Transcutaneous Cervical Vagus Nerve Stimulation Induces Changes in the Electroencephalogram and Heart Rate Variability of Healthy Dogs, a Pilot Study
Bibliographic record
Abstract
Transcutaneous cervical vagus nerve stimulation (tcVNS) has been used to treat epilepsy in people and dogs. Objective electroencephalographic (EEG) and heart rate variability (HRV) data associated with tcVNS have been reported in people. The question remained whether EEG and electrocardiography (ECG) would detect changes in brain activity and HRV, respectively, after tcVNS in dogs. Simultaneous EEG and Holter recordings, from 6 client-owned healthy dogs were compared for differences pre- and post- tcVNS in frequency band power analysis (EEG) and HRV. The feasibility and tolerance of the patients to the tcVNS were also noted. In a general linear mixed model, the average power per channel per frequency band was found to be significantly different pre- and post-stimulation in the theta (p = 0.02) and alpha bands (p = 0.04). The pooled power spectral analysis detected a significant decrease in the alpha (p < 0.01), theta (p = 0.01) and beta (p = 0.035) frequencies post-stimulation. No significant interaction was observed between dog, attitude, and stimulation in the multivariate model, neither within the same dog nor between individuals. There was a significant increase in the HRV measured by the standard deviation of the inter-beat (SDNN) index (p < 0.01) and a decrease in mean heart rate (p < 0.01) after tcVNS. The tcVNS was found to be well-tolerated. The results of this pilot study suggest that EEG and ECG can detect changes in brain activity and HRV associated with tcVNS in healthy dogs. Larger randomized controlled studies are required to confirm the results of this study and to assess tcVNS potential therapeutic value.
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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 | 0.001 |
| 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".