Acute transcutaneous auricular vagus nerve stimulation modulates presynaptic <scp>SV2A</scp> density in healthy rat brain: An in vivo <scp>microPET</scp> study
Bibliographic record
Abstract
Abstract Vagus nerve stimulation (VNS) is the subject of exploration as an adjunct treatment for neurological disorders such as epilepsy, chronic migraine, pain, and depression. A non‐invasive form of VNS is transcutaneous auricular VNS (taVNS). Combining animal models and positron emission tomography (PET) may lead to a better understanding of the elusive mechanisms of taVNS. We evaluated the acute effect of electrical stimulation of the left vagus nerve via the ear on brain synaptic vesicle glycoprotein 2A (SV2A) as a measure of presynaptic density and glucose metabolism in naïve rats. Female Sprague–Dawley rats were imaged with [11C]UCB‐J (n = 11) or [18F]fluorodeoxyglucose ([18F]FDG) PET (n = 13) on two separate days, (1) at baseline, and (2) after acute unilateral left taVNS or sham stimulation (30 min). We calculated the regional volume of distribution (VT) for [11C]UCB‐J and standard uptake values (SUV) for [18F]FDG. We observed regional reductions of [11C]UCB‐J binding in response to taVNS ranging from 36% to 59%. The changes in taVNS compared to baseline were significantly larger than those induced by sham stimulation. The differences were observed bilaterally in the frontal cortex, striatum, and midbrain. The [18F]FDG PET uptake remained unchanged following acute taVNS or sham stimulation compared to baseline values. This proof‐of‐concept study shows for the first time that acute taVNS for 30 min can modulate in vivo synaptic SV2A density in cortical and subcortical regions of healthy rats. Preclinical disease models and PET ligands of different targets can be a powerful combination to assess the therapeutic potential of taVNS.
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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 | 0.001 |
| 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".