Sensor-based control strategies for integration of epidural electrical stimulation in rehabilitation after spinal cord injury

Spinal Cord Injury (SCI) results in damage to neural circuitry connecting the brain to the periphery. Consequently, sensory and motor function is lost, to varying degree, depending on the site and severity of the lesion. More than half of spinal cord injuries result in paraplegia, a partial or complete paralysis of the lower limbs. Despite the emergence of therapeutic interventions in recent decades, outcomes have been incremental, and few have transformed SCI rehabilitation.Epidural electrical stimulation (EES) stimulates paralyzed muscles. Anatomically intact neural circuits below the lesion can be reactivated to a functional state by stimulating afferent fibers in the dorsal roots. Recently, EES enabled independent stepping and improved functional recovery during activity-based rehabilitation. Neuroplasticity, the formation of new functional pathways based on neural activity above and below the lesion, allows for motor control recovery. However, it took months of individualized rehabilitation training to see functional recovery, making widespread application difficult.For the first time in humans, we use biomimetic EES to deliver temporal sequences of spatially selective stimulation trains to the spinal cord. During rehabilitation exercises, we target specific muscle groups to mimic a natural afferent firing pattern. We improve simultaneous descending and ascending activity by using closed-loop motor-intent sensors. We propose three goals to drive clinical translation of biomimetic EES.We first enable EES for locomotor training and outside of a dedicated research environment. Following a systematic review of gait-detection algorithms, we developed a novel neurostimulation platform the functions in closed-loop using inertial measurement units. We further designed a platform intended for independent use of stimulation by the patient. We investigated clinical outcomes of biomimetic EES applied during locomotor rehabilitation and found that performance improved with stimulation and after a few months, participants regained voluntary control over previously paralysed muscles without stimulation.Then we extend EES to patients with trunk impairments and complete leg motor loss. We modified our closed-loop paradigms for people with motor complete SCI and created an electrode lead that covers the thoracic spine. We evaluated biomimetic EES in complete SCI patients during locomotor and trunk rehabilitation. Three participants could walk and control trunk movements after neurorehabilitation.Finally, we incorporate EES into rehabilitation activities across the continuum-of-care. We created a graphical user interface for clinicians to easily create activity-specific simulation programs and synchronized stimulation with common robotic devices. Then we evaluated EES's clinical benefit in conventional and robotic rehab. Individuals could immediately stand, walk, cycle, swim, and move their trunks. Using EES and rehabilitation robotics restored physiologically relevant muscle activity and allowed for more challenging training. Recreational rehabilitation activities, that were otherwise inaccessible, could be performed.Biomimetic EES optimizes conditions for neuroplasticity and shows promise of delivering clinically relevant outcomes in a short timeframe. We thus bring the intervention closer to a clinical reality, offering a new hope to the SCI community.

Reverse engineering the motor control system

Berat Denizdurduran

Mammalian motor control is implemented by a combination of different networks and system, working coherently to plan the movement of the body or a limb and to execute this movement a dynamical environment. While it is believed that complex, voluntary movements are planned in the motor areas of the cortex, the execution of the movement is controlled by a combination of cortical and cerebellar networks together with central pattern generators and reflex circuits in the spinal cord. In this thesis, we propose an abstract model that captures the basic properties of mammalian motor control, using the example of two different movements: arm movements as well as locomotion. Our model consists of three parts: first a high-level network, that learns movements, using a combination of actor-critic based reinforcement learning and Optimal Control theory. The second part corresponds to spinal reflex circuits that execute basic movements of the musculo-skeletal system. They are modeled by a simple neural network, that learns the dynamic properties of the muscoloskeletal system by the mechanism of spontaneous motor activity (muscle twithing) combined with a Hebbian learning rule. We demonstrate that this network can learn the antagonistic control of joint movements. The final part of the model is a cerebellar network, that translates a complex movement trajectory, such as reaching, and activates the spinal reflex circuits to execute the movement. The mapping between the cerebellar neurons and the spinal reflex circuits are trained with artificial neural networks. Using a musculoskeletal arm model, we demonstrate that the proposed neural motor control model can generate the movement to arbitrary goal position.

EPFL2019

Uncovering the neural encoding of behavior in the adult Drosophila motor system

Chin-Lin Chen

One of the most important goals in neuroscience research has always been to understand how animals control their behavior. However, the long focus on the role of brain neurons in behavioral control might be missing the full story. In fact, brain-wide fluctuations in neural activity are highly correlated to the behavioral state, suggesting a closed-loop system where the behavioral command and the body state interact to interpret sensory inputs and dynamically guide action selection. However, to date, little is known about how and where information about the behavioral state is generated. In principle, information about the behavioral state is a relatively high-level encoding of the body state derived from the integration of signals from primary sensors and the local network. Local motor circuits residing in the spinal cord of mammals or the ventral nerve cord (VNC) of insects may give rise to the behavioral state and pass to the brain via brain-projecting interneurons---termed ascending neurons (ANs). However, what information ANs carry and where this information originates is still a mystery due to the technical challenges in measuring these signals in behaving animals. Therefore, it is critical to develop tools to track neuron identity and record neuronal activity during behavior to crack the functional network. In this work, a method is introduced for imaging the motor circuit in the VNC of Drosophila melanogaster. This preparation demonstrates how to retain fly limb function after dissection and allows simultaneous recording of a tethered fly's behavior and its neural activity in the VNC. Incorporating this methodology with other approaches can verify the conclusions from the experiments such as those manipulating neuronal excitability, and motivate further investigation into neuronal function in specific types of behavior. Since it has long been theorized that ANs are the cellular basis of the behavioral state information sent in the brain, the technique introduced in this work was designed to resolve questions relating to what type of information ANs convey. Here, a large-scale functional screen was performed to relate neural activity, behavior, and neuronal morphology for 247 genetically identified ANs in the adult fly. This is the first large-scale screen of neural activity in a behaving animal, and this work uncovered three fundamental features of AN populations. First, rather than low-level limb movements, ANs encode high-level behaviors like walking, grooming, and resting. Second, ANs project nearly exclusively to an integrative sensory center (AVLP) and an action selection hub (GNG). Remarkably, the encodings projected to these distinct areas reflect their potential roles in contextualizing sensory cues (AVLP) and guiding future actions (GNG). Third, the behavioral encodings of ANs are closely linked to their motor system patterning. In conclusion, this work provides an indispensable tool to study the motor system beyond the brain network by overcoming a major technical barrier in in-vivo recording of the VNC of a behaving fly. This tool was used to help resolve what is encoded by ANs as a higher-level behavioral state, suggesting that the primary cellular origin of the behavioral information that is used to modulate brain function comes from the VNC. Overall, the technique and the findings on AN encoding should inspire follow-up studies to uncover additional neural mechanisms used in adaptive behavior generation.

EPFL2022

Neuroprosthetic rehabilitation and translational mechanism after severe spinal cord injury

Lucia Florinda Friedli Wittler

Traumatic SCIs have long-term health, economic and social consequences, stressing the urgency to develop interventions to improve recovery after such injuries. Today, the only proven effective interventions to enhance recovery after SCI are activity-based rehabilitation therapies, such as locomotor training. However, locomotor training shows no or very limited efficacy to improve function after a severe SCI that induces paralysis of the limbs. To mimic the outcome of severe but incomplete SCI in rodents, we developed a model of double opposite-side lateral hemisections termed staggered hemisection in adult rats. This model induced permanent paralysis below the level of injury but leaves an intervening gap of intact neural tissue that provides a substrate for recovery. We showed that this SCI leads to degradation of motor functions, which correlates with the formation of aberrant neuronal connections below the lesion. Robotic devices with a rehabilitative purpose should act as propulsive or postural neuroprosthesis allowing training under natural conditions. Our versatile robotic interface provides multidirectional bodyweight support during overground locomotion in rats. We next evaluated the effects of robot-assisted gait training enabled by electrochemical stimulation of spinal circuits to restore locomotion after staggered hemisection SCI. We found that after two months of daily training, paralyzed rats recovered the ability to initiate, sustain and adjust bipedal locomotion while supported in the robot under electrochemical stimulation. This recovery correlated with ubiquitous reorganization of corticospinal, brainstem, and intraspinal fibers. We next evaluated whether this treatment was capable of restoring supraspinal control of locomotion after a clinically relevant SCI. Rats received a severe contusion of the spinal cord that spared less than 10% of intact tissue. Robot-assisted rehabilitation restored weight-bearing locomotion in all the trained rats when stimulated electrochemicallay and in a subset of rats in the absence of any enabling factors which paralelled with the reorganization of axonal projections of reticulospinal fibers below the contusion. Virus-mediated silencing of reticulospinal neurons projecting to lumbar segments demonstrated that these inputs were necessary to initiate and sustain walking after training. When delaying the onset of training by two months, in the chronic stage, all the rats regained voluntary locomotor movements but the extent of the recovery was reduced compared to rats trained early after SCI. The results provide a strong rationale to evaluate the impact of neuroprosthetic training to improve motor functions in human patients with incomplete SCI. Translation of treatment paradigms developed in rodent models into effective clinical applications remains a major challenge in biomedical research. Here, we studied recovery of motor functions in more than 400 quadriplegic patients who presented various degree of spinal cord damage laterality. We found that recovery increases with the asymmetry of early motor deficits. We conclude that emergence of spinal cord decussating corticospinal fibers and bilateral motor cortex projections during mammalian evolution supports greater recovery after lateralized SCI primates compared to rodents. Novel experimental models and dedicated therapeutic strategies are necessary to take advantage of this powerful neuronal substrate for recovery after SCI.