Towards enhanced functionality of vagus neuroprostheses through in silico optimized stimulation

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computational Biology

Background:

• Vagus nerve stimulation shows promise for treating cardiovascular, inflammatory, and mental health conditions.
• Current clinical applications are hindered by non-specific stimulation, leading to side effects like breathing issues and headaches.
• Developing selective stimulation methods is crucial for advancing neuromodulation therapies.

Purpose of the Study:

• To engineer VaStim, a realistic and efficient in-silico model for vagus nerve stimulation.
• To develop a protocol for personalizing the VaStim model in-vivo using muscle responses.
• To enhance the efficacy and specificity of bioelectronic therapies while minimizing side effects and animal use.

Main Methods:

• Developed VaStim, an in-silico model of the complete vagus nerve fiber population, including unmyelinated fibers.
• Created a protocol for in-vivo personalization of VaStim using simple muscle responses, validated with trials on five pigs.
• Employed optimized algorithms for rapid simulation of nerve fiber activity.

Main Results:

• VaStim successfully reproduced experimental observations and simulated the entire fiber population efficiently.
• The model identified that Aα-fibers influence laryngeal muscles, while B-efferents in specific fascicles affect heart rate.
• Simulations predicted tripolar stimulation paradigms could decrease laryngeal activity by 70% compared to standard protocols.

Conclusions:

• VaStim provides a realistic and efficient platform for designing selective vagus nerve stimulation.
• The model can guide the development of neuromodulation therapies with improved efficacy and specificity.
• VaStim has the potential to reduce the need for animal experimentation in developing these therapies.