Lower volume, more impairment: reduced cholinergic basal forebrain grey matter density is associated with impaired cognition in Parkinson disease
Bibliographic record
Abstract
OBJECTIVE: A major contributor to dementia in Parkinson disease (PD) is degeneration of the cholinergic basal forebrain. This study determined whether cholinergic nucleus 4 (Ch4) density is associated with cognition in early and more advanced PD. METHODS: We analysed brain MRIs and neuropsychological test scores for 228 newly diagnosed PD participants from the Parkinson's Progression Markers Initiative (PPMI), 101 healthy controls from the PPMI and 125 more advanced PD patients from a local retrospective cohort. Cholinergic basal forebrain nuclei densities were determined by applying probabilistic maps to MPRAGE T1 sequences processed using voxel-based morphometry methods. Relationships between grey matter densities and cognitive scores were analysed using correlations and linear regression models. RESULTS: In more advanced PD, greater Ch4 density was associated with Montreal Cognitive Assessment (MoCA) score (β=14.2; 95% CI=1.5 to 27.0; p=0.03), attention domain z-score (β=3.2; 95% CI=0.8 to 5.5; p=0.008) and visuospatial domain z-score (β=7.9; 95% CI=2.0 to 13.8; p=0.009). In the PPMI PD cohort, higher Ch4 was associated with higher scores on MoCA (β=9.2; 95% CI=1.9 to 16.5; p=0.01), Judgement of Line Orientation (β=20.4; 95% CI=13.8 to 27.0; p<0.001), Letter Number Sequencing (β=16.5; 95% CI=9.5 to 23.4; p<0.001) and Symbol Digit Modalities Test (β=41.8; 95% CI=18.7 to 65.0; p<0.001). These same relationships were observed in 97 PPMI PD participants at 4 years. There were no significant associations between Ch4 density and cognitive outcomes in healthy controls. CONCLUSION: In de novo and more advanced PD, lower Ch4 density is associated with impaired global cognition, attention and visuospatial function.
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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) | 0.002 | 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".