Impact of White Matter Hyperintensity Changes on Cognition One Year After Mild Ischemic Stroke
Bibliographic record
Abstract
Abstract Background White matter hyperintensities (WMH) are a main MRI feature of cerebral small vessel disease, and are associated with increased risk of stroke and dementia. Progression can lead to cognitive decline, but some evidence suggests that WMH can also regress. Regression might relate to better clinical outcomes, but this is not well examined yet. We explored if net WMH volume change accounts for changes in cognition 1‐year after mild ischemic stroke. We also explore tissue signatures in areas of white matter change using brain diffusion imaging. Method In a prospective study, we assessed vascular risk factors (VRF), structural and diffusion‐tensor brain MRI and cognition (Montreal Cognitive Assessment; MoCA) at baseline (≤3 months post‐stroke) and at 1‐year. We assessed change in cognition over 1‐year using a linear mixed model. Predictors were age, sex, stroke severity (National Institutes of Health Stroke Scale; NIHSS), combined VRFs, disability (modified Rankin Score), occurrence of incidental infarct in‐between visits and WMH volume (%intracranial volume). We studied tissue areas of stable normal‐appearing white matter (NAWM), stable WMH, regressing and progressing WMH (Figure1) and measured diffusion‐tensor fractional anisotropy (FA) and mean diffusivity (MD), to assess microstructure in each area Result At baseline, mean age = 67.5 (SD = 11.1), 66% male (N = 229), mean MoCA score = 25.0 (SD = 3.5). Between baseline and 1‐year post‐stroke, MoCA score decreased with older age (standardized B[95%CI]: ‐0.271[‐0.383; ‐0.159]) and increasing NIHSS (‐0.193[‐0.287; ‐0.099]). Areas of NAWM that progress to WMH already have lower FA and higher MD at baseline than NAWM that stays NAWM (N = 191; Figure2). WMH that regress to NAWM have higher FA and lower MD than stable WMH. At 1‐year, MD values seem higher and FA values seem lower in progressing WMH than at baseline. Conclusion Cognition deteriorates 1 year post‐stroke with older age and higher stroke severity, but not with WMH changes, when considering net WMH volume. Areas of WMH changes exist within individuals, these areas show different underlying tissue structures before becoming apparent on conventional MRI, indicating that WMH regression is real. Separate areas of WMH progression and regression might be more sensitive to cognitive changes and potentially be intervention targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".