On the Modulation of Brain Activation During Simulated Weight Bearing in Supine Gait-Like Stepping.

To date, the neurophysiological correlates of muscle activation required for weight bearing during walking are poorly understood although, a supraspinal involvement has been discussed in the literature for many years. The present study investigates the effect of simulated ground reaction forces (0, 20, and 40% of individual body weight) on brain activation in sixteen healthy participants. A magnetic resonance compatible robot was applied to render three different levels of load against the feet of the participants during active and passive gait-like stepping movements. Brain activation was analyzed by the means of voxel-wise whole brain analysis as well as by a region-of-interest analysis. A significant modulation of brain activation in sensorimotor areas by the load level could neither be demonstrated during active nor during passive stepping. These observations suggest that the regulation of muscle activation under different weight-bearing conditions during stepping occurs at the level of spinal circuitry or the brainstem rather than at the supraspinal level.

Test-retest reliability of fMRI experiments during robot-assisted active and passive stepping.

BACKGROUND: Brain activity has been shown to undergo cortical and sub-cortical functional reorganisation over the course of gait rehabilitation in patients suffering from a spinal cord injury or a stroke. These changes however, have not been completely elucidated by neuroimaging to date, mainly due to the scarcity of long-term, follow-up investigations. The magnetic resonance imaging (MRI) compatible stepper MARCOS was specifically developed to enable the investigation of the supraspinal adaptations in paretic patients undergoing gait-rehabilitation in a controlled and repeatable manner. In view of future clinical research, the present study aims at examining the test-retest reliability of functional MRI (fMRI) experiments using MARCOS. METHODS: The effect of repeated active and passive stepping movements on brain activity was investigated in 16 healthy participants from fMRI data collected in two separate imaging sessions six weeks apart. Root mean square errors (RMSE) were calculated for the metrics of motor performance. Regional overlap of brain activation between sessions, as well as an intra-class correlation coefficient (ICC) was computed from the single-subject and group activation maps for five regions of interest (ROI). RESULTS: Data from eight participants had to be excluded due to excessive head motion. Reliability of motor performance was higher during passive than active movements, as seen in 4.5- to 13-fold lower RMSE for passive movements. In contrast, ICC ranged from 0.48 to 0.72 during passive movements and from 0.77 to 0.85 during active movements. Regional overlap of activations was also higher during active than during passive movements. CONCLUSION: These findings imply that an increased variability of motor performance during active movements of healthy participants may be associated with a stable neuronal activation pattern across repeated measurements. In contrast, a stable motor performance during passive movements may be accompanied by a confined reliability of brain activation across repeated measurements.

Learning a locomotor task: with or without errors?

BACKGROUND: Robotic haptic guidance is the most commonly used robotic training strategy to reduce performance errors while training. However, research on motor learning has emphasized that errors are a fundamental neural signal that drive motor adaptation. Thus, researchers have proposed robotic therapy algorithms that amplify movement errors rather than decrease them. However, to date, no study has analyzed with precision which training strategy is the most appropriate to learn an especially simple task. METHODS: In this study, the impact of robotic training strategies that amplify or reduce errors on muscle activation and motor learning of a simple locomotor task was investigated in twenty two healthy subjects. The experiment was conducted with the MAgnetic Resonance COmpatible Stepper (MARCOS) a special robotic device developed for investigations in the MR scanner. The robot moved the dominant leg passively and the subject was requested to actively synchronize the non-dominant leg to achieve an alternating stepping-like movement. Learning with four different training strategies that reduce or amplify errors was evaluated: (i) Haptic guidance: errors were eliminated by passively moving the limbs, (ii) No guidance: no robot disturbances were presented, (iii) Error amplification: existing errors were amplified with repulsive forces, (iv) Noise disturbance: errors were evoked intentionally with a randomly-varying force disturbance on top of the no guidance strategy. Additionally, the activation of four lower limb muscles was measured by the means of surface electromyography (EMG). RESULTS: Strategies that reduce or do not amplify errors limit muscle activation during training and result in poor learning gains. Adding random disturbing forces during training seems to increase attention, and therefore improve motor learning. Error amplification seems to be the most suitable strategy for initially less skilled subjects, perhaps because subjects could better detect their errors and correct them. CONCLUSIONS: Error strategies have a great potential to evoke higher muscle activation and provoke better motor learning of simple tasks. Neuroimaging evaluation of brain regions involved in learning can provide valuable information on observed behavioral outcomes related to learning processes. The impacts of these strategies on neurological patients need further investigations.

Learning to play the violin: motor control by freezing, not freeing degrees of freedom.

Playing a violin requires precise patterns of limb coordination that are acquired over years of practice. In the present study, the authors investigated how motion at proximal arm joints influenced the precision of bow movements in novice learners and experts. The authors evaluated the performances of 11 children (4-12 years old), 3 beginning-to-advanced level adult players, and 2 adult concert violinists, using a musical work that all had mastered as their first violin piece. The authors found that learning to play the violin was not associated with a release or freeing of joint degrees of freedom. Instead, learning was characterized by an experience-dependent suppression of sagittal shoulder motion, as documented by an observed reduction in joint angular amplitude. This reduction in the amplitude of shoulder flexion-extension correlated highly with a decrease of bow-movement variability. The remaining mechanical degrees of freedom at the elbow and shoulder showed patterns of neither suppression nor freeing. Only violinists with more than 700 practice hr achieved sagittal shoulder range of motion comparable to experts. The findings imply that restricting joint amplitude at selected joint degrees of freedom, while leaving other degrees of freedom unconstrained, constitutes an appropriate strategy for learning complex, high-precision motor patterns in children and adults. The findings also highlight that mastering even seemingly simple bowing movements constitutes a prolonged learning process.

Effect of Error Augmentation on Brain Activation and Motor Learning of a Complex Locomotor Task.

Up to date, the functional gains obtained after robot-aided gait rehabilitation training are limited. Error augmenting strategies have a great potential to enhance motor learning of simple motor tasks. However, little is known about the effect of these error modulating strategies on complex tasks, such as relearning to walk after a neurologic accident. Additionally, neuroimaging evaluation of brain regions involved in learning processes could provide valuable information on behavioral outcomes. We investigated the effect of robotic training strategies that augment errors-error amplification and random force disturbance-and training without perturbations on brain activation and motor learning of a complex locomotor task. Thirty-four healthy subjects performed the experiment with a robotic stepper (MARCOS) in a 1.5 T MR scanner. The task consisted in tracking a Lissajous figure presented on a display by coordinating the legs in a gait-like movement pattern. Behavioral results showed that training without perturbations enhanced motor learning in initially less skilled subjects, while error amplification benefited better-skilled subjects. Training with error amplification, however, hampered transfer of learning. Randomly disturbing forces induced learning and promoted transfer in all subjects, probably because the unexpected forces increased subjects' attention. Functional MRI revealed main effects of training strategy and skill level during training. A main effect of training strategy was seen in brain regions typically associated with motor control and learning, such as, the basal ganglia, cerebellum, intraparietal sulcus, and angular gyrus. Especially, random disturbance and no perturbation lead to stronger brain activation in similar brain regions than error amplification. Skill-level related effects were observed in the IPS, in parts of the superior parietal lobe (SPL), i.e., precuneus, and temporal cortex. These neuroimaging findings indicate that gait-like motor learning depends on interplay between subcortical, cerebellar, and fronto-parietal brain regions. An interesting observation was the low activation observed in the brain's reward system after training with error amplification compared to training without perturbations. Our results suggest that to enhance learning of a locomotor task, errors should be augmented based on subjects' skill level. The impacts of these strategies on motor learning, brain activation, and motivation in neurological patients need further investigation.

Optimizing learning of a locomotor task: amplifying errors as needed.

Research on motor learning has emphasized that errors drive motor adaptation. Thereby, several researchers have proposed robotic training strategies that amplify movement errors rather than decrease them. In this study, the effect of different robotic training strategies that amplify errors on learning a complex locomotor task was investigated. The experiment was conducted with a one degree-of freedom robotic stepper (MARCOS). Subjects were requested to actively coordinate their legs in a desired gait-like pattern in order to track a Lissajous figure presented on a visual display. Learning with three different training strategies was evaluated: (i) No perturbation: the robot follows the subjects' movement without applying any perturbation, (ii) Error amplification: existing errors were amplified with repulsive forces proportional to errors, (iii) Noise disturbance: errors were evoked with a randomly-varying force disturbance. Results showed that training without perturbations was especially suitable for a subset of initially less-skilled subjects, while error amplification seemed to benefit more skilled subjects. Training with error amplification, however, limited transfer of learning. Random disturbing forces benefited learning and promoted transfer in all subjects, probably because it increased attention. These results suggest that learning a locomotor task can be optimized when errors are randomly evoked or amplified based on subjects' initial skill level.

Brain activation associated with active and passive lower limb stepping.

Reports about standardized and repeatable experimental procedures investigating supraspinal activation in patients with gait disorders are scarce in current neuro-imaging literature. Well-designed and executed tasks are important to gain insight into the effects of gait-rehabilitation on sensorimotor centers of the brain. The present study aims to demonstrate the feasibility of a novel imaging paradigm, combining the magnetic resonance (MR)-compatible stepping robot (MARCOS) with sparse sampling functional magnetic resonance imaging (fMRI) to measure task-related BOLD signal changes and to delineate the supraspinal contribution specific to active and passive stepping. Twenty-four healthy participants underwent fMRI during active and passive, periodic, bilateral, multi-joint, lower limb flexion and extension akin to human gait. Active and passive stepping engaged several cortical and subcortical areas of the sensorimotor network, with higher relative activation of those areas during active movement. Our results indicate that the combination of MARCOS and sparse sampling fMRI is feasible for the detection of lower limb motor related supraspinal activation. Activation of the anterior cingulate and medial frontal areas suggests motor response inhibition during passive movement in healthy participants. Our results are of relevance for understanding the neural mechanisms underlying gait in the healthy.

Non-linear adaptive controllers for an over-actuated pneumatic MR-compatible stepper.

Pneumatics is one of the few actuation principles that can be used in an MR environment, since it can produce high forces without affecting imaging quality. However, pneumatic control is challenging, due to the air high compliance and cylinders non-linearities. Furthermore, the system's properties may change for each subject. Here, we present novel control strategies that adapt to the subject's individual anatomy and needs while performing accurate periodic gait-like movements with an MRI compatible pneumatically driven robot. In subject-passive mode, an iterative learning controller (ILC) was implemented to reduce the system's periodic disturbances. To allow the subjects to intend the task by themselves, a zero-force controller minimized the interaction forces between subject and robot. To assist patients who may be too weak, an assist-as-needed controller that adapts the assistance based on online measurement of the subject's performance was designed. The controllers were experimentally tested. The ILC successfully learned to reduce the variability and tracking errors. The zero-force controller allowed subjects to step in a transparent environment. The assist-as-needed controller adapted the assistance based on individual needs, while still challenged the subjects to perform the task. The presented controllers can provide accurate pneumatic control in MR environments to allow assessments of brain activation.