White Matter Structural Network Dysfunction Mediates the Effect of Hypertension on Cognitive Decline in Parkinson Disease
Bibliographic record
Abstract
OBJECTIVES: Hypertension (HBP) is a risk factor for the development of motor and cognitive functions in Parkinson disease (PD) patients, but the specific mechanism is unclear. This study investigated white matter structural network abnormalities and their mediation effect of cognitive decline in patients with PD and HBP. METHODS: PD patients with HBP and normal blood pressure (HBP and non-HBP) underwent conventional and multi-shell diffusion magnetic resonance imaging (MRI) at baseline. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) at baseline and 12-month follow-up. Enlarged perivascular spaces (EPVS) in the basal ganglia and midbrain were assessed at baseline. White matter structural network based on the diffusion MRI was constructed. The mediation effect and correlations of EPVS and the network metrics with cognitive function were analyzed. RESULTS: At 12-month follow-up, the cognitive decline was found in the HBP group. Global connectivity was impaired in the HBP group. The number and maximum diameter of EPVS were higher in the HBP group. The nodal connectivity was impaired in the HBP group and associated with the cognitive function at baseline and follow-up. Both global and nodal network metrics, as well as the counts of EPVS mediated the effect of HBP on the cognitive decline. CONCLUSIONS: The PD patients with HBP had worse cognitive function. Hypertensive impairment of white matter connectivity may be the underlying mechanism of cognitive decline in PD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".