Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes
Journal of Neural Engineering, vol. 20, pp. 046001
Abstract
Abstract Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon the implantation procedure and the subject’s anatomy. Currently, few alternatives exist for the determination of the target subject structural and functional anatomy, and statistical characterizations from cadaveric samples are limited because of their high cost. We propose an optimization workflow that can guide both the pre-surgical planning and the determination of maximally selective multisite stimulation protocols for implants consisting of several intraneural electrodes, and we characterize its performance in silico. We show that the availability of structural and functional information leads to very high performances and allows taking informed decisions on neuroprosthetic design. Approach. We employ hybrid models (HMs) of neuromodulation in conjunction with a machine learning-based surrogate model to determine fiber activation under electrical stimulation, and two steps of optimization through particle swarm optimization to optimize in silico implant geometry, implantation and stimulation protocols using morphological data from the human median nerve at a reduced computational cost. Main results. Our method allows establishing the optimal geometry of multi-electrode transverse intra-fascicular multichannel electrode implants, the optimal number of electrodes to implant, their optimal insertion, and a set of multipolar stimulation protocols that lead in silico to selective activation of all the muscles innervated by the human median nerve. Significance. We show how to use effectively HMs for optimizing personalized neuroprostheses for motor function restoration. We provide in-silico evidences about the potential of multipolar stimulation to increase greatly selectivity. We also show that the knowledge of structural and functional anatomies of the target subject leads to very high selectivity and motivate the development of methods for their in vivo characterization.
Authors 7
-
École Polytechnique Fédérale de Lausanne
Affiliation as printed
Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
TNE Lab, EPFL, 9 Ch. des Mines, Geneve, 1202, SWITZERLAND
-
Affiliation as printed
The Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pontedera, Italy
Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa, 56127, ITALY
-
Elisabeth Koert Aachen
Universitätsmedizin Göttingen · École Polytechnique Fédérale de Lausanne · University of Göttingen · RWTH Aachen University
Affiliation as printed
Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
Institute for Auditory Neuroscience and InnerEarLab, University Medical Center Goettingen, Goettingen, Germany
RWTH Aachen University, Templegraben 55, Aachen, Nordrhein-Westfalen, 52056, GERMANY
-
Scuola Superiore Sant'Anna · Istituti di Ricovero e Cura a Carattere Scientifico · Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele · École Polytechnique Fédérale de Lausanne
Affiliation as printed
Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
The Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pontedera, Italy
Scuola Superiore Sant'Anna, Piazza Martiri della Libertà 33, Pisa, Toscana, 56127, ITALY
-
Universitat Autònoma de Barcelona · Instituto de Salud Carlos III · Biomedical Research Networking Center on Neurodegenerative Diseases · Centro de Investigación Biomédica en Red
Affiliation as printed
Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain
Department of Cell Biology, Physiology and Immunology, Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain
Unitat de Fisiologia Mèdica, Universitat Autonoma de Barcelona Facultat de Medicina, Av. Can Domenech s/n., Campus UAB., Building M, M4-118, Bellaterra, 08193, SPAIN
-
Universitat Autònoma de Barcelona · Instituto de Salud Carlos III · Biomedical Research Networking Center on Neurodegenerative Diseases · Centro de Investigación Biomédica en Red
Affiliation as printed
Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain
Department of Cell Biology, Physiology and Immunology, Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain
Institute of Neuroscience, Universidad Autonoma de Barcelona, Ed M, Campus UAB, Bellaterra, Barcelona, 08193, SPAIN
-
Silvestro Micera corresponding
Scuola Superiore Sant'Anna · École Polytechnique Fédérale de Lausanne
Affiliation as printed
Bertarelli Foundation Chair in Translational Neural Engineering, Center for Neuroprosthetics and Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
The Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pontedera, Italy
Ecole Polytechnique Federale de Lausanne, Rte Cantonale, Lausanne, 1015, SWITZERLAND
Cited by 15 stored of 15
15 results
No patents citing this paper on Lens.org (checked 2026-10-06).