White Matter Lesions in Mild Cognitive Impairment and Idiopathic Parkinson's Disease: Multimodal Advanced MRI and Cognitive Associations
Bibliographic record
Abstract
Cerebrovascular disease is a common comorbidity in older adults, typically assessed in terms of white matter hyperintensities (WMHs) on MRI. While it is well known that WMHs exacerbate cognitive symptoms, the exact relation of WMHs with cognitive performance and other degenerative diseases is unknown. Furthermore, based on location, WMHs are often classified into periventricular and deep WMHs and are believed to have different pathological origins. Whether the two types of WMHs influence cognition differently is unclear. Using regression models, we assessed the independent association of these two types of WMHs with cognitive performance in two separate studies focused on distinct degenerative diseases, early Alzheimer's (mild cognitive impairment), and Parkinson's disease. We further tested if the two types of WMHs were differentially associated with reduced cortical cerebral blood flow (CBF) as measured by arterial spin labeling and increased mean diffusivity (MD, a marker of tissue injury) as measured by diffusion imaging. Our approach revealed that both deep and periventricular WMHs were associated with poor performance on tests of global cognition (Montreal cognitive Assessment, MoCA), task processing (Trail making test), and category fluency in the study of mild cognitive impairment. They were associated with poor performance in global cognition (MoCA) and category fluency in the Parkinson's disease study. Of note, more associations were detected between cognitive performance and deep WMHs than between cognitive performance and periventricular WMHs. Mechanistically, both deep and periventricular WMHs were associated with increased MD. Both deep and periventricular WMHs were also associated with reduced CBF in the gray matter.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".