Functional Imaging of the Cerebellum and Basal Ganglia During Predictive Motor Timing in Early Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The basal ganglia and the cerebellum have both emerged as important structures involved in the processing of temporal information. METHODS: We examined the roles of the cerebellum and striatum in predictive motor timing during a target interception task in healthy individuals (HC group; n = 21) and in patients with early Parkinson's disease (early stage PD group; n = 20) using functional magnetic resonance imaging. RESULTS: Despite having similar hit ratios, the PD failed more often than the HC to postpone their actions until the right moment and to adapt their behavior from one trial to the next. We found more activation in the right cerebellar lobule VI in HC than in early stage PD during successful trials. Successful trial-by-trial adjustments were associated with higher activity in the right putamen and lobule VI of the cerebellum in HC. CONCLUSIONS: We conclude that both the cerebellum and striatum are involved in predictive motor timing tasks. The cerebellar activity is associated exclusively with the postponement of action until the right moment, whereas both the cerebellum and striatum are needed for successful adaptation of motor actions from one trial to the next. We found a general ''hypoactivation'' of basal ganglia and cerebellum in early stage PD relative to HC, indicating that even in early stages of the PD there could be functional perturbations in the motor system beyond striatum.
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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".