3 research outputs found

    A Computational Approach for the Design of Epidural Electrical Spinal Cord Stimulation Strategies to Enable Locomotion after Spinal Cord Injury

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    Spinal cord injury (SCI) is a major cause of paralysis with currently no effective treatment. Epidural electrical stimulation (EES) of the lumbar spinal cord has been shown to restore locomotion in animal models of SCI, but has not yet reached the same level of efficacy in humans. The mechanisms through which EES promotes locomotion, and the causes underlying these inter-species differences remain largely unknown, although essential to fully exploit the therapeutic potential of this neuromodulation strategy. Here, we addressed these questions using a deductive approach based on computer simulations and hypothesis-driven experiments, and proposed complementary strategies to enhance the current efficacy of EES-based therapies. In the first part of this thesis, we studied the mechanisms through which EES enables locomotion in rat models of SCI. Performing simulations and behavioral experiments, we provided evidence that EES modulates proprioceptive afferents activity, without interfering with the ongoing sensory signals. We showed that this synergistic interaction allows muscle spindle feedback circuits to steer the unspecific excitation delivered by EES to functionally relevant pathways, thus allowing the formation of locomotor patterns. By leveraging this understanding, we developed a stimulation strategy that allowed adjusting lesion-specific gait deficits, hence increasing the therapeutic efficacy of EES. In the second part of this thesis, we evaluated the influence of trunk posture on proprioceptive feedback circuits during locomotion, and thus on the effect of EES, in rat models of SCI. By combining modeling and experiments, we showed that trunk orientation regulates leg proprioceptive signals, as well as the motor patterns produced during EES-induced stepping. We exploited these results to develop a control policy that by automatically regulating trunk orientation significantly enhanced locomotor performance. In the last part of this thesis, we investigated the causes underlying species-specific effects of EES. Hypothesis-driven simulations suggested that in humans continuous EES blocks the proprioceptive signals traveling along the recruited fibers. We corroborated this prediction by performing experiments in rats and people with SCI. In particular, we showed that EES disrupts the conscious perception of leg movements, as well as the afferent modulation of sensorimotor circuits in humans, but not in rats. We provide evidence that in humans, due to this phenomenon, continuous EES can only facilitate locomotion to a limited extent. This was insufficient to provide clinically relevant improvements in the tested participants. Finally, we proposed two sensory-compliant stimulation strategies that might overcome these limitations, and thus augment the therapeutic efficacy of EES. In this thesis we elucidated key mechanisms through which EES promotes locomotion, we exposed critical limitations of continuous EES strategies when applied to humans, and we introduced complementary strategies to maximize the efficacy of EES therapies. These findings have far-reaching implications in the development of future strategies and technologies supporting the recovery of locomotion in people with SCI using EES

    Recent Advances in Motion Analysis

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    The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application
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