Association of Aging and Structure Connectome With Cognitive Performance: A Systematic Review
Bibliographic record
Abstract
Objectives: To systematically review evidence on the association between aging-related alterations in the structural connectome, particularly white matter microstructure, and cognitive performance. Design: Systematic review. Participants: Older adults without clinically diagnosed neurological disorders from 24 eligible studies. Outcome Measures: Diffusion tensor imaging (DTI)-derived indices of white matter microstructure, including fractional anisotropy and mean diffusivity, and cognitive outcomes encompassing working memory, attention, processing speed, and executive function. Results: The majority of included studies demonstrated that advancing age was associated with reduced white matter integrity. These alterations were consistently linked to poorer performance across multiple cognitive domains, particularly processing speed, executive function, and working memory. Multimodal studies integrating DTI with functional MRI further suggested that aging weakens structural–functional coupling and reduces network efficiency, which may contribute to less effective cognitive and spatial processing. However, substantial heterogeneity in sample characteristics, imaging protocols, and analytic approaches limited direct comparison across studies. Conclusions: The current evidence supports a robust association between age-related white matter microstructural changes and cognitive decline. These alterations may represent early markers of cognitive vulnerability and could help identify individuals at increased risk for future cognitive impairment or dementia. Larger longitudinal studies with standardized acquisition and analytic methods are needed to clarify the predictive value of these markers and strengthen causal inference.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".