Cerebral glucose metabolism and cognition in newly diagnosed Parkinson's disease: ICICLE-PD study
Bibliographic record
Abstract
OBJECTIVE: To assess reductions of cerebral glucose metabolism in Parkinson's disease (PD) with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), and their associations with cognitive decline. METHODS: FDG-PET was performed on a cohort of 79 patients with newly diagnosed PD (mean disease duration 8 months) and 20 unrelated controls. PD participants were scanned while on their usual dopaminergic medication. Cognitive testing was performed at baseline, and after 18 months using the Cognitive Drug Research (CDR) and Cambridge Neuropsychological Test Automated Battery (CANTAB) computerised batteries, the Mini-Mental State Examination (MMSE), and the Montreal Cognitive Assessment (MoCA). We used statistical parametric mapping (SPM V.12) software to compare groups and investigate voxelwise correlations between FDG metabolism and cognitive score at baseline. Linear regression was used to evaluate how levels of cortical FDG metabolism were predictive of subsequent cognitive decline rated with the MMSE and MoCA. RESULTS: PD participants showed reduced glucose metabolism in the occipital and inferior parietal lobes relative to controls. Low performance on memory-based tasks was associated with reduced FDG metabolism in posterior parietal and temporal regions, while attentional performance was associated with more frontal deficits. Baseline parietal to cerebellum FDG metabolism ratios predicted MMSE (β=0.38, p=0.001) and MoCA (β=0.3, p=0.002) at 18 months controlling for baseline score. CONCLUSIONS: Reductions in cortical FDG metabolism were present in newly diagnosed PD, and correlated with performance on neuropsychological tests. A reduced baseline parietal metabolism is associated with risk of cognitive decline and may represent a potential biomarker for this state and the development of PD dementia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".