‘ASCENT’ makes it easier to study the electrical stimulation of nerves

The ASCENT tool models how nerves can respond to electrical stimulation from custom electrodes. Nerve fibers appear in red and blue, and the custom electrodes appear in gray. The bottom graphic shows how the nerves appear when they are activated.
Credit: Eric Musselman, Duke University

Computational platform allows non-experts to create patient-specific, 3D models of nerves being electrically stimulated.

Biomedical engineers at Duke University have developed an open-source software platform that automates 3D electrical nerve stimulation modeling. This may allow  researchers to predict how specific nerves will respond to different patterns of stimulation from custom electrodes.

The researchers hope the platform will help researchers create accurate models of new therapies for a variety of diseases, including diabetes, rheumatoid arthritis and cardiovascular disease.

Like a pacemaker for the heart, nerve stimulation devices are implanted in the body and programmed to send targeted pulses of electricity into nerves. These electrical pulses may initiate or suppress signals in the nerve, changing the regulation of organ functions and possibly treating disease.  Nerve stimulation  is a promising alternative to drugs in some cases..

This research appears online August 26 in the journal PLOS Computational Biology.

Certain electrical stimulation therapies, like vagus nerve stimulation, have already proven to be an effective treatment for illnesses like epilepsy and depression in patients who don’t respond to traditional drug therapies.

“Despite promising preclinical data, translating nerve stimulation to the clinic is challenging because there is a lot of variability in neural anatomy between animals and humans and even person to person,” said Nicole Pelot, a research director in the biomedical engineering lab of Warren Grill at Duke University. “These variabilities require choosing different electrodes or stimulation parameters to achieve a targeted response, which can be a bit of a guessing game.”

While computational modeling allows researchers to avoid some of guesswork in electrical stimulation,  the modeling process is highly complex, which usually restricts its use to expert developers and limits model reproducibility and reuse. Grill, the Edmund T. Pratt, Jr. School Distinguished Professor of Biomedical Engineering at Duke, has spent the last four years with his colleagues developing a solution.

Grill said ASCENT, or Automated Simulations to Characterize Electrical Nerve Thresholds, makes modeling nerve stimulation more accessible, reproducible and efficient. It allows researchers to define a variety of information, like nerve geometry, fiber models, electrode design and stimulation waveforms, and then simulate a nerve’s response to the electrical pulses.

ASCENT’S automation allows users to replicate models from previously published research, turning a process that could take years into something that may take just a few weeks or days..

“We designed the code so it’s very expandable,” added Jake Cariello, an undergraduate research assistant in the Grill lab. “We broke down the complex system into its sub-components, and each of those sub-components is customizable.”

ASCENT is an open-source project and is free for users to access, as part of the National Institutes of Health Common Fund’s Stimulating Peripheral Activity to Relieve Conditions program (SPARC). The ASCENT software is available for download HERE.

“A tool that is this specialized is usually inaccessible to more novice users,” said Eric Musselman, a PhD student in the Grill lab. “We hope ASCENT will help push the science forward as more researchers are able to design and interpret therapeutic interventions using computational models.”

This project was supported by the National Institutes of Health’s SPARC Program (OT2OD025340).

CITATION: “ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves” Eric Musselman, Jake Cariello, Warren Grill, Nicole Pelot. PLOS Computational Biology, August 26, 2021. DOI: 10.1371/journal.pcbi.1009285

Journal: PLoS Computational Biology
DOI: 10.1371/journal.pcbi.1009285
Method of Research: Computational simulation/modeling
Subject of Research: Not applicable
Article Title: ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves
Article Publication Date: 26-Aug-2021

Media Contact

Michaela Kane
michaela.kane@duke.edu
Office: 919-660-5155

www.duke.edu

Media Contact

Michaela Kane
Duke University

All latest news from the category: Medical Engineering

The development of medical equipment, products and technical procedures is characterized by high research and development costs in a variety of fields related to the study of human medicine.

innovations-report provides informative and stimulating reports and articles on topics ranging from imaging processes, cell and tissue techniques, optical techniques, implants, orthopedic aids, clinical and medical office equipment, dialysis systems and x-ray/radiation monitoring devices to endoscopy, ultrasound, surgical techniques, and dental materials.

Back to home

Comments (0)

Write a comment

Newest articles

Switching on a superfluid

Exotic phase transitions unlock pathways to future, superfluid-based technologies. We can learn a lot by studying microscopic and macroscopic changes in a material as it crosses from one phase to…

Researchers use breakthrough method to answer key question about electron states

Scientists are working hard to engineer the properties of nanostructures, such as atoms and molecules, to realize efficient logic devices that can operate at the fundamental scale of matter –…

Scientists develop artificial intelligence method to predict anti-cancer immunity

Machine learning algorithms are shedding light on neoantigen T cell-receptor pairs. Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence…

Partners & Sponsors