Relationships between age-associated changes in context memory and cortical thickness across the adult lifespan
Bibliographic record
Abstract
Episodic memory is the ability to remember an item or event in rich contextual detail. Healthy aging is associated with declines in this ability in addition to widespread cortical thinning. These parallel declines suggest that age-related changes in cortical thickness may contribute to episodic memory decline with age. As few studies have directly examined this association, the current study aims to cross-sectionally explore whether regional cortical thickness (CT) mediates the relationship between age and episodic memory as measured by a context memory task for faces. The brain regions examined in the current study have been previously associated with episodic memory performance and demonstrate age-associated cortical thinning in the current healthy lifespan sample (N= 114). The regions on the left were lingual, fusiform, rectus, parahippocampal, superior frontal, caudal middle frontal, inferior frontal, angular, and supramarginal gyri; and the regions on the right were parahippocampal, rectus, angular, supramarginal, superior temporal, middle temporal, superior frontal, caudal middle frontal, and inferior frontal gyri. Conditional mediation models were tested using bootstrapping in order to determine whether and how these regions mediate age-associated changes in performance on the context memory task. It was found that CT of the right parahippocampal and superior frontal gyri were related to performance differentially with age, lending support to the conception that grey matter-episodic memory relationships change with age. The models also demonstrated that regional CT mediated age-associated variance in context memory performance in a way that is conditional upon age. Furthermore, the current analysis identified a dissociation between CT of a region mediating age-associated variance in accuracy, and predicting accuracy after controlling for age. Overall, these findings underscore the importance of implementing a longitudinal approach because the examination of intra-individual changes over time would better tease out how age-related changes in CT affect context memory, without the need to account for CT-episodic memory relationships due to factors other than age.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".