A multivariate physiological model of vagus nerve signalling during metabolic challenges in anaesthetised rats for diabetes treatment

Abstract

Objective. This study aims to develop a comprehensive decoding framework to create a multivariate physiological model of vagus nerve transmission that reveals the complex interactions between the nervous and metabolic systems. Approach. Vagus nerve activity was recorded in female Sprague-Dawley rats using gold hook microwires implanted around the left cervical vagus nerve. The rats were divided into three experimental cohorts (intact nerve, ligation nerve for recording afferent activation, and ligation for recording efferent activation) and metabolic challenges were administered to change glucose levels while recording the nerve activity. The decoding methodology involved various techniques, including continuous wavelet transformation, extraction of breathing rate (BR), and correlation of neural metrics with physiological signals. Main results. Decrease in glucose level was consistently negatively correlated with an increase in the firing activity of the intact vagus nerve that was found to be conveyed by both afferent and efferent pathways, with the afferent response being more similar to the one on the intact nerve. A larger variability was observed in the sensory and motor responses to hyperglycaemia. A novel strategy to extract the BR over time based on inter-burst-interval is also presented. The vagus afferent was found to encode breathing information through amplitude and firing rate modulation. Modulations of the signal amplitude were also observed due to changes in heart rate in the intact and efferent recordings, highlighting the parasympathetic control of the heart. Significance. The analytical framework presented in this study provides an integrative understanding that considers the relationship between metabolic, cardiac, and breathing signals and contributes to the development of a multivariable physiological model for the transmission of vagus nerve signals. This work progresses toward the development of closed-loop neuro-metabolic therapeutic systems for diabetes.