White Matter Hyperintensities Related to Parkinson's Disease Executive Function
Bibliographic record
Abstract
Abstract Background People with Parkinson's disease (PD) can develop multidomain cognitive impairments; however, it is unclear whether different pathologies underlie domain‐specific cognitive dysfunction. Objectives We investigated the contribution of vascular copathology severity and location, as measured by MRI white matter hyperintensities (WMHs), to domain‐specific cognitive impairment in PD. Methods We studied 85 PD (66.6 ± 9.2 years) and 18 control (65.9 ± 6.6) participants. Using the Fazekas scale for rating the severity of WMH, we subdivided PD into 14 PD – WMH + and 71 PD – WMH – . Participants underwent global, executive, visuospatial, episodic memory, and language testing. We performed nonparametric permutation testing to create WMH probability maps based on PD‐WMH group and cognitive test performance. Results The PD – WMH + group showed worse global and executive cognitive performance than the PD – WMH – group. On individual tests, the PD – WMH + group showed worse Montreal Cognitive Assessment (MoCA), Stroop, Symbol Digit Modalities Test (SDMT), and Digit Span scores. WMH probability maps showed that in the PD – WMH + group, worse Stroop was associated with lesions centered around the corticospinal tract (CST), forceps major, inferior‐fronto‐occipital fasciculus, and superior longitudinal fasciculus; worse SDMT with lesions around the CST, forceps major, and posterior corona radiata; worse Digit Span with lesions around the posterior corona radiata; and worse MoCA with lesions around the CST. Conclusions We found that WMH severity was associated with PD executive dysfunction, including worse attention, working memory, and processing speed. Disruption of key white matter tracts in proximity to vascular lesions could contribute to these specific cognitive impairments. Early treatment of vascular disease might mitigate some executive dysfunction in a subset of patients with PD.
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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 | 0.000 |
| 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) | 0.002 | 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".