Dynamic functional connectivity changes associated with dementia in Parkinson’s disease
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Bibliographic record
Abstract
Dynamic functional connectivity captures temporal variations of functional connectivity during MRI acquisition and it may be a suitable method to detect cognitive changes in Parkinson's disease. In this study, we evaluated 118 patients with Parkinson's disease matched for age, sex and education with 35 healthy control subjects. Patients with Parkinson's disease were classified with normal cognition (n = 52), mild cognitive impairment (n = 46), and dementia (n = 20) based on an extensive neuropsychological evaluation. Resting state functional MRI and a sliding-window approach were used to study the dynamic functional connectivity. Dynamic analysis suggested two distinct connectivity 'States' across the entire group: a more frequent, segregated brain state characterized by the predominance of within-network connections, State I, and a less frequent, integrated state with strongly connected functional internetwork components, State II. In Parkinson's disease, State I occurred 13.89% more often than in healthy control subjects, paralleled by a proportional reduction of State II. Parkinson's disease subgroups analyses showed the segregated state occurred more frequently in Parkinson's disease dementia than in mild cognitive impairment and normal cognition groups. Further, patients with Parkinson's disease dementia dwelled significantly longer in the segregated State I, and showed a significant lower number of transitions to the strongly interconnected State II compared to the other subgroups. Our study indicates that dementia in Parkinson's disease is characterized by altered temporal properties in dynamic connectivity. In addition, our results show that increased dwell time in the segregated state and reduced number of transitions between states are associated with presence of dementia in Parkinson's disease. Further studies on dynamic functional connectivity changes could help to better understand the progressive dysfunction of networks between Parkinson's disease cognitive states.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it