Automated Hippocampal Subfield Volumetric Analyses in Atypical Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are two of the most common variants of atypical Alzheimer's disease (AD). Both PCA and LPA are associated with relative sparing of hippocampus compared to neocortex, although hippocampal atrophy is observed. It is unclear whether regional patterns of hippocampal subfield involvement differ between PCA and LPA, and whether they differ from typical AD. OBJECTIVE: To assess volume of specific subfields of the hippocampus in PCA, LPA, and typical AD. METHODS: Fifty-nine patients with PCA and 77 patients with LPA were recruited and underwent T1-weighted MRI and Pittsburgh Compound B (PiB) PET at Mayo Clinic. Thirty-six probable AD patients and 100 controls were identified from the Alzheimer's Disease Neuroimaging Initiative. Hippocampal subfield volumes were calculated using Freesurfer, and volumes were compared between PCA, LPA, AD, and controls using Kruskal-Wallis and Dunn tests. RESULTS: The LPA and PCA groups both showed the most striking abnormalities in CA4, presubiculum, molecular layer of the hippocampus, molecular and granule cell layers of the dentate gyrus, and the hippocampal-amygdala transition area, although atrophy was left-sided in LPA. PCA showed smaller volume of right presubiculum compared to LPA, with trends for smaller volumes of right parasubiculum and fimbria. LPA showed a trend for smaller volumes of left CA1 compared to PCA. The AD group showed smaller volumes of the right subiculum, CA1, and presubiculum compared to LPA. CONCLUSION: Patterns of hippocampal subfield atrophy differ across the different syndromic variants of AD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".