Changes in cortical excitability with thalamic deep brain stimulation
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is an effective treatment for several movement disorders. However, its mechanism of action is largely unknown. Both lesioning and DBS of the ventralis intermedius (VIM) nucleus of thalamus improve essential tremor. Although DBS was initially thought to inhibit the target neurons, recent studies suggest that DBS activates neurons. OBJECTIVE: To test the hypothesis that thalamic DBS activates the target area in patients with essential tremor. METHODS: Cortical excitability was assessed in seven unmedicated patients with essential tremor using unilateral stimulators implanted in the VIM of the dominant hemisphere and in 11 healthy controls using transcranial magnetic stimulation (TMS). Patients were studied during optimal DBS (ON condition), half the optimal frequency (HALF), and with DBS off (OFF) in random order. Tremor was assessed after a change in DBS setting. Electromyography was recorded from the dominant hand, and TMS was applied over the contralateral motor cortex using single and paired pulses to elicit motor evoked potentials (MEPs). MEP recruitment was determined using stimulus intensities from 100% to 150% of motor threshold. RESULTS: Tremor scores were significantly improved with DBS ON. Analysis of variance showed a significant interaction between condition (ON, HALF, OFF, Normal) and stimulus intensity on MEP amplitude. During DBS ON MEP amplitudes were significantly higher compared with controls at high but not at low TMS intensities. CONCLUSION: Because the ventralis intermedius (VIM) projects directly to the motor cortex, the high motor evoked potential amplitude with deep brain stimulation ON suggests that VIM DBS activates rather than inhibits the target area.
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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".