A harmonized, histology‐based protocol for selection of medial temporal lobe cortical subregion ranges on magnetic resonance imaging
Bibliographic record
Abstract
Abstract Background The medial temporal lobe (MTL) has distinct cortical subregions that are differentially vulnerable to pathology and neurodegeneration in diseases such as Alzheimer’s disease. However, previous protocols for segmentation of MTL cortical subregions on magnetic resonance imaging (MRI) vary substantially across research groups, and have been informed by different cytoarchitectonic definitions, precluding consistent interpretations. The Hippocampal Subfields Group aims to create a harmonized, histology‐based protocol for segmentation of MTL cortical subregions that can reliably be applied to T2‐weighted MRI with high in‐plane resolution. Method Nissl‐stained sections from the temporal lobes of three human specimens (66‐90 years old; 2 female) were annotated by four expert neuroanatomists for the following MTL subregions: entorhinal cortex (ERC), Brodmann’s Area 35 (BA35; largely corresponding to “transentorhinal” cortex), Brodmann’s Area 36 (BA36), and parahippocampal cortex (PHC). On each histology section, the number of annotations and the spatial overlap of annotations were analyzed to determine the consensus of the anterior to posterior range of each structure. Gross anatomical landmarks, detectable on MRI and reliably corresponding with each range, were then selected to create an MRI ranging protocol. Feasibility of this MRI protocol was tested by two independent raters across four MRI scans (two healthy adults, two older adults), and agreement in range selection was assessed using Cohen’s kappa statistic. Result The proposed MTL ranging protocol is shown in Fig. 1, and corresponding histology data substantiating the protocol is shown in Fig. 2. MRI‐visible gross anatomical landmarks that reliably corresponded with the anterior or posterior range of each subregion on histology included the anterior‐most appearance of the collateral sulcus (Fig. 3A), hippocampal head (Fig. 3B), hippocampal body, and anterior calcarine fissure (Fig. 3C). This protocol demonstrated high feasibility when applied to MRI, with average kappa values of 0.75 ± 0.07, representing a “substantial” level of agreement of range selection. Conclusion Future directions include obtaining consensus on this protocol from the larger research community through a Delphi procedure, and expansion of the protocol to include slice‐by‐slice segmentation guidelines for full delineation. This harmonized, histology‐based protocol will facilitate critical research on MTL subregion vulnerability and their contributions to memory deficits in Alzheimer’s disease.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".