Long-term subthalamic nucleus stimulation improves sensorimotor integration and proprioception
Bibliographic record
Abstract
OBJECTIVE: Sensorimotor integration is impaired in patients with Parkinson's disease (PD). Short latency afferent inhibition (SAI) and long latency afferent inhibition (LAI) measured with transcranial magnetic stimulation (TMS) can be used to measure sensorimotor integration. Subthalamic nucleus (STN) deep brain stimulation (DBS) has been found to restore these abnormalities, but the time course of these changes is not known. We prospectively evaluated the short-term and long-term effects of STN DBS on SAI, LAI and proprioception. We hypothesised plasticity changes induced by chronic stimulation are necessary to normalise sensorimotor integration and proprioception. METHODS: Patients with PD were studied preoperatively, at 1 month and more than 6 months postoperatively. SAI was tested with median nerve stimulation to the wrist preceding TMS pulse to motor cortex by ~20 ms and LAI by 200 ms. Proprioception (distance and spatial errors) in the arm was quantitatively assessed. For postoperative assessments, patients were studied in the medication-off/stimulator-off, medication-off/stimulator-on, medication-on/stimulator-off and medication-on/stimulator-on conditions. RESULTS: 11 patients with PD and 10 controls were enrolled. Preoperatively, SAI and proprioception was abnormal during the medication-on conditions and LAI was reduced regardless of the medication status. STN DBS had no significant effect on SAI, LAI and proprioception at 1 month. However, at 6 months SAI, LAI and distance errors were normalised in the medication-on/stimulator-on condition. Spatial error was normalised with DBS on and off. CONCLUSIONS: Chronic STN DBS in PD normalises sensorimotor integration and proprioception, likely through long-term plastic changes in the basal ganglia thalamocortical circuit.
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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) | 0.001 | 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".