Age- and reserve-related increases in fronto-parietal and anterior hippocampal activity during episodic encoding predict subsequent memory
Bibliographic record
Abstract
Abstract Remembering associations between encoded items and their contextual setting is a feature of episodic memory. Although this ability deteriorates with age in general, there is substantial variability in how older individuals perform on episodic memory tasks. This variability may stem from genetic and/or environmental factors related to reserve, allowing some individuals to compensate for age-related decline through differential recruitment of brain regions. In this fMRI study, we tested predictions related to reserve and compensation in a large adult lifespan sample (N=154). We used multivariate Behaviour Partial Least Squares (B-PLS) analysis to examine how age, retrieval accuracy, and a proxy measure of reserve, impacted brain activity patterns during spatial and temporal context encoding and retrieval. Reserve modulated age-related compensatory brain responses in ventral visual, temporal, and fronto-parietal regions during memory encoding as a function of task demands. Activity in inferior parietal, medial temporal, and ventral visual regions were strongly impacted by age at encoding and retrieval, but were also related to individual differences in reserve. Our findings are consistent with the concepts of reserve and compensation and suggest that reserve may mitigate age-related decline by modulating compensatory brain responses in the aging brain.
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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 | 0.000 | 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".