Cognitive deficits in mild Parkinson's disease are associated with distinct areas of grey matter atrophy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The neuroanatomical substrates underlying cognitive impairment in Parkinson's disease (PD) remain poorly understood. To address this gap, we compared the grey matter atrophy patterns in PD patients with mild cognitive impairment (PD-MCI) with PD patients having no cognitive impairment (PD-NCI), and examined relationships between atrophic regions and cognitive performance in specific domains. METHODS: 90 non-demented PD patients (64.95±7.54 years, Hoehn and Yahr=1.88±0.39) were classified using formal diagnostic criteria as PD-MCI (n=23) or PD-NCI (n=67). Grey matter volume differences were examined using voxel-based morphometry on structural MRI, and multivariate linear regressions were employed to assess the relationships between cognitive performance in specific domains and atrophic regions. RESULTS: Patients with PD-MCI had lower global cognition scores compared with PD-NCI (Mini Mental State Examination: 26.9 vs. 28.4, p=0.011; Montreal Cognitive Assessment: 24.5 vs. 27.0, p<0.001). The PD-MCI group demonstrated significantly poorer performance on executive function, attention, memory and language abilities. Patients with PD-MCI had reductions in grey matter volumes in the left insular, left superior frontal and left middle temporal areas compared to PD-NCI. Multiple regressions controlling for age, education and cardiovascular risk factors revealed significant positive correlations between left insular atrophy and executive-attention dysfunction. CONCLUSIONS: Domain specific cognitive impairment in mild PD is associated with distinct areas of grey matter atrophy. These regions of atrophy are demonstrable early in the disease course and may serve as a biomarker for dementia in 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 | 0.001 |
| 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".