Vagus nerve stimulation parameters evoke differential neuronal responses in the locus coeruleus
Bibliographic record
Abstract
Vagus nerve stimulation (VNS) is used to treat drug-resistant epilepsy and depression, with additional applications under investigation. The noradrenergic center locus coeruleus (LC) is vital for VNS effects; however, the impact of varying stimulation parameters on LC activation is poorly understood. This study characterized LC activation across VNS parameters. Extracellular activity was recorded in rats' left LC while 11 VNS paradigms, utilizing variable frequencies and bursting characteristics, were pseudorandomly delivered to the left cervical vagus for five cycles. Neurons' change from baseline firing rate and timing response profiles were assessed. The proportion of neurons categorized as responders over 5 VNS cycles doubled in comparison to the first VNS cycle (p < 0.001) for all VNS paradigms, demonstrating an amplification effect. The percentage of positively consistent/positive responders increased for standard VNS paradigms with frequencies ≥10 Hz and for bursting paradigms with shorter interburst intervals and more pulses per burst. The synchrony between pairs of LC neurons increased during bursting VNS but not standard paradigms. Also, the probability of evoking a direct response during bursting VNS was higher with longer interburst intervals and a higher number of pulses per burst. Standard paradigms between 10-30 Hz best positively activates LC with consistency to VNS while the best bursting paradigm to increase activity was 300 Hz, seven pulses per burst separated by 1 s. Bursting VNS was effective in increasing synchrony between pairs of neurons, suggesting a common network recruitment originating from vagal afferents. These results indicate differential activation of LC neurons depending on the VNS parameters delivered.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".