Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain
Bibliographic record
Abstract
Abstract The internal architecture of the hippocampus is challenging to map in detail using traditional histology and in-vivo neuroimaging. This is due, in part, to its complex archicortical folding that is difficult to appreciate in both modalities. Here, we aimed to overcome this challenge by leveraging the unique histological dataset available as open-source 3D BigBrain. Specifically, we investigated the relationship between topology, laminar cytoarchitecture, and detailed morphology with respect to hippocampal subfields and its anterior-posterior axis. Inspired by computational parcellation methods used in the neocortex, we topologically ‘unfolded’ the hippocampus and mapped it with respect to 5 morphological and 10 laminar features. Several features, including thickness, gyrification, and mean neuronal density, clearly differed between subfields. Indeed, data-driven clustering of all features revealed subdivisions which closely resemble manually defined subfields. Some features, most notably gyrification, also showed anterior-posterior differences within subfields, which may relate to connectivity and functional differences described in previous literature. Overall these findings offer quantifiable markers of hippocampal subfields, and provide new anatomical insight into the topology and properties of hippocampal tissue. Future applications could involve translation to in-vivo MRI for probing the internal hippocampal architecture at this mesoscale in cognition and 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".