Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface.

Objective.Brain-machine interfaces (BMIs) have the potential to augment human functions and restore independence in people with disabilities, yet a compromise between non-invasiveness and performance limits their relevance.Approach.Here, we hypothesized that a non-invasive neuromuscular-machine interface providing real-time neurofeedback of individual motor units within a muscle could enable independent motor unit control to an extent suitable for high-performance BMI applications.Main results.Over 6 days of training, eight participants progressively learned to skillfully and independently control three biceps brachii motor units to complete a 2D center-out task. We show that neurofeedback enabled motor unit activity that largely violated recruitment constraints observed during ramp-and-hold isometric contractions thought to limit individual motor unit controllability. Finally, participants demonstrated the suitability of individual motor units for powering general applications through a spelling task.Significance.These results illustrate the flexibility of the sensorimotor system and highlight individual motor units as a promising source of control for BMI applications.

Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience.

Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

A biomimetic electrical stimulation strategy to induce asynchronous stochastic neural activity.

OBJECTIVE: Electrical stimulation is an effective method for artificially modulating the activity of the nervous system. However, current stimulation paradigms fail to reproduce the stochastic and asynchronous properties of natural neural activity. Here, we introduce a novel biomimetic stimulation (BioS) strategy that overcomes these limitations. APPROACH: We hypothesized that high-frequency amplitude-modulated bursts of stimulation could induce asynchronous neural firings by distributing recruitment over the duration of a burst, without sacrificing the ability to precisely control neural activity. We tested this hypothesis using computer simulations and ex vivo experiments. MAIN RESULTS: We found that BioS bursts induce asynchronous, stochastic, yet controllable, neural activity. We established that varying the amplitude, duration, and repetition frequency of a BioS burst enables graded modulation of the number of recruited fibers, their firing rate, and the synchronicity of their responses. SIGNIFICANCE: These results demonstrate an unprecedented level of control over artificially induced neural activity, enabling the design of next-generation BioS paradigms with potentially profound consequences for the field of neurostimulation.

Electrical spinal cord stimulation must preserve proprioception to enable locomotion in humans with spinal cord injury.

Epidural electrical stimulation (EES) of the spinal cord restores locomotion in animal models of spinal cord injury but is less effective in humans. Here we hypothesized that this interspecies discrepancy is due to interference between EES and proprioceptive information in humans. Computational simulations and preclinical and clinical experiments reveal that EES blocks a significant amount of proprioceptive input in humans, but not in rats. This transient deafferentation prevents modulation of reciprocal inhibitory networks involved in locomotion and reduces or abolishes the conscious perception of leg position. Consequently, continuous EES can only facilitate locomotion within a narrow range of stimulation parameters and is unable to provide meaningful locomotor improvements in humans without rehabilitation. Simulations showed that burst stimulation and spatiotemporal stimulation profiles mitigate the cancellation of proprioceptive information, enabling robust control over motor neuron activity. This demonstrates the importance of stimulation protocols that preserve proprioceptive information to facilitate walking with EES.

Closed-loop control of trunk posture improves locomotion through the regulation of leg proprioceptive feedback after spinal cord injury.

After spinal cord injury (SCI), sensory feedback circuits critically contribute to leg motor execution. Compelled by the importance to engage these circuits during gait rehabilitation, assistive robotics and training protocols have primarily focused on guiding leg movements to reinforce sensory feedback. Despite the importance of trunk postural dynamics on gait and balance, trunk assistance has comparatively received little attention. Typically, trunk movements are either constrained within bodyweight support systems, or manually adjusted by therapists. Here, we show that real-time control of trunk posture re-established dynamic balance amongst bilateral proprioceptive feedback circuits, and thereby restored left-right symmetry, loading and stepping consistency in rats with severe SCI. We developed a robotic system that adjusts mediolateral trunk posture during locomotion. This system uncovered robust relationships between trunk orientation and the modulation of bilateral leg kinematics and muscle activity. Computer simulations suggested that these modulations emerged from corrections in the balance between flexor- and extensor-related proprioceptive feedback. We leveraged this knowledge to engineer control policies that regulate trunk orientation and postural sway in real-time. This dynamical postural interface immediately improved stepping quality in all rats regardless of broad differences in deficits. These results emphasize the importance of trunk regulation to optimize performance during rehabilitation.

Mechanisms Underlying the Neuromodulation of Spinal Circuits for Correcting Gait and Balance Deficits after Spinal Cord Injury.

Epidural electrical stimulation of lumbar segments facilitates standing and walking in animal models and humans with spinal cord injury. However, the mechanisms through which this neuromodulation therapy engages spinal circuits remain enigmatic. Using computer simulations and behavioral experiments, we provide evidence that epidural electrical stimulation interacts with muscle spindle feedback circuits to modulate muscle activity during locomotion. Hypothesis-driven strategies emerging from simulations steered the design of stimulation protocols that adjust bilateral hindlimb kinematics throughout gait execution. These stimulation strategies corrected subject-specific gait and balance deficits in rats with incomplete and complete spinal cord injury. The conservation of muscle spindle feedback circuits across mammals suggests that the same mechanisms may facilitate motor control in humans. These results provide a conceptual framework to improve stimulation protocols for clinical applications.