P2‐060: A Harmonized Protocol for Medial Temporal Lobe Subfield Segmentation: Initial Results of The 3‐Tesla Protocol For The Hippocampal Body
Bibliographic record
Abstract
Alzheimer’s disease (AD), aging and vascular pathology are proposed to differentially affect subregions of the medial temporal lobe (MTL). Characterizing these differences may provide more insight in disease processes and better biomarkers. However, the comparison of different studies is hampered by differences in how these subfields are segmented on in vivo MRIs by different research groups. The Hippocampal Subfields Group (HSG) was formed to create a harmonized protocol for MTL subregion segmentation. The group began developing a subfield segmentation protocol for the hippocampal body using high-resolution T2-weighted 3 tesla MRI. We present the initial results of this effort. Stage 1. Establish the anterior and posterior borders of the hippocampal body as a whole and the boundaries with surrounding structures, 2. Determine subfield boundaries on histological sections throughout the hippocampal body in three specimens, 3. Identify the corresponding boundaries between adjacent subfields on in vivo MRI, 4. Solicit feedback on the initial protocol from the HSG, and 5. Perform formal reliability analysis by six experts. The initial results are from stage 1. The anterior border was defined as one slice posterior to the last slice containing the uncal apex and the posterior border as the most posterior slice containing the colliculi. A reliability test yielded a Fleiss κ >0.75. An initial protocol for the boundaries with surrounding structures is also developed, with the dorsal and lateral border at the interface of the gray matter of the hippocampus and the white matter of the alveus/fimbria, the ventral border placed at the parahippocampal white matter and the medial border at the most medial point of the hippocampus. Additionally, the histological annotations are being finalized and an MRI protocol for the subfield boundaries is being prepared. The harmonized protocol, once completed, is expected to impact the field significantly by producing measurements that are comparable between labs and by making it easier to relate and pool results from different studies. Given the heterogeneity of AD, vascular and aging-related changes in the MTL, a reliable measure of subfield-specific effects may lead to more powerful biomarkers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.024 |
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".