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bioRxiv · 10.1101/2023.02.24.529836

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Romeni, S.·Losanno, E.·Koert, E.·Pierantoni, L.·Delgado-Martinez, I.·Navarro, X.·Micera, S.
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Abstract

ObjectivePeripheral 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 subjects 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. ApproachWe 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 (PSO) to optimize in silico implant geometry, implantation and stimulation protocols using morphological data from the human median nerve at a reduced computational cost. Main resultsOur method allows establishing the optimal geometry of multi-electrode transverse intra-fascicular multichannel electrode (TIME) 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. SignificanceWe 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.

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Romeni, S., Losanno, E., Koert, E., Pierantoni, L., Delgado-Martinez, I., Navarro, X., Micera, S.. 2023-02-24. Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes. https://doi.org/10.1101/2023.02.24.529836

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