Longitudinal Surface-Based Morphometry Changes in the Hippocampus in Dementia
Bibliographic record
Abstract
Abstract The hippocampus is central to Alzheimer’s disease (AD), characterized by atrophy and cognitive decline. While volume loss is well-documented, surface-based morphometric (SBM) features—curvature, gyrification, and thickness—remain less explored. Using T1-weighted MRI data from the Alzheimer’s Disease Neuroimaging Initiative (4,617 timepoints; CN: 475, MCI: 673, AD: 269), hippocampal subfields were analyzed with HippUnfold. Linear mixed effects models examined volume and SBM changes, tracking cognitive trajectories in stable (n = 1017) and progressing (n = 301) individuals. Focusing on CA1, the stable AD group showed significant volume reductions (β = −1.01, p < .001) compared to CN (β = 6.19, p < .001). SBM metrics significantly increased over time across subregions (e.g., curvature: CN: β = −0.72, p < .001; AD: β = 0.006, p < .001), though gyrification did not reach significance in bilateral CA1, CA3, CA4, and subiculum. Time-dependent interactions indicated progressive volume loss and SBM increases across groups (all p’s < .001). Notably, SBM metrics predicted cognitive improvements in AD. While volume loss remains a key AD marker, it may not capture early morphometric changes. SBM features provide additional insights, underscoring the need for integrated volumetric and surface-based analyses to refine disease detection and therapeutic strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".