Cognitive reserve is associated with less cognitive decline from white matter hyperintensities
Bibliographic record
Abstract
Previous research examining the contribution of white matter hyperintensities (WMHs) to cognitive decline has focused on overall lesion burden. A new approach, afforded by the Lesion Quantification Toolkit (LQT), measures localized connectivity disruption from WMHs to better estimate their impact on cognition. This methodology shifts the focus from lesion volume to the level of network disruption between brain regions. In this novel study, we applied the LQT approach to healthy aging and linked the degree of disconnection of gray matter by WMHs to both cognitive impairment and resilience via cognitive reserve. Using three pre-existing MRI datasets of older adults (total N = 259), we used the LQT to examine localized disruptions to brain connectivity due to WMHs. We then used partial least-squares path modeling to examine the relationships between this disruption, cognitive performance, age, and cognitive reserve. The results support a link between connectivity disruption and reduced cognitive performance. Results from all three individual datasets, one of which included a detailed measure of cognitive reserve, showed a link between cognitive reserve and higher cognitive performance, suggesting cognitive reserve allows for maintained cognitive function in spite of the negative impact of WMHs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".