Arterial spin labelling reveals prolonged arterial arrival time in idiopathic Parkinson's disease
Bibliographic record
Abstract
Idiopathic Parkinson's disease (IPD) is the second most common neurodegenerative disease, yet effective disease modifying treatments are still lacking. Neurodegeneration involves multiple interacting pathological pathways. The extent to which neurovascular mechanisms are involved is not well defined in IPD. We aimed to determine whether novel magnetic resonance imaging (MRI) techniques, including arterial spin labelling (ASL) quantification of cerebral perfusion, can reveal altered neurovascular status (NVS) in IPD. Fourteen participants with IPD (mean ± SD age 65.1 ± 5.9 years) and 14 age and cardiovascular risk factor matched control participants (mean ± SD age 64.6 ± 4.2 years) underwent a 3T MRI scan protocol. ASL images were collected before, during and after a 6 minute hypercapnic challenge. FLAIR images were used to determine white matter lesion score. Quantitative images of cerebral blood flow (CBF) and arterial arrival time (AAT) were calculated from the ASL data both at rest and during hypercapnia. Cerebrovascular reactivity (CVR) images were calculated, depicting the change in CBF and AAT relative to the change in end-tidal CO2. A significant (p = 0.005) increase in whole brain averaged baseline AAT was observed in IPD participants (mean ± SD age 1532 ± 138 ms) compared to controls (mean ± SD age 1335 ± 165 ms). Voxel-wise analysis revealed this to be widespread across the brain. However, there were no statistically significant differences in white matter lesion score, CBF, or CVR between patients and controls. Regional CBF, but not AAT, in the IPD group was found to correlate positively with Montreal cognitive assessment (MoCA) scores. These findings provide further evidence of alterations in NVS in IPD.
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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 | 0.000 | 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".