Altered cerebral perfusion and microstructure in advanced Parkinson’s disease and their associations with clinical features
Bibliographic record
Abstract
OBJECTIVE: To explore the whole cerebral perfusion and microstructure alteration patterns in Parkinson's disease (PD) and the associations of these patterns with clinical features. METHODS: Forty-one subjects [20 PD patients and 21 healthy controls (HCs)] underwent arterial spin labeling (ASL), diffusion tensor imaging (DTI) and 3D T1-weighted imaging (T1WI) MRI. The cerebral blood flow (CBF) of the whole brain and the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of subcortical and cerebellar regions were measured and compared between groups. Pearson's correlation was calculated between MRI measurements and clinical features [Unified Parkinson's Disease Rating Scale (UPDRS), UPDRS III, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and olfactory test scores]. RESULTS: Compared to HCs, PD patients showed lower CBF in the frontal, parietal and temporal lobes but higher CBF in bilateral hippocampi, red nuclei, right substantia nigra, thalamus and most cerebellar regions. The MD in the right thalamus and several regions in the cerebellum increased in PD compared to HCs. In PD patients, the total UPDRS, UPDRS III, MoCA, MMSE and olfactory test scores were related to FA or CBF in cerebellum. (all p < 0.05). CONCLUSION: Hypoperfusion in cortical regions, together with hyperperfusion in subcortical and cerebellar regions may be the characteristic perfusion pattern in advanced PD patients. The microstructures of the right thalamus and cerebellum were changed in PD patients. The cognitive, motor and olfactory performance of PD patients is closely related to the perfusion and microstructure of the brain, especially the cerebellum.
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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) | 0.001 | 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".