The oculomotor system of the non-human primate as a preclinical model for temporal interference brain stimulation
Bibliographic record
Abstract
electrophysiological recording during TI stimulation would pave the way for that, but it has to address the challenges of TI-induced nonlinear artefacts.Here, using state-of-the-art strategies, we recorded artefact-free electrophysiological signals during TI stimulation, specifically local field potential (LFP) and neural spikes, and unveiled the fundamentals of TI stimulation using electrophysiological observations.We first discovered the electrical and electrochemical sources of TI-induced nonlinear artefacts and then developed strategies to attenuate these artefacts.Using these strategies, we demonstrated that artefact-free local field potential (LFP) was concurrently recorded during TI stimulation in anaesthetized mice.Next, using the LFP approach, we explored the fundamentals of TI stimulation, like how the carrier frequencies and stimulation amplitudes affect the stimulation strength.Additionally, we also compared the efficiency of transcranial alternating current stimulation (tACS) and TI by measuring the stimulation-induced neural spike entrainment in anaesthetized rats, which cannot be completed by the LFP approach due to tACS-induced linear artefacts.We found both tACS and TI entrained the neural spikes, but the entrainment is stronger in tACS, which indicates that tACS is more effective than TI.This study advanced our understanding of the sources as well as attenuation strategies of TI-induced nonlinear artefacts and developed the LFP as well as neural spike approaches to directly monitor the brain responses to TI stimulation, which provides tools to investigate TI stimulation.The LFP and neural spike results unveil the fundamentals of TI stimulation, which advances our understanding of TI and facilitates its clinical translation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".