Losanno, E. | bioRxiv Science

bioRxiv2023

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

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.

bioengineering↗

bioRxiv2022

Validation of manifold-based direct control for a brain-to-body neural bypass

Brain-body interfaces (BBIs) are neuroprostheses that can restore the connection between brain activity and body movements. They have emerged as a radical solution for restoring voluntary hand control in people with upper-limb paralysis. The BBI module decoding motor commands to actuate the limb from brain signals should provide the user with intuitive, accurate, and stable control. Here, we present the design and demonstration in a monkey of a novel brain decoding strategy based on the direct coupling between the activity of intrinsic neural ensembles and output variables, meant to achieve ease of learning and long-term robustness. We identified once an intrinsic low-dimensional space (called manifold) capturing the co-variation patterns of the monkeys neural activity associated to reach-to-grasp movements. We then tested the animals ability to directly control a computer cursor using cortical activation along the manifold axes and demonstrated rapid learning and stable high performance over 16 weeks of experiments. Finally, we showed that this brain decoding strategy can be effectively coupled to peripheral nerve stimulation to trigger hand movements. These results provide evidence that manifold-based direct control has promising characteristics for clinical applications of BBIs.

bioengineering↗