Analysis of the MIRIAD Data Shows Sex Differences in Hippocampal Atrophy Progression
Bibliographic record
Abstract
BACKGROUND: Hippocampus (HC) atrophy is a hallmark of early Alzheimer's disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and cognitive deficits and rates of brain atrophy in early AD dementia. It is important to corroborate these findings on independent datasets. OBJECTIVE: To study temporal rates of HC atrophy over a one-year period in probable AD patients and cognitively normal (CN) subjects by longitudinal MRI scans obtained from the Minimal Interval Resonance Imaging in AD (MIRIAD) database. METHODS: We used a novel algorithm to compute an index of hippocampal (volumetric) integrity (HI) at baseline and one-year follow-up in 43 mild-moderate probable AD patients and 22 CN subjects in MIRIAD. The diagnostic power of longitudinal HI measurement was assessed using a support vector machines (SVM) classifier. RESULTS: The HI was significantly reduced in the AD group (p < 10(-20)). In addition, the annualized percentage rate of reduction in HI was significantly greater in the AD group (p < 10(-13)). Within the AD group, the annual reduction of HI in women was significantly greater than in men (p = 0.008). The accuracy of SVM classification between AD and CN subjects was estimated to be 97% by 10-fold cross-validation. CONCLUSION: In the MIRIAD patients with probable AD, the HC atrophies at a significantly faster rate in women as compared to men. Female sex is a risk factor for faster descent into AD. The HI measure has potential for AD diagnosis, as a biomarker of AD progression and a therapeutic target in clinical 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".