Resting-State Cerebello-Cortical Dysfunction in Parkinson's Disease
Bibliographic record
Abstract
Purpose: Recently, the cerebellum's role in Parkinson's disease (PD) has been highlighted. Therefore, this study sought to test the hypothesis that functional connectivity (FC) between cerebellar and cortical nodes of the resting-state networks differentiates PD patients from controls by scanning participants at rest using functional magnetic resonance imaging (fMRI) and investigating connectivity of the cerebellar nodes of the resting-state networks. Materials and Methods: Sixty-two PD participants off medication for at least 12 h and 33 normal controls (NCs) were scanned at rest using blood oxygenation level-dependent fMRI scans. Motor and cognitive functions were assessed with the Movement Disorder Society's Revision of the Unified Parkinson's Disease Rating Scale III and Montreal Cognitive Assessment, respectively. Connectivity was investigated with cerebellar seeds defined by Buckner's 7-network atlas. Results: PD participants had significant differences in FC when compared to NC participants. Most notably, PD patients had higher FC between cerebellar nodes of the somatomotor network (SMN) and the corresponding cortical nodes. Cognitive functioning was differentially associated with connectivity of the cerebellar SMN and dorsal attention network. Further, cerebellar connectivity of frontoparietal and default mode networks correlated with the severity of motor function. Conclusion: Our study demonstrates altered cerebello-cortical FC in PD, as well as an association of this FC with PD-related motor and cognitive disruptions, thus providing additional evidence for the cerebellum's role 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 | 0.001 |
| 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) | 0.001 | 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".