Impact of White Matter Hyperintensities on domain‐specific cognition in Southeast Asians
Bibliographic record
Abstract
Abstract Background Dementia affects 55 million people worldwide, with 60% of the burden in Asia. White Matter Hyperintensities (WMH) is a key marker of small vessel disease, with high prevalence in Asian populations with prodromal and clinical dementia. WMH is described as: Deep White Matter Hyperintensities (DWMH), Periventricular Hyperintensities (PVH) or Fazekas‐Total (DWMH and PVH). However, association between WMH topography and performance in specific cognitive domains remains unexplored. Thus, this study aims to characterise the impact of Fazekas‐Total, DWMH and PVH on different cognitive domains. Method 304 participants (mean age 60.6, mean education years 14.2, mean Montreal Cognitive Assessment 25.99, 43.8% males) from Biomarkers and Cognition Study, Singapore (Dementia Research Centre (Singapore)) met the inclusion criteria. Eight domains of cognition were tested: global cognition, learning and memory, language, executive function, complex‐attention, perceptual‐motor, social‐cognition, and processing speed. Normality tests, correlation analysis and stepwise regression (with Benjamini and Hochberg False Discovery Rate correction) were performed to understand association between Fazekas‐Total, DWMH, PVH on domains of cognition tested, accounting for age, education, and gender. Two approaches were used for data analysis: (1) by grouping participants as Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD) and Cognitively Normal (CN) as per the National Institute on Aging‐Alzheimer’s Association (NIA‐AA) criteria and (2) by grouping participants’ WMH as confluent (DWMH ≥2 and PVH ≥3) and non‐confluent. Result Higher Fazekas‐Total was associated with slower processing speed (p = 0.0255, R = ‐0.039) in prodromal participants (MCI and SCD). Higher PVH was associated with slower processing speed in MCI (p = 0.017, R = ‐0.32) and CN (p = 0.017, R = ‐0.663) participants. Higher PVH was significantly associated with poorer learning and memory (p = 0.045, R = ‐0.043) in SCD participants. Higher DWMH was significantly associated with poorer learning and memory (p = 0.017, R = ‐0.765) in CN participants. Conclusion These results demonstrate differential association between PVH, DWMH and cognitive domains, thus the location of WMH determines the affected cognitive domains. Therefore, it is worthwhile to assess PVH and DWMH in clinical cognitive outcomes separately and to understand the upstream pathobiology of DWMH and PVH. Furthermore, higher Fazekas‐Total was strongly associated with slower processing speed in prodromal participants and could be a marker of cognitive decline.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".