Voluntary tic suppression and the normalization of motor cortical beta power in Gilles de la Tourette syndrome: an <scp>EEG</scp> study
Bibliographic record
Abstract
Gilles de la Tourette syndrome (GTS) is a neurological condition characterized by motor and vocal tics. Previous studies suggested that this syndrome is associated with abnormal sensorimotor cortex activity at rest, as well as during the execution of voluntary movements. It has been hypothesized that this abnormality might be interpreted as a form of increased tonic inhibition, probably to suppress tics; however, this hypothesis has not been tested so far. The present study was designed to formally test how voluntary tic suppression in GTS influences the activity of the sensorimotor cortex during the execution of a motor task. We used EEG to record neural activity over the contralateral sensorimotor cortex during a finger movement task in adult GTS patients, in both free ticcing and tic suppression conditions; these data were then compared with those collected during the same task in age-matched healthy subjects. We focused on the levels of activity in the beta frequency band, which is typically associated with the activation of the motor system, during three different phases: a pre-movement, a movement, and a post-movement phase. GTS patients showed decreased levels of beta modulation with respect to the healthy controls, during the execution of the task; however, this abnormal pattern returned to be normal when they were explicitly asked to suppress their tics while moving. This is the first demonstration that voluntary tic suppression in GTS operates through the normalization of the EEG rhythm in the beta frequency range during the execution of a voluntary finger movement.
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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 | 0.001 | 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".