Hippocampal subfield volumes and memory performances: associations with stimulus modality and free recall or recognition
Bibliographic record
Abstract
Abstract Background Age‐related memory decline is well associated with hippocampal atrophy, although the specific role of hippocampal subfields in memory function has not been thoroughly investigated yet. In this study, we investigated the associations between hippocampal subfield volumes and free recall and recognition performances in verbal and visual memory tasks in older adults without dementia. Method We selected 97 right‐handed participants without dementia aged 60 to 85, 42 males, from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. From T2‐weighted MRIs with 0.7*0.7*1mm voxel size, the hippocampi were segmented five subfields: 1) DG (DG); 2) CA2 with CA3 (CA2/CA3); 3) CA1; 4) strata radiatum, lacunosum and moleculare (SRLM); and 5) subiculum by using the MAGeT‐Brain algorithm. Memory was assessed with Rey Auditory Learning test for verbal free recall (RAVLT7) and recognition (RAVLT‐R), and with Aggie Figure Learning test for visual free recall (AFLT7) and recognition (AFLT‐R). Linear models were used to test the association between hippocampal subfield volumes and memory performances with age, sex and total intracranial volume as covariates (p<0.05, corrected for false discovery rate) Result The volumes of bilateral CA1 and SRLM were significantly associated with RAVLT7 and RAVLT‐R. The right DG volume was significantly associated with AFLT7 and RAVLT7. AFLT7 was also associated with the volume of right CA2/CA3 while AFLT‐R was not associated with any hippocampal subfield (see Figures 1 & 2). Conclusion Our results are consistent with the view that hippocampal subfields contribute differently to memory traces: CA1 for recollection that requires contextual information; the DG for pattern separation (creating new distinct representations of each stimulus); and CA3 for pattern completion (the recollection of a stimulus from a partial memory). Contextual information is more important when recollecting known words, while distinct and efficient encoding may be especially useful for search strategies inherent to free recall. Pattern completion could be particularly useful to efficiently recollect visual abstract figures based on visual features during free recall. Overall, this shows that hippocampal subfield segmentation offers a better understanding of their distinctive roles in cognition.
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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 | 0.000 |
| 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) | 0.003 | 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".