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Updated: Dec 19, 2025

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

A computational outlook on neurostimulation.

Marco Capogrosso^1,2, Scott F Lempka^3,4,5

¹Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA USA.

Bioelectronic Medicine

|June 4, 2020

Summary

This summary is machine-generated.

Computational models accelerate neurostimulation research by providing mechanistic understanding of electric fields and neural circuits. This approach aids in developing effective neurostimulation strategies for clinical applications and device regulations.

Keywords:

Chronic pain Computational modelling Finite element modelling Neuromodulation Neurostimulation Spinal cord injury Spinal cord stimulation

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Published on: August 18, 2018

Area of Science:

Neuroscience
Biomedical Engineering
Computational Modeling

Background:

Neurostimulation is vital for treating neurological disorders, but its translation to clinical practice is slow.
Complex technologies and regulatory hurdles impede the development of effective neurostimulation strategies.
A mechanistic understanding of electric field-neural circuit interactions is lacking.

Purpose of the Study:

To discuss the influence of computational models on neurostimulation research and applications.
To highlight the role of computational models in accelerating translational research.
To propose a vision for future applications of computational models in neurostimulation.

Main Methods:

Review of computational modeling applications in neurostimulation.
Analysis of model impact on pain and movement recovery, deep brain stimulation, and regulatory processes.
Discussion of mechanistic insights derived from computational models.

Main Results:

Computational models have significantly influenced neurostimulation for pain, movement recovery, and deep brain stimulation.
Models have aided in understanding and navigating device regulations for neurostimulation.
The application of computational models facilitates a mechanistic understanding of neurostimulation.

Conclusions:

Computational models are essential tools for advancing neurostimulation research and clinical translation.
Integrating computational models can overcome current limitations in neurostimulation development.
Future advancements in neurostimulation will heavily rely on computational modeling for mechanistic insights and accelerated clinical adoption.