Curved multiplanar reformatting allows the accurate histological delineation of hippocampal subfields
Bibliographic record
Abstract
BACKGROUND: Hippocampal subfields perform specific roles in normal cognitive functioning and have distinct vulnerabilities in neurological disorders. However, measurement of subfields with MRI is technically difficult in the head and tail of the hippocampus. Recent studies have utilized curved multiplanar reconstruction (CMPR) to improve subfield visualization in the head and tail, but this method has not yet been applied to histological data. METHODS: We utilized BigBrain data, an open-source database of serially sectioned histological data for our analyses. The left hippocampus was segmented according to histological criteria by two raters in order to evaluate intra- and inter-rater reliability of histology-based segmentation throughout the long axis. Segmentation according to our previous protocol for the hippocampal body was then compared to these histological measurements to evaluate for histological validity. Agreement between segmentations was evaluated using Dice similarity coefficients (DSCs). RESULTS: Intra-rater reliability (DSCs) of histological segmentation was excellent for all subfields: CA1 (0.8599), CA2 (0.7586), CA3/CA4/DG (0.8907), SLM (0.9123), subiculum (0.8149). Similarly, inter-rater reliability analysis demonstrated excellent agreement (DSCs) for all subfield locations: CA1 (0.8203), CA2 (0.7253), CA3/CA4/DG (0.8439), SLM (0.8700), subiculum (0.7794). Finally, histological accuracy (DSCs) for our previous protocol was excellent for all subfields: CA1 (0.8821), CA2 (0.8810), CA3/CA4/DG (0.9802), SLM (0.9879), subiculum (0.8774). When subfields in the hippocampus head, body, and tail were analyzed independently, DSCs also showed excellent agreement. CONCLUSIONS: CMPR allows reliable subfield segmentation based on histological criteria throughout the hippocampal head, body, and tail. Our previous protocol for the hippocampal body can be applied to provide histologically valid subfield measurements throughout the entire hippocampal long axis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".