Patterns of white matter hyperintensities associated with cognition in middle-aged cognitively healthy individuals
Bibliographic record
Abstract
White matter hyperintensities (WMH) are commonly detected in the brain of elderly individuals and have been associated with a negative impact on multiple cognitive domains. We aim to investigate the impact of global and regional distribution of WMH on episodic memory and executive function in middle-aged cognitively unimpaired participants [N = 561 (45-75 years)] enriched for Alzheimer's disease risk factors. WMH were automatically segmented from FLAIR, T1 and FSE MR images. WMH load was calculated both globally and regionally. At each cerebral lobe, regional WMH load was measured at four equidistant layers extending from the lateral ventricles to juxtacortical areas. Cognition was measured by The Memory Binding Test (MBT) and WAIS-IV subtests. Global composite z-scores were calculated for the two cognitive domains. Association between global and regional WMH measurements were sought against cognitive measures, both in global composite scores and in individual subtests. We adjusted cognition and WMH burden for the main sociodemographic (age, sex and education) and genetic factors (APOE-ε4). Memory and executive function were significantly associated with global WMH load. Regionally, lower executive performance was mainly associated with higher deep WMH load in frontal areas and, to a lower degree, in occipital, parietal and temporal regions. Lower episodic memory performance was correlated with higher WMH burden in deep frontal and occipital areas. Our novel methodological approach of regional analysis allowed us to reveal the association between cognition and WMH in strategic brain locations. Our results suggest that, even a small WMH load can impact cognition in cognitively unimpaired middle-aged subjects.
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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 | 0.001 | 0.001 |
| 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".