Graph theory analysis of the dopamine D2 receptor network in Parkinson’s disease patients with cognitive decline
Bibliographic record
Abstract
Abstract Cognitive decline in Parkinson's disease (PD) is a common sequela of the disorder that has a large impact on patient well‐being. Its physiological etiology, however, remains elusive. Our study used graph theory analysis to investigate the large‐scale topological patterns of the extrastriatal dopamine D2 receptor network. We used positron emission tomography with [ 11 C]FLB‐457 to measure the binding potential of cortical dopamine D2 receptors in two networks: the meso‐cortical dopamine network and the meso‐limbic dopamine network. We also investigated the application of partial volume effect correction (PVEC) in conjunction with graph theory analysis. Three groups were investigated in this study divided according to their cognitive status as measured by the Montreal Cognitive Assessment score, with a score ≤25 considered cognitively impaired: (a) healthy controls ( n = 13, 11 female), (b) cognitively unimpaired PD patients (PD‐CU, n = 13, 5 female), and (c) PD patients with mild cognitive impairment (PD‐MCI, n = 17, 4 female). In the meso‐cortical network, we observed increased small‐worldness, normalized clustering, and local efficiency in the PD‐CU group compared to the PD‐MCI group, as well as a hub shift in the PD‐MCI group. Compensatory reorganization of the meso‐cortical dopamine D2 receptor network may be responsible for some of the cognitive preservation observed in PD‐CU. These results were found without PVEC applied and PVEC proved detrimental to the graph theory analysis. Overall, our findings demonstrate how graph theory analysis can be used to detect subtle changes in the brain that would otherwise be missed by regional comparisons of receptor density.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".