First Application of a Novel Brain Template: Motor Training Improves Cortico-cerebellar Connectivity in Cerebellar Ataxia
Bibliographic record
Abstract
People with cerebellar degeneration show characteristic ataxic motor impairments. Despite cerebellar dysfunction, they can improve motor performance through sensorimotor training. Yet, how such training changes functional brain networks affected by cerebellar degeneration is unknown. We here investigated neuroplastic changes in the cortico-cerebellar network after a 5-d forearm training in 40 patients with mild to severe cerebellar degeneration and 40 age- and sex-matched healthy controls. Human participants (41 female) were assigned to one of four motor training conditions, varying online visual feedback and explicit verbal feedback. Anatomical and resting-state fMRI was collected on days before and after training. To overcome the limitations of standard brain templates that fail when encountering severe anatomical abnormalities, we developed a specific template for comparing cerebellar patients with controls. Our new template reduced the spatial spread of cerebellar anatomical landmarks by 30% and tripled fMRI noise classification accuracy. Using this pipeline, we found that patients showed impaired connectivity between cerebellar motor regions and neocortical visuomotor and premotor regions at baseline compared with controls, whereas their corticocortical connectivity remained intact. Training with vision strengthened connectivity in the cortico-cerebellar visuomotor network contralateral to the trained arm in all participants. Cerebellar patients exhibited additional increased connectivity ipsilateral to the training arm in this network. Further, training with explicit verbal feedback affected connectivity between a cerebellar cognitive region and dorsolateral prefrontal cortex, although post hoc tests did not reach significance. These results document enhanced cortico-cerebellar connectivity as a neurophysiological response to visuomotor training in people with cerebellar degeneration.
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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".