Effects of Deep Brain Stimulation and Dopaminergic Medication on Excitatory and Inhibitory Spinal Pathways in Parkinson Disease
Bibliographic record
Abstract
PURPOSE: Abnormal activity within the corticospinal system is believed to contribute to the motor dysfunction associated with Parkinson disease. However, the effect of treatment for parkinsonian motor symptoms on dysfunctional descending input to the motor neuron pool remains unclear. METHODS: We recruited nine patients with PD treated with deep brain stimulation and examined the time course of interaction between a conditioning pulse from transcranial magnetic stimulation and the soleus H-reflex. Patients with Parkinson disease were examined under four treatment conditions and compared with 10 age-matched control subjects. RESULTS: In healthy controls, transcranial magnetic stimulation conditioning led to early inhibition of the H-reflex (76.2% ± 6.3%) at a condition-test interval of -2 ms. This early inhibition was absent when patients were OFF medication/OFF stimulation (132.5% ± 20.4%; P > 0.05) but was maximally restored toward control levels ON medication/ON stimulation (80.3% ± 7.0%). Of note, early inhibition ON medication/ON stimulation tended to be stronger than when medication (85.4% ± 5.9%) or deep brain stimulation (95.7% ± 9.4%) were applied separately. Late facilitation was observed in controls at condition-test intervals ≥5 ms but was significantly reduced (by 50% to 80% of controls) in Parkinson disease OFF stimulation at condition-test intervals ≥15 ms. The late facilitation was akin to control subjects when patients were ON stimulation. CONCLUSIONS: The present pilot study demonstrates that the recruitment of early inhibition and late facilitation is disrupted in untreated Parkinson disease and that medication and deep brain stimulation may act together to normalize supraspinal drive to the motor neuron pool.
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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".