Publications

Bioelectronic therapies modulating the vagus nerve are promising for cardiovascular, inflammatory, and mental disorders, but clinical applications are limited by side-effects such as breathing obstruction and headache caused by non-specific stimulation. To design selective and functional stimulation, researchers engineered VaStim, a realistic and efficient in-silico model. They developed a protocol to personalize VaStim in-vivo using simple muscle responses, successfully reproducing experimental observations by combining models with trials on five pigs. Through optimized algorithms, VaStim simulated the complete fiber population in minutes, including often omitted unmyelinated fibers which constitute 80% of the nerve. The model suggested that all Aα-fibers across the nerve affect laryngeal muscle, while heart rate changes were caused by B-efferents in specific fascicles. The complete realistic model is available as a free, publicly accessible tool with a web-based platform for optimizing VNS paradigms and electrode designs.

Background: Vagal nerve fibers traveling in distinct fascicles innervate different organs and regulate specific functions. Current vagus nerve stimulation (VNS) therapies activate vagal fibers non-selectively, often resulting in reduced efficacy and side effects. Objective: To characterize the anatomical organization of vagal fibers and demonstrate fascicle-selective VNS. Methods: We used quantified immunohistochemistry, micro-computed tomography imaging, and multi-contact cuff electrodes to map fascicular organization and perform selective stimulation in swine. Results: Myelinated afferents and efferents occupy separate fascicles. Larynx-, heart-, and lung-specific fascicles are spatially separated and progressively merge. Fascicle-selective VNS elicited organ-specific physiological responses with radially asymmetric compound action potentials. Conclusions: Fascicular VNS enables selective modulation of specific organs and functions, offering improved efficacy and reduced off-target effects compared to conventional non-selective VNS.

Quantitative descriptions of the morphology and structure of peripheral nerves is central in the development of bioelectronic devices interfacing nerves. While histological procedures and microscopy techniques yield high-resolution detailed images of individual axons, automated methods to extract relevant information at the single-axon level are not widely available. A segmentation algorithm was implemented that allows for feature extraction in immunohistochemistry (IHC) images of peripheral nerves at the single fiber scale. Features extracted include short and long cross-sectional diameters, area, perimeter, thickness of surrounding myelin and polar coordinates of single axons within a nerve or nerve fascicle. The algorithm was evaluated using manually annotated IHC images of 27 fascicles of the swine cervical vagus; the accuracy of single-axon detection was 82%, and the classification accuracy of fiber myelination was 89%.

Stimulus-evoked compound action potentials (eCAPs) directly provide fiber engagement information but are currently not feasible in humans. A method to estimate fiber engagement through common, noninvasive physiological readouts could be used in place of eCAP measurements. In anesthetized rats, eCAPs were recorded while registering acute physiological response markers to VNS: cervical electromyography (EMG), changes in heart rate (ΔHR) and breathing interval (ΔBI). Results showed that EMG correlates with A-fiber, ΔHR with B-fiber and ΔBI with C-fiber activation, in agreement with known physiological functions of the vagus. Multivariate models were compiled for quantitative estimation of fiber engagement from physiological markers and stimulation parameters, and frequency gain models allow estimation of fiber engagement at a wide range of VNS frequencies.