Longitudinal Surface‐Based Morphometry Changes in the Hippocampus in Dementia
Bibliographic record
Abstract
BACKGROUND: The hippocampus plays a critical role in Alzheimer's disease (AD), marked by brain atrophy and cognitive decline. AD pathology begins during the transition from healthy aging to mild cognitive impairment (MCI) and eventually AD, with memory impairment as a hallmark. While hippocampal volume reductions are well-documented, surface-based morphometric (SBM) features-curvature, gyrification, and thickness-remain less understood. METHOD: T1-weighted MRI data from 3.51 average annual scans (5,263 timepoints) across four phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using HippUnfold, a hippocampal subfield segmentation tool. Individuals (CN: 475; MCI: 673; AD: 269) were grouped by final clinical diagnosis to track cognitive trajectories: stable/non-progressors (n = 1017) and progressors (n = 301). Linear mixed effects models adjusted for age, education, sex, scanner-site, and eTIV evaluated hippocampal volume and surface metrics (CA1-CA4, DG, Subiculum, SRLM, and Cysts), with CN as the intercept. RESULTS: Focusing on CA1, the region most vulnerable to early AD, the stable AD group showed significant volume reductions (β = -1.05, p < .001) compared to CN (β = 3.88, p < .001). All SBM metrics (curvature, gyrification, and thickness) showed significant changes, with curvature in CN (β = -0.48, p < .001) and AD (β = 0.01, p < .001). Time-dependent interactions showed volume reductions in CN (β = -0.04, p < .001) and MCI (β = -0.07, p < .001), and increases in SBM metrics (all p's < .001). Cognitive domain analyses showed volume changes primarily affect memory and executive functioning, while SBM metrics influence language and visuospatial ability. Curvature influenced language (61.76%, p < .001) and visuospatial ability (32.35%, p < .001), while thickness affected language (57.14%, p < .001) and visuospatial ability (35.71%, p < .001). CONCLUSION: Hippocampal volume reductions are well-established markers of AD, but surface-based features like curvature, gyrification, and thickness provide additional insights, revealing changes that volume alone may miss. These findings highlight the importance of integrating surface-based metrics with volumetric analyses to improve understanding of disease mechanisms and interventions.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".