ReviewNature protocols2020
Tutorial: a computational framework for the design and optimization of peripheral neural interfaces.
Review in Nature protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 71 citations in OpenAlex.
- The optimization of neuroprosthetic interfaces relying on biophysical and surrogate digital twins.npj biomedical innovations · 2026Review
- Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology.APL bioengineering · 2026Article
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.Journal of neural engineering · 2026Article
- In Silico analysis of concentration and spatial distribution effects of magnetoelectric nanoparticles on peripheral nerve activation.Bioelectronic medicine · 2025Article
- Biophysical characterization of the recording of unmyelinated and myelinated fiber activity with peripheral interfaces.iScience · 2025Article
- Computational insights into magnetoelectric nanoparticles for neural stimulation.Frontiers in neuroscience · 2025Article
- Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation.Bioelectromagnetics · 2025Article
- Article
- Computational modeling of autonomic nerve stimulation: Vagus et al.Current opinion in biomedical engineering · 2024Article
- Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.Nature communications · 2024Article
- Towards enhanced functionality of vagus neuroprostheses through in silico optimized stimulation.Nature communications · 2024Article
- NRV: An open framework for in silico evaluation of peripheral nerve electrical stimulation strategies.PLoS computational biology · 2024Article
- AxoDetect: an automated nerve image segmentation and quantification workflow for computational nerve modeling.Journal of neural engineering · 2024Article
- NRV: An open framework forbioRxiv : the preprint server for biology · 2024Article
- Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA.Frontiers in neuroscience · 2024Article
- Bioelectronic modulation of carotid sinus nerve to treat type 2 diabetes: current knowledge and future perspectives.Frontiers in neuroscience · 2024Review
- Advanced neuroprosthetic electrode design optimized by electromagnetic finite element simulation: innovations and applications.Frontiers in bioengineering and biotechnology · 2024Review
- Design of an adaptable intrafascicular electrode (AIR) for selective nerve stimulation by model-based optimization.PLoS computational biology · 2023Article
- Non-rectangular neurostimulation waveforms elicit varied sensation quality and perceptive fields on the hand.Scientific reports · 2023Article
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peripheral neural interfaces have been successfully used in the recent past to restore sensory-motor functions in disabled subjects and for the neuromodulation of the autonomic nervous system. The optimization of these neural interfaces is crucial for ethical, clinical and economic reasons. In particular, hybrid models (HMs) constitute an effective framework to simulate direct nerve stimulation and optimize virtually every aspect of implantable electrode design: the type of electrode (for example, intrafascicular versus extrafascicular), their insertion position and the used stimulation routines. They are based on the combined use of finite element methods (to calculate the voltage distribution inside the nerve due to the electrical stimulation) and computational frameworks such as NEURON ( https://neuron.yale.edu/neuron/ ) to determine the effects of the electric field generated on the neural structures. They have already provided useful results for different applications, but the overall usability of this powerful approach is still limited by the intrinsic complexity of the procedure. Here, we illustrate a general, modular and expandable framework for the application of HMs to peripheral neural interfaces, in which the correct degree of approximation required to answer different kinds of research questions can be readily determined and implemented. The HM workflow is divided into the following tasks: identify and characterize the fiber subpopulations inside the fascicles of a given nerve section, determine different degrees of approximation for fascicular geometries, locate the fibers inside these geometries and parametrize electrode geometries and the geometry of the nerve-electrode interface. These tasks are examined in turn, and solutions to the most relevant issues regarding their implementation are described. Finally, some examples related to the simulation of common peripheral neural interfaces are provided.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.