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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.

The clinical applications of neurotechnology are rapidly expanding, and the combination of different approaches could be more effective and precise to treat brain disorders. This Perspective discusses the potential and challenges of \"multimodal neuromodulation,\" which combines modalities such as electrical, magnetic, and ultrasound stimulation.

Peripheral neural interfaces have been successfully used in the recent past to restore sensory-motor functions in disabled subjects and for the neuromodulation of the autonomic nervous system. The optimization of these neural interfaces is crucial for ethical, clinical and economic reasons. In particular, hybrid models (HMs) constitute an effective framework to simulate direct nerve stimulation and optimize virtually every aspect of implantable electrode design: the type of electrode (for example, intrafascicular versus extrafascicular), their insertion position and the used stimulation routines. They are based on the combined use of finite element methods (to calculate the voltage distribution inside the nerve due to the electrical stimulation) and computational frameworks such as NEURON ( https://neuron.yale.edu/neuron/ ) to determine the effects of the electric field generated on the neural structures. They have already provided useful results for different applications, but the overall usability of this powerful approach is still limited by the intrinsic complexity of the procedure. Here, we illustrate a general, modular and expandable framework for the application of HMs to peripheral neural interfaces, in which the correct degree of approximation required to answer different kinds of research questions can be readily determined and implemented. The HM workflow is divided into the following tasks: identify and characterize the fiber subpopulations inside the fascicles of a given nerve section, determine different degrees of approximation for fascicular geometries, locate the fibers inside these geometries and parametrize electrode geometries and the geometry of the nerve–electrode interface. These tasks are examined in turn, and solutions to the most relevant issues regarding their implementation are described. Finally, some examples related to the simulation of common peripheral neural interfaces are provided. Neural interfaces with implantable electrodes are used to modulate and restore function to the peripheral nervous system. Hybrid modeling described in this protocol is used to optimize each aspect of the implantable electrode design and operation.

In lower mammals, locomotion seems to be mainly regulated by subcortical and spinal networks. On the contrary, recent evidence suggests that in humans the motor cortex is also significantly engaged during complex locomotion tasks. However, a detailed understanding of cortical contribution to locomotion is still lacking especially during stereotyped activities. Here, we show that cortical motor areas finely control leg muscle activation during treadmill stereotyped walking. Using a novel technique based on a combination of Reliable Independent Component Analysis, source localization and effective connectivity, and by combining electroencephalographic (EEG) and electromyographic (EMG) recordings in able-bodied adults we were able to examine for the first time cortical activation patterns and cortico-muscular connectivity including information flow direction. Results not only provided evidence of cortical activity associated with locomotion, but demonstrated significant causal unidirectional drive from contralateral motor cortex to muscles in the swing leg. These insights overturn the traditional view that human cortex has a limited role in the control of stereotyped locomotion, and suggest useful hypotheses concerning mechanisms underlying gait under other conditions. Motor cortex proactively drives contralateral swing leg muscles during treadmill walking, counter to the traditional view of stereotyped human locomotion. •Cortical contributions to stereotyped locomotion are still not well understood.•We studied EEG – EMG effective cortico-muscular connectivity during treadmill walking.•A novel technique based on reliable source localization and effective connectivity is proposed.•The Motor Cortex drives leg muscles even during stereotyped locomotion.•The results counter the traditional view of limited Cortex involvement in stereotyped locomotion.

According to amputees, sensory feedback is amongst the most important features lacking from commercial prostheses. Although restoration of touch by means of implantable neural interfaces has been achieved, these approaches require surgical interventions, and their long-term usability still needs to be fully investigated. Here, we developed a non-invasive alternative which maintains some of the advantages of invasive approaches, such as a somatotopic sensory restitution scheme. We used transcutaneous electrical nerve stimulation (TENS) to induce referred sensations to the phantom hand of amputees. These sensations were characterized in four amputees over two weeks. Although the induced sensation was often paresthesia, the location corresponded to parts of the innervation regions of the median and ulnar nerves, and electroencephalographic (EEG) recordings confirmed the presence of appropriate responses in relevant cortical areas. Using these sensations as feedback during bidirectional prosthesis control, the patients were able to perform several functional tasks that would not be possible otherwise, such as applying one of three levels of force on an external sensor. Performance during these tasks was high, suggesting that this approach could be a viable alternative to the more invasive solutions, offering a trade-off between the quality of the sensation, and the invasiveness of the intervention.

Regulatory sandboxes could be fruitfully used to boost Invasive Brain-Computer Interfaces, but they should be carefully designed. We highlight five elements are essential: they concern the entry criteria, the participated, adaptive and supervised design of decision-making process, and long-term risk management. Regulatory sandboxes could be fruitfully used to boost Invasive Brain-Computer Interfaces, but they should be carefully designed. The authors highlight and discuss five key elements.

The spinal cord is the main interface between the brain and the periphery. It notably plays a central role in motor control, as spinal motoneurons activate skeletal muscles involved in voluntary movements. Yet, the spinal mechanisms underlying human movement generation have not been completely elucidated. In this regard, functional magnetic resonance imaging (fMRI) represents a potential tool to probe spinal cord function non-invasively and with high spatial resolution. Nonetheless, a thorough characterization of this approach is still lacking, currently limiting its impact. Here, we aimed at systematically quantifying to which extent fMRI can reveal spinal cord activity along the rostrocaudal direction. We investigated changes in the blood oxygenation level dependent signal of the human cervical spinal cord during bimanual upper limb movements (wrist extension, wrist adduction and finger abduction) in nineteen healthy volunteers. Prior to scanning, we recorded the muscle activity associated with these movements in order to reconstruct the theoretical motor-pool output pattern using an anatomy-based mapping of the electromyographic (EMG) waveforms. EMG-derived spinal maps were characterized by distinct rostrocaudal patterns of activation, thus confirming the task-specific features of the different movements. Analogous activation patterns were captured using spinal cord fMRI. Finally, an additional fMRI dataset was acquired from a subset of the participants (n = 6) to deploy a multivoxel pattern analysis, which allowed successful decoding of movements. These combined results suggest that spinal cord fMRI can be used to image rostrocaudal activation patterns reflecting the underlying activity of the motoneuron pools innervating the task-related muscles. Spinal cord fMRI offers the prospect of a novel tool to study motor processes and potentially their modification following neurological motor disorders. [Display omitted] •fMRI was used to probe cervical functional activity during upper limb motor tasks.•Muscular recordings were employed to obtain theoretical spinal cord output.•Consistent rostrocaudal patterns were obtained using both modalities.•Motor tasks could be decoded based on the spinal activation maps.

A spinal cord injury usually spares some components of the locomotor circuitry. Deep brain stimulation (DBS) of the midbrain locomotor region and epidural electrical stimulation of the lumbar spinal cord (EES) are being used to tap into this spared circuitry to enable locomotion in humans with spinal cord injury. While appealing, the potential synergy between DBS and EES remains unknown. Here, we report the synergistic facilitation of locomotion when DBS is combined with EES in a rat model of severe contusion spinal cord injury leading to leg paralysis. However, this synergy requires high amplitudes of DBS, which triggers forced locomotion associated with stress responses. To suppress these undesired responses, we link DBS to the intention to walk, decoded from cortical activity using a robust, rapidly calibrated unsupervised learning algorithm. This contingency amplifies the supraspinal descending command while empowering the rats into volitional walking. However, the resulting improvements may not outweigh the complex technological framework necessary to establish viable therapeutic conditions. Deep brain stimulation and epidural electrical stimulation of the spinal cord enable locomotion in humans with spinal cord injury (SCI) but the potential synergy between both approaches is unclear. The authors show that a complex technological approach is required to enable volitional walking in rats with SCI.

Restoration of touch after hand amputation is a desirable feature of ideal prostheses. Here, we show that texture discrimination can be artificially provided in human subjects by implementing a neuromorphic real-time mechano-neuro-transduction (MNT), which emulates to some extent the firing dynamics of SA1 cutaneous afferents. The MNT process was used to modulate the temporal pattern of electrical spikes delivered to the human median nerve via percutaneous microstimulation in four intact subjects and via implanted intrafascicular stimulation in one transradial amputee. Both approaches allowed the subjects to reliably discriminate spatial coarseness of surfaces as confirmed also by a hybrid neural model of the median nerve. Moreover, MNT-evoked EEG activity showed physiologically plausible responses that were superimposable in time and topography to the ones elicited by a natural mechanical tactile stimulation. These findings can open up novel opportunities for sensory restoration in the next generation of neuro-prosthetic hands. Our hands provide us with a wide variety of information about our surroundings, enabling us to detect pain, temperature and pressure. Our sense of touch also allows us to interact with objects by feeling their texture and solidity. However, completely reproducing a sense of touch in artificial or prosthetic hands has proven challenging. While commercial prostheses can mimic the range of movements of natural limbs, even the latest experimental prostheses have only a limited ability to ‘feel’ the objects being manipulated. Oddo, Raspopovic et al. have now brought this ability a step closer by exploiting an artificial fingertip and appropriate neural interfaces through which different textures can be identified. The initial experiments were performed in four healthy volunteers with intact limbs. Oddo, Raspopovic et al. connected the artificial fingertip to the volunteers via an electrode inserted into a nerve in the arm. When moved over a rough surface, sensors in the fingertip produced patterns of electrical pulses that stimulated the nerve, causing the volunteers to feel like they were touching the surface. The volunteers were even able to tell the difference between the different surface textures the artificial fingertip moved across. The temporary electrodes used in this group of volunteers are unsuitable for use with prosthetic limbs because they can easily be knocked out of position. Therefore, in a further experiment involving a volunteer who had undergone an arm amputation a number of years previously, Oddo, Raspopovic et al. tested an implanted electrode array that could, in principle, remain in place long-term. This volunteer could also identify the different textures the artificial fingertip touched, with a slightly higher degree of accuracy than the previous group of intact volunteers. Further studies are now required to explore the potential of this approach in larger groups of volunteers.

The accurate teleoperation of robotic devices requires simple, yet intuitive and reliable control interfaces. However, current human–machine interfaces (HMIs) often fail to fulfill these characteristics, leading to systems requiring an intensive practice to reach a sufficient operation expertise. Here, we present a systematic methodology to identify the spontaneous gesture-based interaction strategies of naive individuals with a distant device, and to exploit this information to develop a data-driven body–machine interface (BoMI) to efficiently control this device. We applied this approach to the specific case of drone steering and derived a simple control method relying on upper-body motion. The identified BoMI allowed participants with no prior experience to rapidly master the control of both simulated and real drones, outperforming joystick users, and comparing with the control ability reached by participants using the bird-like flight simulator Birdly.