Response of Human Thalamic Neurons to High-Frequency Stimulation
Bibliographic record
Abstract
Thalamic deep brain stimulation (DBS) is an effective treatment for tremor, but the mechanisms of action remain unclear. Previous studies of human thalamic neurons to noted transient rebound bursting activity followed by prolonged inhibition after cessation of high frequency extracellular stimulation, and the present study sought to identify the mechanisms underlying this response. Recordings from 13 thalamic neurons exhibiting low threshold spike (LTS) bursting to brief periods of extracellular stimulation were made during surgeries to implant DBS leads in 6 subjects with Parkinson's disease. The response immediately after cessation of stimulation included a short epoch of burst activity, followed by a prolonged period of silence before a return to LTS bursting. A computational model of a population of thalamocortical relay neurons and presynaptic axons terminating on the neurons was used to identify cellular mechanisms of the observed responses. The model included the actions of neuromodulators through inhibition of a non-pertussis toxin sensitive K(+) current (I(KL)), activation of a pertussis toxin sensitive K(+) current (I(KG)), and a shift in the activation curve of the hyperpolarization-activated cation current (I(h)). The model replicated well the measured responses, and the prolonged inhibition was associated most strongly with changes in I(KG) while modulation of I(KL) or I(h) had minimal effects on post-stimulus inhibition suggesting that neuromodulators released in response to high frequency stimulation are responsible for mediating the post-stimulation bursting and subsequent long duration silence of thalamic neurons. The modeling also indicated that the axons of the model neurons responded robustly to suprathreshold stimulation despite the inhibitory effects on the soma. The findings suggest that during DBS the axons of thalamocortical neurons are activated while the cell bodies are inhibited thus blocking the transmission of pathological signals through the network and replacing them with high frequency regular firing.
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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".