The Compensatory Role of the Hippocampus in Working Memory Among Older Adults With Low MoCA Scores
Bibliographic record
Abstract
ABSTRACT Evidence suggests that working memory (WM) capacity decreases with age, resulting in cognitive decline. Given the link between aging and reduced hippocampal volume, this study examined whether and how hippocampal volume is associated with WM. 46 participants aged 65–85 years (Mage = 71.80, SD = 5.05, 17.4% male) took part in the study. WM was assessed with the Numbers Reversed test, cognitive functioning with the Montreal Cognitive Assessment (MoCA), and hippocampal structural data were obtained via magnetic resonance imaging. We hypothesized that hippocampal substructure volume would correlate with WM performance in older adults. Additionally, considering that the hippocampus interacts extensively with the fronto‐parietal network, which is regarded as the core WM network, we hypothesized that this association would be stronger in adults with mild cognitive impairment (MCI), reflecting a compensatory role of the hippocampus. The results showed a statistically significant relationship between WM and hippocampus ( r s = 0.35, p < 0.05) and several hippocampal subsections in right and left hemispheres; however, the associations weakened after controlling for estimated total intracranial volume and MoCA scores in a regression model ( R 2 = 0.113, F = 5.61, p = 0.022). At the group level, the MCI group exhibited stronger and more widespread associations between WM and hippocampal subregions than the cognitively intact group (R 2 values varying from 0.25 to 0.579, p ≤ 0.05). The results suggest that the hippocampus may play a more direct role in WM performance in older adults, particularly in the context of cognitive impairment, pointing to a possible compensatory mechanism and the involvement of long‐term memory processes in WM.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".