PREDICTING GENERAL VAGAL NERVE ACTIVITY VIA THE DEVELOPMENT OF BIOPHYSICAL ARTIFICIAL INTELLIGENCE

The vagus nerve (VN) is the tenth cranial nerve that mediates most of the parasympathetic functions of the autonomic nervous system. The axons of the human VN comprise a mix of unmyelinated and myelinated axons, where ~80% of the axons are unmyelinated C fibers (Havton et al., 2021). Understanding that most VN axons are unmyelinated, there is a need to map the pathways of these axons to and from organs to understand their function(s) and whether C fiber morphology or signaling characteristics yield insights into their functions. Developing a machine learning model that detects and predicts the morphology of VN single fiber action potentials based on select fiber characteristics, e.g., diameter, myelination, and position within the VN, allows us to more readily categorize the nerve fibers with respect to their function(s). Additionally, the features of this machine learning model could help inform peripheral neuromodulation devices that aim to restore, replace, or augment one or more specific functions of the VN that have been lost due to injury, disease, or developmental abnormalities.

We designed and trained four types of Multi-layer Perceptron Artificial Deep Neural Networks (MLP-ANN) with 10,000 rat abdominal vagal C-fibers simulated via the peripheral neural interface model ViNERS. We analyze the accuracy of each MLP-ANN’s SFAP predictions by conducting normalized cross-correlation and morphology analyses with the ViNERS C-fiber SFAP counterparts. Our results showed that our best MLP predicted over 94% of the C-fiber SFAPs with strong normalized cross-correlation coefficients of 0.7 through 1 with the ViNERS SFAPs. Overall, this novel tool can use a C-fiber’s biophysical characteristics (i.e., fiber diameter size, fiber position on the x/y axis, etc.) to predict C-fiber SFAP morphology.