Jack DiGiovanna

Restoring Voluntary Control of Locomotion after Paralyzing Spinal Cord Injury

Regaining Limb Movement Despite many years of intensive research, there is still an urgent need for novel treatments to help patients restore motor function after spinal cord injuries. van den Brand et al. (p. 1182) produced left and right hemisections at different levels of the rat thoracic spinal cord to cause complete hind limb paralysis mimicking the situation in humans with spinal cord injury. Systemic application of pharmacological agents, combined with a multisystem rehabilitation program including a robotic postural neuroprosthesis, restored voluntary movements of both hind limbs. A rehabilitation program involving robotic neuroprosthetics restores previously paralyzed hindlimb function. Half of human spinal cord injuries lead to chronic paralysis. Here, we introduce an electrochemical neuroprosthesis and a robotic postural interface designed to encourage supraspinally mediated movements in rats with paralyzing lesions. Despite the interruption of direct supraspinal pathways, the cortex regained the capacity to transform contextual information into task-specific commands to execute refined locomotion. This recovery relied on the extensive remodeling of cortical projections, including the formation of brainstem and intraspinal relays that restored qualitative control over electrochemically enabled lumbosacral circuitries. Automated treadmill-restricted training, which did not engage cortical neurons, failed to promote translesional plasticity and recovery. By encouraging active participation under functional states, our training paradigm triggered a cortex-dependent recovery that may improve function after similar injuries in humans.

Coadaptive Brain–Machine Interface via Reinforcement Learning

This paper introduces and demonstrates a novel brain-machine interface (BMI) architecture based on the concepts of reinforcement learning (RL), coadaptation, and shaping. RL allows the BMI control algorithm to learn to complete tasks from interactions with the environment, rather than an explicit training signal. Coadaption enables continuous, synergistic adaptation between the BMI control algorithm and BMI user working in changing environments. Shaping is designed to reduce the learning curve for BMI users attempting to control a prosthetic. Here, we present the theory and in vivo experimental paradigm to illustrate how this BMI learns to complete a reaching task using a prosthetic arm in a 3-D workspace based on the users neuronal activity. This semisupervised learning framework does not require user movements. We quantify BMI performance in closed-loop brain control over six to ten days for three rats as a function of increasing task difficulty. All three subjects coadapted with their BMI control algorithms to control the prosthetic significantly above chance at each level of difficulty.

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

Closed-loop neuromodulation of spinal sensorimotor circuits allows high-fidelity control over leg movements in paralyzed rats. Closing the Loop on Neuroprosthetic Control Patients with spinal cord injury (SCI) and paralysis may soon be outfitted with so-called neuromodulation devices, which electrically stimulate the brain or spinal cord, causing movement in the legs. Currently, tuning such modulation requires constant observation and patient-specific adjustments, which are not ideal for fluid movement or for broad translation of these technologies to injured patients. In response, Wenger et al. have created a closed-loop system that will essentially “auto-tune” the device, allowing the paralyzed patient—or, in their study, the paralyzed rat—to move freely, without worrying about adjusting electrical pulse width, amplitude, or frequency. The authors gave rats complete SCI epidural electrical stimulation and then mapped their leg movements and sensorimotor responses while in a body support system, walking upright (bipedal) on a treadmill, or climbing stairs. From this information, they devised a computational system that integrated feedback and feed-forward models for closed-loop, continuous control of leg movement and, in turn, a more natural locomotion. Closed-loop neuromodulation of spinal circuits could impart fluid motor control and prevent fatigue when rehabilitating patients with SCI. 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.

Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury

Electrical neuromodulation of lumbar segments improves motor control after spinal cord injury in animal models and humans. However, the physiological principles underlying the effect of this intervention remain poorly understood, which has limited the therapeutic approach to continuous stimulation applied to restricted spinal cord locations. Here we developed stimulation protocols that reproduce the natural dynamics of motoneuron activation during locomotion. For this, we computed the spatiotemporal activation pattern of muscle synergies during locomotion in healthy rats. Computer simulations identified optimal electrode locations to target each synergy through the recruitment of proprioceptive feedback circuits. This framework steered the design of spatially selective spinal implants and real-time control software that modulate extensor and flexor synergies with precise temporal resolution. Spatiotemporal neuromodulation therapies improved gait quality, weight-bearing capacity, endurance and skilled locomotion in several rodent models of spinal cord injury. These new concepts are directly translatable to strategies to improve motor control in humans.

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.

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.

Brain–machine interface: closer to therapeutic reality?

Keywords: brain-machine interface ; neuroprosthetics ; neuromotor disorders ; rehabilitation Reference EPFL-ARTICLE-182818doi:10.1016/S0140-6736(12)62164-3View record in Web of Science URL: http://www.sciencedirect.com/science/article/pii/S0140673612621643 Record created on 2012-12-17, modified on 2017-05-12

Towards Real-Time Distributed Signal Modeling for Brain-Machine Interfaces

New architectures for Brain-Machine Interface communication and control use mixture models for expanding rehabilitation capabilities of disabled patients. Here we present and test a dynamic data-driven (BMI) Brain-Machine Interface architecture that relies on multiple pairs of forward-inverse models to predict, control, and learn the trajectories of a robotic arm in a real-time closed-loop system. A method of window-RLS was used to compute the forward-inverse model pairs in real-time and a model switching mechanism based on reinforcement learning was used to test the ability to map neural activity to elementary behaviors. The architectures were tested with in vivodata and implemented using remote computing resources.

Cyber-Workstation for Computational Neuroscience

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.

Improved linear BMI systems via population averaging.

We investigate population averaging as a preprocessing stage for linear FIR BMIs. Population averaging is a biologically-inspired technique based on spatial constraints and neuronal correlation. We achieve a statistically significant improvement in accuracy while substantially (45%) reducing model parameters. Further analysis is performed to show that population averaging improves model accuracy by reducing variance in estimating the firing rate from spike bins. However, we find that population averaging provides a greater accuracy improvement than other groupings which also reduce firing rate variance. Our results suggest that appropriate spatial organization of neural signals enhances BMI performance