Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review.

STUDY DESIGN: Systematic review of the literature. OBJECTIVES: In recent years, brain-computer interface (BCI) has emerged as a potential treatment for patients with spinal cord injury (SCI). This is the first systematic review of the literature on invasive closed-loop BCI technologies for the treatment of SCI in humans. MATERIALS AND METHODS: A comprehensive search of PubMed MEDLINE, Web of Science, and Ovid EMBASE was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: Of 8316 articles collected, 19 studies met all the inclusion criteria. Data from 21 patients were extracted from these studies. All patients sustained a cervical SCI and were treated using either a BCI with intracortical microelectrode arrays (n = 18, 85.7%) or electrocorticography (n = 3, 14.3%). To decode these neural signals, machine learning and statistical models were used: support vector machine in eight patients (38.1%), linear estimator in seven patients (33.3%), Hidden Markov Model in three patients (14.3%), and other in three patients (14.3%). As the outputs, ten patients (47.6%) underwent noninvasive functional electrical stimulation (FES) with a cuff; one (4.8%) had an invasive FES with percutaneous stimulation, and ten (47.6%) used an external device (neuroprosthesis or virtual avatar). Motor function was restored in all patients for each assigned task. Clinical outcome measures were heterogeneous across all studies. CONCLUSIONS: Invasive techniques of BCI show promise for the treatment of SCI, but there is currently no technology that can restore complete functional autonomy in patients with SCI. The current techniques and outcomes of BCI vary greatly. Because invasive BCIs are still in the early stages of development, further clinical studies should be conducted to optimize the prognosis for patients with SCI.

Neuroprosthetics: an outlook on active challenges toward clinical adoption.

Neural prostheses are designed to counter the effects of neurotrauma and restore the fundamental building blocks of human experience including motor action, sensation, and meaningful communication with other individuals. Here, we present an overview of active avenues, open questions, and debated topics in neuroprosthetics, such as targeting the mechanisms of sensorimotor recovery and designing brain interfaces for scalability. We review leading opinions in this thriving field, aiming to inform translational practice toward clinical adoption.

Gaussian-process-based Bayesian optimization for neurostimulation interventions in rats.

Effective neural stimulation requires adequate parametrization. Gaussian-process (GP)-based Bayesian optimization (BO) offers a framework to discover optimal stimulation parameters in real time. Here, we first provide a general protocol to deploy this framework in neurostimulation interventions and follow by exemplifying its use in detail. Specifically, we describe the steps to implant rats with multi-channel electrode arrays in the hindlimb motor cortex. We then detail how to utilize the GP-BO algorithm to maximize evoked target movements, measured as electromyographic responses. For complete details on the use and execution of this protocol, please refer to Bonizzato and colleagues (2023).1.

Uncovering and leveraging the return of voluntary motor programs after paralysis using a bi-cortical neuroprosthesis.

Rehabilitative and neuroprosthetic approaches after spinal cord injury (SCI) aim to reestablish voluntary control of movement. Promoting recovery requires a mechanistic understanding of the return of volition over action, but the relationship between re-emerging cortical commands and the return of locomotion is not well established. We introduced a neuroprosthesis delivering targeted bi-cortical stimulation in a clinically relevant contusive SCI model. In healthy and SCI cats, we controlled hindlimb locomotor output by tuning stimulation timing, duration, amplitude, and site. In intact cats, we unveiled a large repertoire of motor programs. After SCI, the evoked hindlimb lifts were highly stereotyped, yet effective in modulating gait and alleviating bilateral foot drag. Results suggest that the neural substrate underpinning motor recovery had traded-off selectivity for efficacy. Longitudinal tests revealed that the return of locomotion after SCI was correlated with recovery of the descending drive, which advocates for rehabilitation interventions directed at the cortical target.

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations.

Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.

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

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.

An intracortical neuroprosthesis immediately alleviates walking deficits and improves recovery of leg control after spinal cord injury.

Most rehabilitation interventions after spinal cord injury (SCI) only target the sublesional spinal networks, peripheral nerves, and muscles. However, mammalian locomotion is not a mere act of rhythmic pattern generation. Recovery of cortical control is essential for voluntary movement and modulation of gait. We developed an intracortical neuroprosthetic intervention to SCI, with the goal to condition cortical locomotor control. Neurostimulation delivered in phase coherence with ongoing locomotion immediately alleviated primary SCI deficits, such as leg dragging, in rats with incomplete SCI. Cortical neurostimulation achieved high fidelity and markedly proportional online control of leg trajectories in both healthy and SCI rats. Long-term neuroprosthetic training lastingly improved cortical control of locomotion, whereas short training held transient improvements. We performed longitudinal awake cortical motor mapping, unveiling that recovery of cortico-spinal transmission tightly parallels return of locomotor function in rats. These results advocate directly targeting the motor cortex in clinical neuroprosthetic approaches.

Multi-pronged neuromodulation intervention engages the residual motor circuitry to facilitate walking in a rat model of spinal cord injury.

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.

Hierarchical Bayesian Optimization of Spatiotemporal Neurostimulations for Targeted Motor Outputs.

The development of neurostimulation techniques to evoke motor patterns is an active area of research. It serves as a crucial experimental tool to probe computation in neural circuits, and has applications in neuroprostheses used to aid recovery of motor function after stroke or injury to the nervous system. There are two important challenges when designing algorithms to unveil and control neurostimulation-to-motor correspondences, thereby linking spatiotemporal patterns of neural stimulation to muscle activation: (1) the exploration of motor maps needs to be fast and efficient (exhaustive search is to be avoided for clinical and experimental reasons) (2) online learning needs to be flexible enough to deal with noise and occasional spurious responses. We propose a stimulation search algorithm to address these issues, and demonstrate its efficacy with experiments in the motor cortex (M1) of a non-human primate model. Our solution is a novel iterative process using Bayesian Optimization via Gaussian Processes on a hierarchy of increasingly complex signal spaces. We show that our algorithm can successfully and rapidly learn correspondences between complex stimulation patterns and evoked muscle activation patterns, where standard approaches fail. Importantly, we uncover nonlinear circuit-level computations in M1 that would have been difficult to identify using conventional mapping techniques.

Introducing a biomimetic coating for graphene neuroelectronics: towardin-vivoapplications.

Electronic micro and nano-devices are suitable tools to monitor the activity of many individual neurons over mesoscale networks. However the inorganic materials currently used in microelectronics are barely accepted by neural cells and tissues, thus limiting both the sensor lifetime and efficiency. In particular, penetrating intracortical probes face high failure rate because of a wide immune response of cells and tissues. This adverse reaction called gliosis leads to the rejection of the implanted probe after few weeks and prevent long-lasting recordings of cortical neurons. Such acceptance issue impedes the realization of many neuro-rehabilitation projects. To overcome this, graphene and related carbon-based materials have attracted a lot of interest regarding their positive impact on the adhesion and regeneration of neurons, and their ability to provide high-sensitive electronic devices, such as graphene field effect transistor (G-FET). Such devices can also be implemented on numerous suitable substrates including soft substrates to match the mechanical compliance of cells and tissues, improving further the biocompatibility of the implants. Thus, using graphene as a coating and sensing device material could significantly enhance the acceptance of intracortical probes. However, such a thin monolayer of carbon atoms could be teared off during manipulation and insertion within the brain, and could also display degradation over time. In this work, we have investigated the ability to protect graphene with a natural, biocompatible and degradable polymeric film derivated from hyaluronic acid (HA). We demonstrate that HA-based coatings can be deposited over a wide range of substrates, including intracortical probes and graphene FET arrays without altering the underlying device material, its biocompatibility and sensitivity. Moreover, we show that this coating can be monitoredin situby quantifying the number of deposited charges with the G-FET arrays. The reported graphene functionalization offers promising alternatives for improving the acceptance of various neural interfaces.

Monolayer Graphene Coating of Intracortical Probes for Long-Lasting Neural Activity Monitoring.

The invasiveness of intracortical interfaces currently used today is responsible for the formation of an intense immunoresponse and inflammatory reaction from neural cells and tissues. This leads to a high concentration of reactive glial cells around the implant site, creating a physical barrier between the neurons and the recording channels. Such a rejection of foreign analog interfaces causes neural signals to fade from recordings which become flooded by background noise after a few weeks. Despite their invasiveness, those devices are required to track single neuron activity and decode fine sensory or motor commands. In particular, such quantitative and long-lasting recordings of individual neurons are crucial during a long time period (several months) to restore essential functions of the cortex, disrupted after injuries, stroke, or neurodegenerative diseases. To overcome this limitation, graphene and related materials have attracted numerous interests, as they gather in the same material many suitable properties for interfacing living matter, such as an exceptionally high neural affinity, diffusion barrier, and high physical robustness. In this work, the neural affinity of a graphene monolayer with numerous materials commonly used in neuroprostheses is compared, and its impact on the performance and durability of intracortical probes is investigated. For that purpose, an innovative coating method to wrap 3D intracortical probes with a continuous monolayer graphene is developed. Experimental evidence demonstrate the positive impact of graphene on the bioacceptance of conventional intracortical probes, in terms of detection efficiency and tissues responses, allowing real-time samplings of motor neuron activity during 5 weeks. Since continuous graphene coatings can easily be implemented on a wide range of 3D surfaces, this study further motivates the use of graphene and related materials as it could significantly contribute to reduce the current rejection of neural probes currently used in many research areas, from fundamental neurosciences to medicine and neuroprostheses.

Brain-controlled modulation of spinal circuits improves recovery from spinal cord injury.

The delivery of brain-controlled neuromodulation therapies during motor rehabilitation may augment recovery from neurological disorders. To test this hypothesis, we conceived a brain-controlled neuromodulation therapy that combines the technical and practical features necessary to be deployed daily during gait rehabilitation. Rats received a severe spinal cord contusion that led to leg paralysis. We engineered a proportional brain-spine interface whereby cortical ensemble activity constantly determines the amplitude of spinal cord stimulation protocols promoting leg flexion during swing. After minimal calibration time and without prior training, this neural bypass enables paralyzed rats to walk overground and adjust foot clearance in order to climb a staircase. Compared to continuous spinal cord stimulation, brain-controlled stimulation accelerates and enhances the long-term recovery of locomotion. These results demonstrate the relevance of brain-controlled neuromodulation therapies to augment recovery from motor disorders, establishing important proofs-of-concept that warrant clinical studies.

Closed-loop neuromodulation of spinal sensorimotor circuits controls refined locomotion after complete spinal cord injury.

Neuromodulation of spinal sensorimotor circuits improves motor control in animal models and humans with spinal cord injury. With common neuromodulation devices, electrical stimulation parameters are tuned manually and remain constant during movement. We developed a mechanistic framework to optimize neuromodulation in real time to achieve high-fidelity control of leg kinematics during locomotion in rats. We first uncovered relationships between neuromodulation parameters and recruitment of distinct sensorimotor circuits, resulting in predictive adjustments of leg kinematics. Second, we established a technological platform with embedded control policies that integrated robust movement feedback and feed-forward control loops in real time. These developments allowed us to conceive a neuroprosthetic system that controlled a broad range of foot trajectories during continuous locomotion in paralyzed rats. Animals with complete spinal cord injury performed more than 1000 successive steps without failure, and were able to climb staircases of various heights and lengths with precision and fluidity. Beyond therapeutic potential, these findings provide a conceptual and technical framework to personalize neuromodulation treatments for other neurological disorders.

Corticospinal neuroprostheses to restore locomotion after spinal cord injury.

In this conceptual review, we highlight our strategy for, and progress in the development of corticospinal neuroprostheses for restoring locomotor functions and promoting neural repair after thoracic spinal cord injury in experimental animal models. We specifically focus on recent developments in recording and stimulating neural interfaces, decoding algorithms, extraction of real-time feedback information, and closed-loop control systems. Each of these complex neurotechnologies plays a significant role for the design of corticospinal neuroprostheses. Even more challenging is the coordinated integration of such multifaceted technologies into effective and practical neuroprosthetic systems to improve movement execution, and augment neural plasticity after injury. In this review we address our progress in rodent animal models to explore the viability of a technology-intensive strategy for recovery and repair of the damaged nervous system. The technical, practical, and regulatory hurdles that lie ahead along the path toward clinical applications are enormous - and their resolution is uncertain at this stage. However, it is imperative that the discoveries and technological developments being made across the field of neuroprosthetics do not stay in the lab, but instead reach clinical fruition at the fastest pace possible.

Restoring natural sensory feedback in real-time bidirectional hand prostheses.

Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user's intentions and the delivery of nearly "natural" sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and "life-like" quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.