Robotic training paradigms that enforce a fixed kinematic control might be suboptimal for rehabilitative training because they abolish variability, an intrinsic property of neuromuscular control (Jezernik et al., 2003). In the present study we introduce "assist-as-needed" (AAN) robotic training paradigms for rehabilitation of spinal cord injury subjects. To test the efficacy of these robotic control strategies to teach spinal mice to step, we divided 27 adult female Swiss-Webster mice randomly into three groups. Each group was trained robotically by using one of three control strategies: a fixed training trajectory (Fixed group), an AAN training paradigm without interlimb coordination (Band group), and an AAN training paradigm with bilateral hindlimb coordination (Window group). Beginning at 14 d after a complete midthoracic spinal cord transection, the mice were trained daily (10 min/d, 5 d/week) to step on a treadmill 10 min after the administration of quipazine (0.5 mg/kg), a serotonin agonist, for a period of 6 weeks. During weekly performance evaluations, the mice trained with the AAN window paradigm generally showed the highest level of recovery as measured by the number, consistency, and periodicity of steps during the testing sessions. In all three measurements there were no significant differences between the Band and the Fixed training groups. These results indicate that the window training approach, which includes loose alternating interlimb coordination, is more effective than a fixed trajectory paradigm with rigid alternating interlimb coordination or an AAN paradigm without any interlimb constraints in promoting robust postinjury stepping behavior.

For a complete adult spinal rat to regain some weight-bearing stepping capability, it appears that a sequence of specific proprioceptive inputs that are similar, but not identical, from step to step must be generated over repetitive step cycles. Furthermore, these cycles must include the activation of specific neural circuits that are intrinsic to the lumbosacral spinal cord segments. For these sensorimotor pathways to be effective in generating stepping, the spinal circuitry must be modulated to an appropriate excitability level. This level of modulation is sustained from supraspinal input in intact, but not spinal, rats. In a series of experiments with complete spinal rats, we have shown that an appropriate level of excitability of the spinal circuitry can be achieved using widely different means. For example, this modulation level can be acquired pharmacologically, via epidural electrical stimulation over specific lumbosacral spinal cord segments, and/or by use-dependent mechanisms such as step or stand training. Evidence as to how each of these treatments can "tune" the spinal circuitry to a "physiological state" that enables it to respond appropriately to proprioceptive input will be presented. We have found that each of these interventions can enable the proprioceptive input to actually control extensive details that define the dynamics of stepping over a range of speeds, loads, and directions. A series of experiments will be described that illustrate sensory control of stepping and standing after a spinal cord injury and the necessity for the "physiological state" of the spinal circuitry to be modulated within a critical window of excitability for this control to be manifested. The present findings have important consequences not only for our understanding of how the motor pattern for stepping is formed, but also for the design of rehabilitation intervention to restore lumbosacral circuit function in humans following a spinal cord injury.

In the present study, concurrent treatment with robotic step training and a serotonin agonist, quipazine, generated significant recovery of locomotor function in complete spinal cord-transected mice (T7-T9) that otherwise could not step. The extent of recovery achieved when these treatments were combined exceeded that obtained when either treatment was applied independently. We quantitatively analyzed the stepping characteristics of spinal mice after alternatively administering no training, manual training, robotic training, quipazine treatment, or a combination of robotic training with quipazine treatment, to examine the mechanisms by which training and quipazine treatment promote functional recovery. Using fast Fourier transform and principal components analysis, significant improvements in the step rhythm, step shape consistency, and number of weight-bearing steps were observed in robotically trained compared with manually trained or nontrained mice. In contrast, manual training had no effect on stepping performance, yielding no improvement compared with nontrained mice. Daily bolus quipazine treatment acutely improved the step shape consistency and number of steps executed by both robotically trained and nontrained mice, but these improvements did not persist after quipazine was withdrawn. At the dosage used (0.5 mg/kg body weight), quipazine appeared to facilitate, rather than directly generate, stepping, by enabling the spinal cord neural circuitry to process specific patterns of sensory information associated with weight-bearing stepping. Via this mechanism, quipazine treatment enhanced kinematically appropriate robotic training. When administered intermittently during an extended period of robotic training, quipazine revealed training-induced stepping improvements that were masked in the absence of the pharmacological treatment.

We hypothesized that epidural spinal cord stimulation (ES) and quipazine (a serotonergic agonist) modulates the excitability of flexor and extensor related intraspinal neural networks in qualitatively unique, but complementary, ways to facilitate locomotion in spinal cord-injured rats. To test this hypothesis, we stimulated (40 Hz) the S(1) spinal segment before and after quipazine administration (0.3 mg/kg, ip) in bipedally step-trained and nontrained, adult, complete spinal (mid-thoracic) rats. The stepping pattern of these rats was compared with control rats. At the stimulation levels used, stepping was elicited only when the hindlimbs were placed on a moving treadmill. In nontrained rats, the stepping induced by ES and quipazine administration was non-weight bearing, and the cycle period was shorter than in controls. In contrast, the stepping induced by ES and quipazine in step-trained rats was highly coordinated with clear plantar foot placement and partial weight bearing. The effect of ES and quipazine on EMG burst amplitude and duration was greater in flexor than extensor motor pools. Using fast Fourier transformation analysis of EMG bursts during ES, we observed one dominant peak at 40 Hz in the medial gastrocnemius (ankle extensor), whereas there was less of dominant spectral peak in the tibialis anterior (ankle flexor). We suggest that these frequency distributions reflect amplitude modulation of predominantly monosynaptic potentials in the extensor and predominantly polysynaptic pathways in the flexor muscle. Quipazine potentiated the amplitude of these responses. The data suggest that there are fundamental differences in the circuitry that generates flexion and extension during locomotion.

This paper emphasizes several characteristics of the neural control of locomotion that provide opportunities for developing strategies to maximize the recovery of postural and locomotor functions after a spinal cord injury (SCI). The major points of this paper are: (i) the circuitry that controls standing and stepping is extremely malleable and reflects a continuously varying combination of neurons that are activated when executing stereotypical movements; (ii) the connectivity between neurons is more accurately perceived as a functional rather than as an anatomical phenomenon; (iii) the functional connectivity that controls standing and stepping reflects the physiological state of a given assembly of synapses, where the probability of these synaptic events is not deterministic; (iv) rather, this probability can be modulated by other factors such as pharmacological agents, epidural stimulation and/or motor training; (v) the variability observed in the kinematics of consecutive steps reflects a fundamental feature of the neural control system and (vi) machine-learning theories elucidate the need to accommodate variability in developing strategies designed to enhance motor performance by motor training using robotic devices after an SCI.