Hippocampal Subfield Volumes in Amateur Marathon Runners
Bibliographic record
Abstract
PURPOSE: Numerous studies have implicated the involvement of structure and function of the hippocampus in physical exercise, and the larger hippocampal volume is one of the relevant benefits reported in exercise. It remains to be determined how the different subfields of hippocampus respond to physical exercise. METHODS: A 3D T1-weighted magnetic resonance imaging was acquired in 73 amateur marathon runners (AMR) and 52 healthy controls (HC) matched with age, sex, and education. The Montreal Cognitive Assessment, the Pittsburgh Sleep Quality Index (PSQI), and the Fatigue Severity Scale were assessed in all participants. We obtained hippocampal subfield volumes using FreeSurfer 6.0. We compared the volumes of the hippocampal subfield between the two groups and ascertained correlation between the significant subfield metrics and the significant behavioral measure in AMR group. RESULTS: The AMR had significantly better sleep than HC, manifested as with lower score of PSQI. Sleep duration in AMR and HC was not significantly different from each other. In the AMR group, the left and right hippocampus, cornu ammonis 1 (CA1), CA4, granule cell and molecular layers of the dentate gyrus, molecular layer, left CA2-3, and left hippocampal-amygdaloid transition area volumes were significantly larger compared with those in the HC group. In AMR group, the correlations between the PSQI and the hippocampal subfield volumes were not significant. No correlations were found between hippocampal subfield volumes and sleep duration in AMR group. CONCLUSIONS: We reported larger volumes of specific hippocampal subfields in AMR, which may provide a hippocampal volumetric reserve that protects against age-related hippocampal deterioration. These findings should be further investigated in longitudinal studies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".