Discrete Subfields and Continuous Gradients Coexist: A Multi-Scale View of Hippocampal Organization
Bibliographic record
Abstract
Abstract The human hippocampus is studied via two competing frameworks: one dividing it into discrete anatomical subfields with distinct computational processes, and another describing it as a continuous, functional gradients along the anterior-posterior and medial-lateral axes. How these organizational principles relate to one another, particularly regarding intrinsic neural timescales of the hippocampus, remains unknown. Here, we used high-resolution resting-state fMRI to investigate how single-voxel autocorrelation, a measure of intrinsic neural timescale, maps onto hippocampal subfields. We found evidence for a hybrid organization. First, consistent with our predictions, we observed significantly higher autocorrelation (longer timescales) in the subiculum compared to the other subfields. Contrary to our hypotheses, we found that CA1, which is implicated in integration, had low autocorrelation whereas CA2/3 and CA4DG, which are linked to pattern separation, had intermediate autocorrelation. Second, we discovered that the overarching anterior-posterior and medial-lateral gradients of autocorrelation were recapitulated within each individual subfield. Finally, data-driven clusters of autocorrelation values aligned more strongly with the continuous gradients than with the discrete anatomical boundaries, particularly in the right hemisphere. These results suggest that the discrete and continuous views of hippocampal organization are not mutually exclusive but coexist across different spatial scales. We therefore propose a new comprehensive model of hippocampal function that integrates both its modular, subfield-specific properties and its graded, large-scale organization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".