Bibliographic record
Abstract
The uncal apex is an anatomical landmark frequently used for segmenting the hippocampus into its anterior and posterior segments, a necessary step for computing many measurements of its long axis. It functions well, as it is both local to the hippocampus and easy to identify. However, in spite of widespread use and definition in the EADC-ADNI Harmonized Hippocampal Protocol (HarP), how the uncal apex is influenced by gross hippocampal changes during normal aging has not been established, nor has the possible impact on measures of anterior hippocampus (aHPC) and posterior hippocampus (pHPC) volume. Here I drew upon three large data sets to describe and confirm these relationships, investigating them in one large data set and replicating my findings in the two others, evaluating a total of 4,434 hippocampi. I found the uncal apex fell in an increasingly more anterior position with increasing age. This age-related retraction of the uncus began after age 36, with the sharpest effects arising after age 60. This phenomenon exaggerates age-related aHPC volume decreases while simultaneously underestimating age-related pHPC volume decreases, a pattern I confirmed by juxtaposing uncal apex and MNI space-based landmarking. A hippocampally based reference frame was also rendered unstable by age-related shifts in the posterior extent of the hippocampus. Both the uncal apex and hippocampal reference frame should therefore be used with caution in aging research, or in research involving other demographic or disease factors known to evoke gross changes in the hippocampus. Instead, MNI coordinate-based heuristics may be appropriate for segmenting the hippocampus in study designs involving such factors. Apex-based segmentation is still attractive, however, in study designs where advanced age and atrophy are not used as regressors, including investigations into long-axis effects in healthy young adults. Progress toward localizing functional divisions within the hippocampus is needed to identify best practices for the field.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".