White matter microstructural alterations and brain metabolism distributions in Parkinson’s disease
Bibliographic record
Abstract
This study aimed to use simultaneous 18 F-FDG PET/MRI based on Automated Fiber Quantification (AFQ) to determine whether there is a relationship between white matter microstructure changes and glucose metabolism distribution in PD. The study involved 38 subjects, including 23 parkinson's disease (PD) patients and 15 age and sex-matched healthy controls (HC). Primary clinical data and cognitive assessments were collected. All subjects underwent a simultaneous 18 F-FDG PET/MRI scan. AFQ was utilized to calculate tract-wise diffusion properties of 20 major white matter tracts. PD patients showed reduced mean Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores compared to HC subjects ( P < 0.05). PD patients showed higher mean diffusivity (MD) ( P = 0.047) and axial diffusivity (AD) ( P = 0.02) along the right corticospinal tract (CST) compared to HC. The microstructural change of CST was mainly located in the parietal part (node 67–100). Compared to HC, PD patients had FDG hypermetabolism in the right paracentral lobule ( P = 0.0204) and bilateral putamen (left: P = 0.0075; right: P = 0.0155) and hypometabolism in the right calcarine ( P = 0.0489). Hypermetabolism was found in the right paracentral lobule, which connects with the cortex of the right CST, and positively correlated with MD (r = 0.612, P < 0.001) and AD (r = 0.516, P < 0.001). We observed microstructural changes and glucose metabolism distribution characteristics in PD patients. These results may provide imaging evidence for studying the pathology of 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 | 0.000 |
| 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) | 0.000 | 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".