Structural network topology mediated cognitive impairment in Parkinson’s disease
Bibliographic record
Abstract
Abstract Cognitive impairment (CI) is one of the most prominent non-motor symptoms in Parkinson’s disease (PD). How brain network abnormalities contribute to CI in PD patients remain largely unclear. The goal of this study is to explore whether aberrations of brain network topology were causally associated with cognitive decline in PD patients. PD patients receiving magnetic resonance imaging from Parkinson’s Progression Markers Initiative (PPMI) database were specifically selected. According to the scores of Montreal Cognitive Assessment (MoCA), PD patients were classified into CI+ group (MoCA score ≤ 25) and CI-group (MoCA score > 25) to investigate whether clinical features and brain networks were significantly different between two groups. Mediation analysis was utilized to evaluate whether brain network alterations contributed to CI in PD patients. We revealed CI + group exhibited more severe non-motor symptoms compared to CI-group. In addition, age, excessive daytime sleepiness, and depressive symptoms were found to be significantly associated with CI of PD patients. Moreover, CI+ group exhibited statistically different local topological properties in structural network compared to CI-group. Furthermore, differential local topological metrics in structural network meditated the effects of age, excessive daytime sleepiness, and depression on cognitive decline of PD patients. Taken together, out study suggested that PD patients with CI exhibited notable disturbances of structural network topology, which mediated negative associations between of age, excessive daytime sleepiness, depression and cognitive decline of PD patients.
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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 | 0.001 |
| 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".