Dataset Information

Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.

ABSTRACT: Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged neurostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming other search strategies after testing only a fraction of the possible combinations. Through a series of real-time multi-dimensional neurostimulation experiments, we demonstrate optimization across diverse biological targets (brain, spinal cord), animal models (rats, non-human primates), in healthy subjects, and in neuroprosthetic intervention after injury, for both immediate and continual learning over multiple sessions. GP-BO can embed and improve "prior" expert/clinical knowledge to dramatically enhance its performance. These results advocate for broader establishment of learning agents as structural elements of neuroprosthetic design, enabling personalization and maximization of therapeutic effectiveness.

SUBMITTER: Bonizzato M

PROVIDER: S-EPMC10140617 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.

Bonizzato Marco M Guay Hottin Rose R Côté Sandrine L SL Massai Elena E Choinière Léo L Macar Uzay U Laferrière Samuel S Sirpal Parikshat P Quessy Stephan S Lajoie Guillaume G Martinez Marina M Dancause Numa N

Cell reports. Medicine 20230411 4

Neural stimulation can alleviate paralysis and sensory deficits. Novel high-density neural interfaces can enable refined and multipronged neurostimulation interventions. To achieve this, it is essential to develop algorithmic frameworks capable of handling optimization in large parameter spaces. Here, we leveraged an algorithmic class, Gaussian-process (GP)-based Bayesian optimization (BO), to solve this problem. We show that GP-BO efficiently explores the neurostimulation space, outperforming o ...[more]

PMID: 37044093

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Project description:Electrical stimulation of the spinal cord is a promising rehabilitation intervention to restore/augment motor function after spinal cord injury (SCI). Combining sensory feedback with stimulation of remaining motor circuits has been shown to be a prerequisite for the functional improvement of SCI patients. However, little is known about the cellular mechanisms potentially underlying this functional benefit in the injured spinal cord. Here, we combine computer simulations with an isolated whole-tissue adult mouse spinal cord preparation to examine synaptic, cellular, and system potentials measured from single motoneurons and ventral roots. The stimulation protocol included separate and combined activation of the sensory inputs (evoked by dorsal root stimulation) and motor inputs (evoked by stimulation of spinal cord tissue) at different frequencies, intensities, and neuromodulatory states. Our data show that, while sensory inputs exhibit short-term depression in response to a train of stimulation, motor inputs exhibit short-term facilitation. However, the concurrent activation of both inputs elicits a stronger and steadier motor output. This effect is enhanced by the application of pharmacological neuromodulators. Furthermore, sensorimotor excitatory postsynaptic potentials (EPSPs) summate sublinearly (i.e., their combination produces an excitatory potential smaller than the sum of the excitatory potentials they would individually produce). However, ventral root compound action potentials (CoAPs) summate supralinearly generating much higher outputs. Computer simulations revealed that the contrasting summation and disproportionality in plasticity between the excitatory postsynaptic potentials (EPSPs) and CoAPs result from the motoneuronal firing threshold acting as an amplitude-selective filter. Together, these results provide the mechanistic basis for the cellular processes contributing to the generation of steady motor outputs using spinal stimulation. This data has great potential to guide the design of more effective stimulation protocols in SCI patients.

Dataset Information

Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.

Publications

Autonomous optimization of neuroprosthetic stimulation parameters that drive the motor cortex and spinal cord outputs in rats and monkeys.

Similar Datasets

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