Cortical connectivity predicts cognition across time in Parkinson’s disease
Bibliographic record
Abstract
Abstract Cognitive symptoms are common in Parkinson’s disease (PD), yet the underlying brain mechanisms remain poorly understood. To address this gap, we investigated the relationship between functional connectivity and cognition at multiple time points using longitudinal functional MRI (fMRI) and cognitive assessments from the Parkinson’s Progression Marker Initiative (PPMI). We calculated resting-state functional connectivity within and between three key cortical brain networks that have been linked with cognitive function in PD: the frontoparietal network (FPN); the salience network (SAL); and the default mode network (DMN). Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA). Linear mixed-effects modeling revealed that decreased FPN-DMN functional connectivity is associated with lower MoCA scores over time. This finding suggests that cortical connectivity is associated with and may contribute to the progression of cognitive symptoms in PD. Our findings advance knowledge about cognitive changes in PD and emphasize the importance of functional brain network architecture. Highlights We studied cortical functional connectivity and cognition in PD over time. FPN-DMN functional connectivity robustly predicted cognition at multiple time points. These data provide insight into brain networks underlying cognitive progression 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 | 0.002 |
| 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".