Common and unique connectivity at the interface of motor, neuropsychiatric, and cognitive symptoms in Parkinson's disease: A commonality analysis
Bibliographic record
Abstract
Parkinson's disease (PD) is characterized by overlapping motor, neuropsychiatric, and cognitive symptoms. Worse performance in one domain is associated with worse performance in the other domains. Commonality analysis (CA) is a method of variance partitioning in multiple regression, used to separate the specific and common influence of collinear predictors. We apply, for the first time, CA to the functional connectome to investigate the unique and common neural connectivity underlying the interface of the symptom domains in 74 non-demented PD subjects. Edges were modeled as a function of global motor, cognitive, and neuropsychiatric scores. CA was performed, yielding measures of the unique and common contribution of the symptom domains. Bootstrap confidence intervals were used to determine the precision of the estimates and to directly compare each commonality coefficient. The overall model identified a network with the caudate nucleus as a hub. Neuropsychiatric impairment accounted for connectivity in the caudate-dorsal anterior cingulate and caudate-right dorsolateral prefrontal-right inferior parietal circuits, while caudate-medial prefrontal connectivity reflected a unique effect of both neuropsychiatric and cognitive impairment. Caudate-precuneus connectivity was explained by both unique and shared influence of neuropsychiatric and cognitive symptoms. Lastly, posterior cortical connectivity reflected an interplay of the unique and common effects of each symptom domain. We show that CA can determine the amount of variance in the connectome that is unique and shared amongst motor, neuropsychiatric, and cognitive symptoms in PD, thereby improving our ability to interpret the data while gaining novel insight into networks at the interface of these symptom domains.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".