Sex-specific brain atrophy patterns associated with the motoric cognitive risk syndrome
Bibliographic record
Abstract
Background Females are twice as likely to be diagnosed with Alzheimer's disease (AD) as males, but the underlying mechanisms of this sex difference are not well-understood. The motoric cognitive risk (MCR) syndrome is characterized by slow gait and subjective cognitive concerns and predicts both AD and vascular dementia (VaD). The prevalence of MCR is typically similar in females and males. We have previously shown that MCR is associated with cortical atrophy in frontal, parietal, and temporal regions. Objective The current study examined sex-specific, cortical (frontal), and subcortical (hippocampal) atrophy patterns associated with MCR. Methods Frontal cortical thicknesses (in 11 frontal regions) and hippocampal volumes (in 12 hippocampal subfields) were quantified in 940 females ( M Age = 71.03 years) and 1108 males ( M Age = 71.07 years). Sex-stratified linear models were used to examine frontal cortical thicknesses and hippocampal volumes as a function of MCR—after adjusting for age, education, total intracranial volume, study site, vascular comorbidities, white matter lesion burden, and multiple comparisons (with the false discovery rate). Results MCR-related frontal atrophy was observed (in pars orbitalis and caudal middle frontal) in males but not in females. MCR-related hippocampal atrophy (in CA1, molecular layer, GCMLDG, and Fimbria) was observed in females but not in males. Conclusions There are sex-specific patterns of atrophy associated with MCR. Females with MCR display brain atrophy patterns more consistent with early AD, while males with MCR display atrophy patterns more consistent with VaD.
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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 | 0.000 | 0.001 |
| 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".