Physiological effects of subthalamic nucleus deep brain stimulation surgery in cervical dystonia
Bibliographic record
Abstract
BACKGROUND: Subthalamic nucleus deep brain stimulation (STN DBS) surgery is clinically effective for treatment of cervical dystonia; however, the underlying physiology has not been examined. We used transcranial magnetic stimulation (TMS) to examine the effects of STN DBS on sensorimotor integration, sensorimotor plasticity and motor cortex excitability, which are identified as the key pathophysiological features underlying dystonia. METHODS: TMS paradigms of short latency afferent inhibition (SAI) and long latency afferent inhibition (LAI) were used to examine the sensorimotor integration. Sensorimotor plasticity was measured with paired associative stimulation paradigm, and motor cortex excitability was examined with short interval intracortical inhibition and intracortical facilitation. DBS was turned off and on to record these measures. RESULTS: STN DBS modulated SAI and LAI, which correlated well with the acute clinical improvement. While there were no changes seen in the motor cortex excitability, DBS was found to normalise the sensorimotor plasticity; however, there was no clinical correlation. CONCLUSION: Modulation of sensorimotor integration is a key contributor to clinical improvement with acute stimulation of STN. Since the motor cortex excitability did not change and the change in sensorimotor plasticity did not correlate with clinical improvement, STN DBS demonstrates restricted effects on the underlying physiology. CLINICAL TRIAL REGISTRATION: NCT01671527.
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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 | 0.000 |
| 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) | 0.002 | 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".