Striatal dopaminergic correlates of the Montreal Cognitive Assessment test and the Mini Mental State Exam in Parkinson disease: A [11C]-DTBZ PET study
Bibliographic record
Abstract
2003 Objectives Cognitive impairment is common in Parkinson disease (PD), especially in more advanced stages of the disease. A common screening method for cognitive assessment is the Mini Mental State Exam (MMSE). Compared to the MMSE, the Montreal Cognitive Assessment test (MoCA) is supposedly more sensitive in detecting so-called Mild Cognitive Impairment (MCI). It is unclear, however, how both tests relate to striatal dopaminergic denervation, the pathophysiological hallmark of PD. Methods 26 PD patients (1F/25M; 67.0±7.4 (50-84) years; 6.1±3.2 (1-14) years motor disease duration; Hoehn and Yahr (HY HY HY H&Y 3: N=7) underwent striatal [11C]-DTBZ PET imaging for assessment of striatal dopaminergic nerve terminal integrity. All patients were simultaneously enrolled in one study that used the MMSE and another study that used the MoCA for cognitive assessment.. Striatal [11C]-DTBZ Distribution Volume Ratio (DVR) was calculated using the non-invasive Logan plot approach with the neocortex as the reference region. Results The average MMSE score was 28.5±1.9 (22-30) and the average MoCA score was 24.8±2.3 (19-29). Neither motor disease duration nor Hoehn and Yahr stage correlated significantly with either the MMSE score or MoCA score. Higher MoCA scores were associated with higher striatal [11C]-DTBZ DVR (ρ=0.410, p=0.037); MMSE did not correlate with striatal [11C]-DTBZ DVR (ρ=0.0, ns). Conclusions Decreased MoCA but not MMSE scores, are associated with decreased nigrostriatal nerve terminal integrity. Whereas it is possible that the MoCA may better reflect subcortical than cortical cognitive changes, it is also possible that the MoCA score may be affected more by motor impairment associated with striatal dopaminergic denervation while the MMSE is not. Research Support NIH P01 NS015655 & RO1 NS070856 and Department of Veterans Affair
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".