RÉSUMÉ
OBJECTIVE: Spinal cord injury causes a drastic loss of motor, sensory and autonomic function. The goal of this project was to investigate the use of intraspinal microstimulation (ISMS) for producing long distances of walking over ground. ISMS is an electrical stimulation method developed for restoring motor function by activating spinal networks below the level of an injury. It produces movements of the legs by stimulating the ventral horn of the lumbar enlargement using fine penetrating electrodes (≤50 µm diameter). APPROACH: In each of five adult cats (4.2-5.5 kg), ISMS was applied through 16 electrodes implanted with tips targeting lamina IX in the ventral horn bilaterally. A desktop system implemented a physiologically-based control strategy that delivered different stimulation patterns through groups of electrodes to evoke walking movements with appropriate limb kinematics and forces corresponding to swing and stance. Each cat walked over an instrumented 2.9 m walkway and limb kinematics and forces were recorded. MAIN RESULTS: Both propulsive and supportive forces were required for over-ground walking. Cumulative walking distances ranging from 609 to 835 m (longest tested) were achieved in three animals. In these three cats, the mean peak supportive force was 3.5 ± 0.6 N corresponding to full-weight-support of the hind legs, while the angular range of the hip, knee, and ankle joints were 23.1 ± 2.0°, 29.1 ± 0.2°, and 60.3 ± 5.2°, respectively. To further demonstrate the viability of ISMS for future clinical use, a prototype implantable module was successfully implemented in a subset of trials and produced comparable walking performance. SIGNIFICANCE: By activating inherent locomotor networks within the lumbosacral spinal cord, ISMS was capable of producing bilaterally coordinated and functional over-ground walking with current amplitudes <100 µA. These exciting results suggest that ISMS may be an effective intervention for restoring functional walking after spinal cord injury.
Sujet(s)
Stimulation électrique/méthodes , Microélectrodes , Moelle spinale/physiologie , Marche à pied/physiologie , Anesthésie , Animaux , Phénomènes biomécaniques , Chats , Stimulation électrique/instrumentation , Électrodes implantées , Membres/innervation , Membres/physiologie , Membre pelvien/innervation , Membre pelvien/physiologie , Locomotion/physiologie , Fatigue musculaire/physiologie , Réseau nerveux/physiologie , Traumatismes de la moelle épinière/rééducation et réadaptationRÉSUMÉ
OBJECTIVE: The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. APPROACH: In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback. MAIN RESULTS: Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. SIGNIFICANCE: Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.
Sujet(s)
Systèmes informatiques , Ganglions sensitifs des nerfs spinaux/physiologie , Marche à pied/physiologie , Adaptation physiologique/physiologie , Algorithmes , Animaux , Artéfacts , Intelligence artificielle , Phénomènes biomécaniques , Techniques de biocapteur , Chats , Stimulation électrique , Femelle , Membre pelvien/physiologie , Mâle , Modèles neurologiques , Neuroprothèses , Neurones afférents/physiologie , Traitement du signal assisté par ordinateurRÉSUMÉ
The biological central pattern generator (CPG) integrates open and closed loop control to produce over-ground walking. The goal of this study was to develop a physiologically based algorithm capable of mimicking the biological system to control multiple joints in the lower extremities for producing over-ground walking. The algorithm used state-based models of the step cycle each of which produced different stimulation patterns. Two configurations were implemented to restore over-ground walking in five adult anaesthetized cats using intramuscular stimulation (IMS) of the main hip, knee and ankle flexor and extensor muscles in the hind limbs. An open loop controller relied only on intrinsic timing while a hybrid-CPG controller added sensory feedback from force plates (representing limb loading), and accelerometers and gyroscopes (representing limb position). Stimulation applied to hind limb muscles caused extension or flexion in the hips, knees and ankles. A total of 113 walking trials were obtained across all experiments. Of these, 74 were successful in which the cats traversed 75% of the 3.5 m over-ground walkway. In these trials, the average peak step length decreased from 24.9 ± 8.4 to 21.8 ± 7.5 (normalized units) and the median number of steps per trial increased from 7 (Q1 = 6, Q3 = 9) to 9 (8, 11) with the hybrid-CPG controller. Moreover, within these trials, the hybrid-CPG controller produced more successful steps (step length ≤ 20 cm; ground reaction force ≥ 12.5% body weight) than the open loop controller: 372 of 544 steps (68%) versus 65 of 134 steps (49%), respectively. This supports our previous preliminary findings, and affirms that physiologically based hybrid-CPG approaches produce more successful stepping than open loop controllers. The algorithm provides the foundation for a neural prosthetic controller and a framework to implement more detailed control of locomotion in the future.