Using computers to simulate, optimize, and understand neural responses to electrical signals to treat disease


Designing improved therapies to treat diseases of organs innervated by the autonomic nervous system by building next generation computational models that simulate the responses of the vagus nerve to electrical signals.

Problem: Selective and Informed Delivery of Electrical Signals to Your Vagus Nerve

Peripheral nerves course throughout your body, sending signals from the brain and spinal cord to your muscles for that bicep curl and sending signals back from your skin to feel the sun’s warmth. This bidirectional neural signaling also exists for your inner functions, providing subconscious control of your heart, lungs, stomach, liver, and other organs.

Vagus nerve stimulation (VNS) is a type of bioelectronic medicine, where electricity is delivered to our biology, and offers 21st century medicine as an approach to treat a wide variety of diseases, from epilepsy to heart failure, from rheumatoid arthritis to obesity. But how do we know what electrical signals to deliver? The potential choices are almost limitless (Figure 1): the location of stimulation, the shape of the electrical pulses, their amplitude, the rate and pattern of delivery, their ON and OFF times during the day.

Vagus nerve stimulation

Figure 1. The right and left vagus nerves connect to the brainstem and innervate most of the thoracic and abdominal organs. Design of vagus nerve stimulation therapies must consider many parameters. Source of left-hand image: https://biofieldtuningstore.com/products/the-vagus-nerve

The direct responses to these electrical signals are changes in neural activity. Activity might be turned on in the nerve, or if there is ongoing activity, it might be changed or stopped by the electrical signals. These effects can be studied in the lab, but there are limitations. Recordings from the whole nerve indicate the bulk response, like listening to the whole orchestra, while recordings from individual nerve fibers provide detailed information from a very small portion of the population, like listening to only the French horns.

Solution: Developing Computational Models of Nerve Responses

With the rapid growth of computer power, we can build anatomically accurate models of electrical nerve stimulation. In our SPARC program at Duke University, we are designing, building, and running next generation computational models. We developed the ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds) pipeline, which builds 3D models of nerves with cuff electrodes and simulates the responses of individual neurons to electrical signals (Figure 2). We also implemented and compared existing models of individual neurons, and we developed new models of the small unmyelinated neurons in peripheral nerves that reproduce experimental responses.

Stained vagus nerve

Figure 2. Using an image of the vagus nerve stained with tissue dye (left), we can build a 3D model of the nerve with a cuff electrode that delivers electrical signals (middle) and predicts the required signal amplitudes to activate the nerve fibers (right).

Impact and Collaborations: Computational Tools, Validated Models, Clinical Translation, and Therapeutic Mechanisms

We developed a suite of computational tools for simulating, optimizing, and understanding electrical therapies delivered to peripheral nerves. We mapped the vagus nerve structure in rats, pigs, and humans, collaborating with the UNC Research Histology Core. These tools and data allow us to compare the neural responses across individuals (for example, stimulating your vagus nerve versus my vagus nerve) and across species (how do the results in a laboratory animal translate to treating patients). We can also compare the neural responses across stimulation parameters, such as different shapes of the electrical signals. In addition, we are developing optimization tools to design improved therapies. The SPARC Data and Resource Center will be key for disseminating these computational toolkits.

Through the SPARC Initiative, we work with multiple research groups who study vagus nerve stimulation in lab experiments with different species and different target organs and diseases (Figure 3). These collaborations allow us to validate our models, and allow our collaborators to gain further insights into their experimental data and to define stimulation parameters to use during the limited experimental time. For example, our collaboration with the University of Wisconsin-Madison revealed key pathways for side effects of vagus nerve stimulation used to treat epilepsy; these results can then serve as a guide to reduce or avoid side effects and increase the therapeutic effects.

SPARC Vagus Nerve Stimulation Collaborators

Figure 3. The SPARC Initiative has a strong culture of collaborative work. We build computational models of vagus nerve stimulation to complement experimental work conducted by our team members and by other research groups.

Author
Nicole A. Pelot, PhD

Published Date
August 11, 2021

Team Members

Duke University

PI: Warren M. Grill, PhD ORCID iD: 0000-0001-5240-6588
Research Director: Nicole A. Pelot, PhD ORCID iD: 0000-0003-2844-0190

PhD students
Bradley B. Barth ORCID iD: 0000-0002-4344-7182
William J. Huffman ORCID iD: 0000-0001-8995-8829
Minhaj A. Hussain ORCID iD: 0000-0002-4833-7046
Eric D. Musselman ORCID iD: 0000-0001-5295-7267
Brandon J. Thio ORCID iD: 0000-0002-0136-969X
Nathan D. Titus ORCID iD: 0000-0001-8769-2959

Postdocs
Atefeh Ghazavi, PhD ORCID iD: 0000-0002-8021-3393
Kevin J. Mohsenian, PhD ORCID iD: 0000-0001-8693-2121
Edgar Peña, PhD ORCID iD: 0000-0003-3148-8355
Casey J. Steadman, PhD ORCID iD: 0000-0002-1726-2571

Staff
David C. Catherall ORCID iD: 0000-0002-0085-7325
Christopher J. Davis ORCID iD: 0000-0002-0631-9966
Timothy M. Hoer ORCID iD: 0000-0001-8523-9499
Karthik Kumaravelu ORCID iD: 0000-0002-7619-322X
Christopher L. Langdale ORCID iD: 0000-0002-6704-2665

Masters students
Erin G. Harten ORCID iD: 0000-0003-1434-7438

Undergraduate students
Jake E. Cariello ORCID iD: 0000-0001-7288-9943
Gabriel B. Goldhagen ORCID iD: 0000-0002-6319-9569

Collaborators

University of Wisconsin Madison
PI: Kip A. Ludwig, PhD ORCID iD: 0000-0003-4889-1941

PhD students
Stephan L. Blanz ORCID iD: 0000-0003-1952-0973
Evan N. Nicolai ORCID iD: 0000-0003-0447-568X
Nishant Verma ORCID iD: 0000-0002-7657-0275

Postdocs
Megan L. Settell, PhD ORCID iD: 0000-0002-2924-360X

Case Western Reserve University
PI: Andrew J. Shoffstall, PhD ORCID iD: 0000-0002-0881-2180

PhD students
Aniruddha Upadhye ORCID iD: 0000-0002-9303-5872
Chaitanya Kolluru ORCID iD: 0000-0002-3211-7794


Supporting information

Musselman ED, Cariello JE, Grill WM, Pelot NA (2021) ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves. PLoS Comput Biol 17(8): e1009285. https://doi.org/10.1371/journal.pcbi.1009285 (other resources: code and documentation)

Nicolai EN, Settell ML, Knudsen BE, McConico AL, Gosink BA, Trevathan JK, Baumgart IW, Ross EK, Pelot NA, Grill WM, Gustafson KJ, Shoffstall AJ, Williams JC, Ludwig KA. Sources of off-target effects of vagus nerve stimulation using the helical clinical lead in domestic pigs. J Neural Eng. 2020;17(4):046017. https://doi.org/10.1088/1741-2552/ab9db8

Pelot NA, Goldhagen GB, Cariello JE, Musselman ED, Clissold KA, Ezzell JA, Grill WM. Quantified Morphology of the Cervical and Subdiaphragmatic Vagus Nerves of Human, Pig, and Rat. Front Neurosci. 2020;14:601479. https://doi.org/10.3389/fnins.2020.601479

Pelot NA, Catherall DC, Thio BJ, Titus ND, Liang ED, Henriquez CS, Grill WM. Excitation properties of computational models of unmyelinated peripheral axons. J Neurophysiol. 2021;125(1):86-104. https://doi.org/10.1152/jn.00315.2020

Peña E, Pelot NA, Grill WM. Quantitative comparisons of block thresholds and onset responses for charge-balanced kilohertz frequency waveforms. J Neural Eng. 2020;17(4):046048. https://doi.org/10.1088/1741-2552/abadb5

Peña E, Pelot NA, Grill WM. Non-monotonic kilohertz frequency neural block thresholds arise from amplitude- and frequency-dependent charge imbalance. Sci Rep. 2021;11(1):5077. https://doi.org/10.1038/s41598-021-84503-3

Settell ML, Pelot NA, Knudsen BE, Dingle AM, McConico AL, Nicolai EN, Trevathan JK, Ezzell JA, Ross EK, Gustafson KJ, Shoffstall AJ, Williams JC, Zeng W, Poore SO, Populin LC, Suminski AJ, Grill WM, Ludwig KA. Functional vagotopy in the cervical vagus nerve of the domestic pig: implications for the study of vagus nerve stimulation. J Neural Eng. 2020;17(2):026022. https://doi.org/10.1088/1741-2552/ab7ad4


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