White matter hyperintensities are an early marker for cognitive decline in cognitively healthy older adults
Bibliographic record
Abstract
Abstract Background Previous research suggests that white matter hyperintensities, amyloid, and tau contribute to cognitive decline. It remains unknown as to how these factors relate to one another and how they jointly contribute to cognitive decline in normal aging. The goal of this study was to examine the association between these pathologies and their relationship to cognitive decline. Methods Cognitively normal older adult data from the Alzheimer’s Disease Neuroimaging Initiative were examined. Participants were included if they had no subjective cognitive decline, had baseline white matter hyperintensity measurements, CSF Aß42, CSF pTau181, and cognitive scores. WMHs were segmented using a previously validated automatic technique (Dadar et al., 2018). Of the 230 participants included, only 199 had follow‐up cognitive scores. Linear regressions examined the influence of white matter hyperintensities, amyloid, and tau on baseline and follow‐up cognitive scores. Linear regressions also examined the association of amyloid and tau on white matter hyperintensities and between tau and amyloid. Results Increased baseline WMHs were associated with increased baseline ADAS‐13 scores (t=2.59, p=.01) and lower follow‐up executive functioning (t= ‐2.84, p=.005). Lower baseline Aß42 was associated with lower baseline (t=3.58, p<.004) but not follow‐up executive function. Baseline pTau was not associated with decline in cognition at baseline or follow‐up. At baseline, WMHs were not associated with pTau but were inversely related to lower baseline Aß42 (t=‐4.20, p<.001). Aß42 and pTau were not associated (t=0.51, p=.61). Conclusion White matter hyperintensities may be one of the earliest pathologies observed in healthy older adults that contribute to cognitive decline. The inclusion of white matter hyperintensities as an additional marker for early cognitive decline may improve our current understanding of age‐related changes in cognitively healthy older adults.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".