Artifactual hyperpolarization during extracellular electrical stimulation: Proposed mechanism of high-rate neuromodulation disproved
Bibliographic record
Abstract
BACKGROUND: Kilohertz-frequency electric field stimulation (kEFS) applied to the spinal cord can reduce chronic pain without causing the buzzing sensation (paresthesia) associated with activation of dorsal column fibers. This suggests that high-rate spinal cord stimulation (SCS) has a mode of action distinct from conventional, parasthesia-based SCS. A recent study reported that kEFS hyperpolarizes spinal neurons, yet this potentially transformative mode of action contradicts previous evidence that kEFS induces depolarization and was based on patch clamp recordings whose accuracy in the presence of kEFS has not been verified. OBJECTIVES: We sought to elucidate the basis for kEFS-induced hyperpolarization and to validate the effects of kEFS observed in patch clamp recordings by comparing with independent optical methods. METHODS: Using patch clamp electrophysiology and voltage-sensitive dye (VSD) imaging, we measured the response to kEFS applied in vitro to hippocampal and spinal neurons. RESULTS: The kEFS-induced hyperpolarization observed with current clamp recordings was corroborated by VSD imaging and rheobase measurements in patched neurons. However, no hyperpolarization was observed when imaging unpatched neurons or when recording with a voltage-follower amplifier rather than with a patch clamp amplifier (PCA). We found that EFS induced an artifactual current in PCAs that was injected back into current clamped neurons. The artifactual current induced by single, charge-balanced EFS pulses caused modest hyperpolarization, but these unitary hyperpolarizations accumulated when EFS pulses were repeated at kilohertz frequencies. CONCLUSION: Our results rule out hyperpolarization as the mechanism underlying kEFS-mediated analgesia and highlight the risk of recording artifacts caused by extracellular electrical stimulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".