Tutorial: a computational framework for the design and optimization of peripheral neural interfaces

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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Fig. 1: Geometry generation for FEM.
Fig. 2: FEM settings and solution workflow.
Fig. 3: Fiber computational model.
Fig. 4: Result analysis methods.
Fig. 5: Nerve histological components.
Fig. 6: Manual segmentation of histological sections of the human sciatic nerve from ref. 50.
Fig. 7: Fascicle geometry simplification and reshaping.
Fig. 8: TIME-induced fascicle reshaping.
Fig. 9: Complete workflow of the fiber diameter distribution fitting routine.
Fig. 10: Fiber diameter distribution histograms (normalized area) in human sural nerve.
Fig. 11: Fiber diameter distribution fit results.
Fig. 12: Fiber and fascicle bidimensional packing.
Fig. 13: Heatmaps of the packing fractions, fraction of failed attempts and packing times for each combination of number and radius of fascicles.
Fig. 14: COMSOL electrode geometries.
Fig. 15: FEM potential field examples.

Data availability

The authors declare that all data needed for the simulation examples in this tutorial can be found within the paper and its references. No new experimental data have been used for the writing of this protocol.

Software availability

Illustrative code for the COMSOL/MATLAB setting of HMs, for fascicle shape simplification and for fiber and fascicle packing is available at https://github.com/s-romeni/PNS-HM.

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Acknowledgements

This work was partly funded by the Bertarelli Foundation, and the Swiss National Science Foundation via the National Competence Center Research (NCCR) Robotics and the projects SYMBIOLEGs, NeuGrasp and CHRONOS.

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Authors

Contributions

S.R. built the presented framework starting from state-of-the-art HM, developed the software on which the presented framework was run to produce the presented results and figures, wrote the manuscript and produced the figures; G.V. provided state-of-the-art insight and software on HM, supervised modeling activity and helped drafting/revising the manuscript; A.M. and S.M. guided the framework development providing main core concepts on needed expansions of state-of-the-art HM, and revised the manuscript.

Corresponding author

Correspondence to Silvestro Micera.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Shih Cheng Yen, Mario Romero-Ortega and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Key reference(s) using this protocol

Raspopovic, S., Capogrosso, M. & Micera, S. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 333–344 (2011): https://ieeexplore.ieee.org/document/5898424

Raspopovic, S., Capogrosso, M., Badia, J., Navarro, X. & Micera, S. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 395–404 (2012): https://ieeexplore.ieee.org/document/6177270

Raspopovic, S., Petrini, M. P., Zelechowski, M. & Valle, G. Proc. IEEE 105, 34–49 (2016): https://ieeexplore.ieee.org/document/7570207

Gaillet, V. et al. Nat. Biomed. Eng. 4, 181–194 (2020): https://doi.org/10.1038/s41551-019-0446-8

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Oddo, C. eLife 5, e09148 (2016): https://doi.org/10.7554/eLife.09148.001

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Romeni, S., Valle, G., Mazzoni, A. et al. Tutorial: a computational framework for the design and optimization of peripheral neural interfaces. Nat Protoc 15, 3129–3153 (2020). https://doi.org/10.1038/s41596-020-0377-6

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