic females with genetic FTD had lower frontal cortical thickness than males, and the C9orf72 subgroup showed lower-than-expected frontal cortical thickness for a given level of executive functioning. Differences in cognitive reserve between sexes peaked near symptom onset but diminished thereafter. DISCUSSION: Females with genetic FTD showed higher cognitive reserve than males, suggesting that self-reported sex modulates resilience to frontotemporal neurodegeneration. HIGHLIGHTS: Females with genetic FTD showed higher cognitive reserve than males. Those differences were particularly pronounced in the C9orf72 and GRN subgroups. The higher cognitive reserve in females declined as the disease progressed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".