Characterization of hippocampal subfields using histology-based annotated postmortem MRI: Lessons for in vivo segmentation II
Bibliographic record
Abstract
MRI is generally guided by postmortem reference material, which has been limited by small sample sizes that preclude comprehensive characterization of subfield border locations and their variability. Addressing this, we characterized hippocampal subfield border variability in two ultra-high-resolution postmortem MRI datasets with combined annotated histological sections, including cases with and without dementia. We examined: 1) the order of appearance and disappearance of subfields along the long axis of the hippocampus; 2) the order of appearance and disappearance of subicular subregions; 3) the medial-lateral position of subicular subregional boundaries along the hippocampal body; 4) the location of the CA3 relative to hippocampal head digitations; 5) the subfield borders in the hippocampal body relative to a volume proportion of the dark band; and 6) the association of hippocampal length and subiculum-CA1 border location with diagnosis, demographic factors, and factors related to postmortem imaging. Our findings reaffirmed that there is a consistent order of appearance and disappearance of subfields in the hippocampal head and tail, respectively. The subicular subregions exhibited a 'first in, last out' order of appearance and disappearance, and pre/parasubiculum consistently occupied half of the subicular complex in coronal slices throughout the hippocampal body. Hippocampal head digitations were not a reliable landmark for CA3 appearance, but SRLM proportionality did offer a potentially consistent approach for estimating CA2 and CA3 subfield borders in relation to the hippocampal border. No clear relationship was found between the anatomical features and diagnosis, demographic factors, and factors related to postmortem imaging. These findings have implications for the development and harmonization of hippocampal subfield segmentation protocols and interpretation of high-resolution functional MRI studies of the human hippocampus.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".