Social Networks and Cognitive Reserve: Network Structure Moderates the Association Between Amygdalar Volume and Cognitive Outcomes
Bibliographic record
Abstract
OBJECTIVES: The cognitive reserve hypothesis has been proposed as a key mechanism explaining the link between social networks and cognitive function but has rarely been empirically tested using neuroimaging data. This study examines whether social network attributes moderate the association between amygdalar volume and cognitive function. METHODS: Data were from the Social Networks in Alzheimer Disease study (N = 154) and Indiana Alzheimer's Disease Research Center. Social networks were measured using the PhenX Social Network Battery. Regional data from magnetic resonance imaging (amygdalar volume [AV]) were analyzed using FreeSurfer software. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) and consensus diagnosis. Linear regression analyses were conducted to test the moderating role of social networks on the association between AV and cognitive function. RESULTS: Participants with greater ability to span multiple social roles and subgroups within their networks scored higher on the MoCA after adjusting for sociodemographic variables, depression, frequency of contact, and AV. Social networks moderated the association between AV and cognitive function. DISCUSSION: Among participants who engaged in diverse and loosely connected social networks, the expected adverse cognitive effects of brain volume in regions implicated in socioemotional processing were attenuated. These findings suggest that cognitive stimulation achieved through social interaction with a diverse array of social relationships across multiple contexts may help promote cognitive reserve.
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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 | 0.003 |
| 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 | 0.001 |
| 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".