Disruption in cerebellar and basal ganglia networks during a visuospatial task in cervical dystonia
Bibliographic record
Abstract
BACKGROUND: Although dystonia is traditionally conceptualized as a basal ganglia disorder, increasing interest has been directed at a different neural network node, the cerebellum, which may play a significant role in the pathophysiology of dystonia. Abnormal sensorimotor processing and disturbed motor schemes, possibly attributable to cerebellar changes, remain unclear. METHODS: We sought to characterize the extent of cerebellar dysfunction within the motor network using functional MRI activation analysis, connectivity analysis, and voxel-based morphometry in cervical dystonia patients (n = 25, 15 women, mean age 45.8 years) and healthy volunteers (n = 25, 15 women, mean age 44.7 years) in a visuospatial task requiring predictive motor timing. RESULTS: Cervical dystonia patients showed decreased activation in the posterior cerebellar lobules as well as in the premotor areas, the associative parietal cortex, and visual regions. Patients also had decreased cerebellar connectivity with bilateral basal ganglia structures and the dorsolateral prefrontal cortex. CONCLUSIONS: This promotes the view that dystonia results from miscommunication between the basal ganglia and cerebellar loops, thus providing new insights into the brain regions essential for the development of cervical dystonia. © 2017 International Parkinson and Movement Disorder Society.
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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".