Identification of SSRI-evoked antidepressant sensory signals by decoding vagus nerve activity
Bibliographic record
Abstract
The vagus nerve relays mood-altering signals originating in the gut lumen to the brain. In mice, an intact vagus is required to mediate the behavioural effects of both intraluminally applied selective serotonin reuptake inhibitors and a strain of Lactobacillus with antidepressant-like activity. Similarly, the prodepressant effect of lipopolysaccharide is vagus nerve dependent. Single vagal fibres are broadly tuned to respond by excitation to both anti- and prodepressant agents, but it remains unclear how neural responses encode behaviour-specific information. Here we demonstrate using ex vivo experiments that for single vagal fibres within the mesenteric neurovascular bundle supplying the mouse small intestine, a unique neural firing pattern code is common to both chemical and bacterial vagus-dependent antidepressant luminal stimuli. This code is qualitatively and statistically discernible from that evoked by lipopolysaccharide, a non-vagus-dependent antidepressant or control non-antidepressant Lactobacillus strain and are not affected by sex status. We found that all vagus dependent antidepressants evoked a decrease in mean spike interval, increase in spike burst duration, decrease in gap duration between bursts and increase in intra-burst spike intervals. Our results offer a novel neuronal electrical perspective as one explanation for mechanisms of action of gut-derived vagal dependent antidepressants. We expect that our ex vivo individual vagal fibre recording model will improve the design and operation of new, extant electroceutical vagal stimulation devices currently used to treat major depression. Furthermore, use of this vagal antidepressant code should provide a valuable screening tool for novel potential oral antidepressant candidates in preclinical animal models.
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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) | 0.001 | 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".