Transcranial alternating current stimulation entrains single-neuron activity in the primate brain
Bibliographic record
Abstract
Spike timing is thought to play a critical role in neural computation and communication. Methods for adjusting spike timing are therefore of great interest to researchers and clinicians alike. Transcranial electrical stimulation (tES) is a noninvasive technique that uses weak electric fields to manipulate brain activity. Early results have suggested that this technique can improve subjects' behavioral performance on a wide range of tasks and ameliorate some clinical conditions. Nevertheless, considerable skepticism remains about its efficacy, especially because the electric fields reaching the brain during tES are small, whereas the likelihood of indirect effects is large. Our understanding of its effects in humans is largely based on extrapolations from simple model systems and indirect measures of neural activity. As a result, fundamental questions remain about whether and how tES can influence neuronal activity in the human brain. Here, we demonstrate that tES, as typically applied to humans, affects the firing patterns of individual neurons in alert nonhuman primates, which are the best available animal model for the human brain. Specifically, tES consistently influences the timing, but not the rate, of spiking activity within the targeted brain region. Such effects are frequency- and location-specific and can reach deep brain structures; control experiments show that they cannot be explained by sensory stimulation or other indirect influences. These data thus provide a strong mechanistic rationale for the use of tES in humans and will help guide the development of future tES applications.
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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 | 0.001 |
| 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".