Tutorial: a Computational Framework for the Design and Optimization of Peripheral Neural Interfaces

Overview

Journal Nat Protoc

Specialties Biology
Pathology
Science

Date 2020 Sep 29

PMID 32989306

Citations 25

Authors

Simone Romeni

Giacomo Valle

Alberto Mazzoni

Silvestro Micera

Affiliations

Soon will be listed here.

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.

Citing Articles

Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation.

Toni L, Pierantoni L, Verardo C, Romeni S, Micera S Bioelectromagnetics. 2024; 46(1):e22535.

PMID: 39739852 PMC: 11683760. DOI: 10.1002/bem.22535.

Computational modeling of autonomic nerve stimulation: Vagus et al.

Grill W, Pelot N Curr Opin Biomed Eng. 2024; 32.

PMID: 39650310 PMC: 11619812. DOI: 10.1016/j.cobme.2024.100557.

A computational model to design wide field-of-view optic nerve neuroprostheses.

Romeni S, De Luca D, Pierantoni L, Toni L, Marino G, Moccia S iScience. 2024; 27(12):111321.

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Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA.

Beaubois R, Cheslet J, Ikeuchi Y, Branchereau P, Levi T Front Neurosci. 2024; 18:1457774.

PMID: 39600652 PMC: 11588749. DOI: 10.3389/fnins.2024.1457774.

Advanced neuroprosthetic electrode design optimized by electromagnetic finite element simulation: innovations and applications.

Yang S, Yang S, Li P, Gou S, Cheng Y, Jia Q Front Bioeng Biotechnol. 2024; 12:1476447.

PMID: 39574462 PMC: 11579925. DOI: 10.3389/fbioe.2024.1476447.

References

Grill Jr W . Modeling the effects of electric fields on nerve fibers: influence of tissue electrical properties. IEEE Trans Biomed Eng. 1999; 46(8):918-28. DOI: 10.1109/10.775401. View

McNeal D . Analysis of a model for excitation of myelinated nerve. IEEE Trans Biomed Eng. 1976; 23(4):329-37. DOI: 10.1109/tbme.1976.324593. View

Rattay F . Analysis of models for external stimulation of axons. IEEE Trans Biomed Eng. 1986; 33(10):974-7. DOI: 10.1109/TBME.1986.325670. View

Coburn B . Electrical stimulation of the spinal cord: two-dimensional finite element analysis with particular reference to epidural electrodes. Med Biol Eng Comput. 1980; 18(5):573-84. DOI: 10.1007/BF02443129. View

Coburn B, Sin W . A theoretical study of epidural electrical stimulation of the spinal cord--Part I: Finite element analysis of stimulus fields. IEEE Trans Biomed Eng. 1985; 32(11):971-7. DOI: 10.1109/tbme.1985.325648. View

Coburn B . A theoretical study of epidural electrical stimulation of the spinal cord--Part II: Effects on long myelinated fibers. IEEE Trans Biomed Eng. 1985; 32(11):978-86. DOI: 10.1109/TBME.1985.325649. View

Bossetti C, Birdno M, Grill W . Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation. J Neural Eng. 2008; 5(1):44-53. DOI: 10.1088/1741-2560/5/1/005. View

Struijk J, Holsheimer J, van der Heide G, Boom H . Recruitment of dorsal column fibers in spinal cord stimulation: influence of collateral branching. IEEE Trans Biomed Eng. 1992; 39(9):903-12. DOI: 10.1109/10.256423. View

Rattay F, Minassian K, Dimitrijevic M . Epidural electrical stimulation of posterior structures of the human lumbosacral cord: 2. quantitative analysis by computer modeling. Spinal Cord. 2000; 38(8):473-89. DOI: 10.1038/sj.sc.3101039. View

10.

Anderson D, Kipke D, Nagel S, Lempka S, Machado A, Holland M . Intradural Spinal Cord Stimulation: Performance Modeling of a New Modality. Front Neurosci. 2019; 13:253. PMC: 6434968. DOI: 10.3389/fnins.2019.00253. View

11.

McIntyre C, Grill W, Sherman D, Thakor N . Cellular effects of deep brain stimulation: model-based analysis of activation and inhibition. J Neurophysiol. 2003; 91(4):1457-69. DOI: 10.1152/jn.00989.2003. View

12.

Chaturvedi A, Butson C, Lempka S, Cooper S, McIntyre C . Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions. Brain Stimul. 2010; 3(2):65-7. PMC: 2895675. DOI: 10.1016/j.brs.2010.01.003. View

13.

McIntyre C, Foutz T . Computational modeling of deep brain stimulation. Handb Clin Neurol. 2013; 116:55-61. PMC: 5570759. DOI: 10.1016/B978-0-444-53497-2.00005-X. View

14.

Li M, Yan Y, Wang Q, Zhao H, Chai X, Sui X . A simulation of current focusing and steering with penetrating optic nerve electrodes. J Neural Eng. 2013; 10(6):066007. DOI: 10.1088/1741-2560/10/6/066007. View

15.

McIntyre C, Grill W . Finite element analysis of the current-density and electric field generated by metal microelectrodes. Ann Biomed Eng. 2001; 29(3):227-35. DOI: 10.1114/1.1352640. View

16.

Schiefer M, Triolo R, Tyler D . A model of selective activation of the femoral nerve with a flat interface nerve electrode for a lower extremity neuroprosthesis. IEEE Trans Neural Syst Rehabil Eng. 2008; 16(2):195-204. PMC: 2920206. DOI: 10.1109/TNSRE.2008.918425. View

17.

Zelechowski M, Valle G, Raspopovic S . A computational model to design neural interfaces for lower-limb sensory neuroprostheses. J Neuroeng Rehabil. 2020; 17(1):24. PMC: 7029520. DOI: 10.1186/s12984-020-00657-7. View

18.

Raspopovic S, Capogrosso M, Micera S . A computational model for the stimulation of rat sciatic nerve using a transverse intrafascicular multichannel electrode. IEEE Trans Neural Syst Rehabil Eng. 2011; 19(4):333-44. DOI: 10.1109/TNSRE.2011.2151878. View

19.

Raspopovic S, Capogrosso M, Badia J, Navarro X, Micera S . Experimental validation of a hybrid computational model for selective stimulation using transverse intrafascicular multichannel electrodes. IEEE Trans Neural Syst Rehabil Eng. 2012; 20(3):395-404. DOI: 10.1109/TNSRE.2012.2189021. View

20.

McIntyre C, Grill W . Selective microstimulation of central nervous system neurons. Ann Biomed Eng. 2000; 28(3):219-33. DOI: 10.1114/1.262. View