Why do we move to the beat? A multi-scale approach, from physical principles to brain dynamics.

Humans' ability to synchronize movement with auditory rhythms relies on motor networks, such as cortical areas, basal ganglia and the cerebellum, which also participate in rhythm perception and movement production. Current research has provided insights into the dependence of this action-perception coupling upon the entrainment of neuronal activity by external rhythms. At a physical level, advances on wearable robotics have enriched our understanding of the dynamical properties of the locomotor system showing evidence of mechanical entrainment. Here we defend the view that modelling brain and locomotor oscillatory activities as dynamical systems, at both neural and physical levels, provides a unified theoretical framework for the understanding of externally driven rhythmic entrainment of biological systems. To better understand the underlying mechanisms of this multi-level entrainment during locomotion, we review in a common framework the core questions related to the dynamic properties of biological oscillators and the neural bases of auditory-motor synchronization. Illustrations of our approach, using personalized auditory stimulation, to gait rehabilitation in Parkinson disease and to manipulation of runners' kinematics are presented.

A flexible and accurate method to estimate the mode and stability of spontaneous coordinated behaviors: The index-of-stability (IS) analysis.

Patterns of coordination result from the interaction between (at least) two oscillatory components. This interaction is typically understood by means of two variables: the mode that expresses the shape of the interaction, and the stability that is the robustness of the interaction in this mode. A potent method of investigating coordinated behaviors is to examine the extent to which patterns of coordination arise spontaneously. However, a prominent issue faced by researchers is that, to date, no standard methods exist to fairly assess the stability of spontaneous coordination. In the present study, we introduce a new method called the index-of-stability (IS) analysis. We developed this method from the phase-coupling (PC) analysis that has been traditionally used for examining locomotion-respiration coordinated systems. We compared the extents to which both methods estimate the stability of simulated coordinated behaviors. Computer-generated time series were used to simulate the coordination of two rhythmic components according to a selected mode m:n and a selected degree of stability. The IS analysis was superior to the PC analysis in estimating the stability of spontaneous coordinated behaviors, in three ways: First, the estimation of stability itself was found to be more accurate and more reliable with the IS analysis. Second, the IS analysis is not constrained by the limitations of the PC analysis. Third and last, the IS analysis offers more flexibility, and so can be adapted according to the user's needs.

Ankle voluntary movement enhancement following robotic-assisted locomotor training in spinal cord injury.

BACKGROUND: In incomplete spinal cord injury (iSCI), sensorimotor impairments result in severe limitations to ambulation. To improve walking capacity, physical therapies using robotic-assisted locomotor devices, such as the Lokomat, have been developed. Following locomotor training, an improvement in gait capabilities-characterized by increases in the over-ground walking speed and endurance-is generally observed in patients. To better understand the mechanisms underlying these improvements, we studied the effects of Lokomat training on impaired ankle voluntary movement, known to be an important limiting factor in gait for iSCI patients. METHODS: Fifteen chronic iSCI subjects performed twelve 1-hour sessions of Lokomat training over the course of a month. The voluntary movement was qualified by measuring active range of motion, maximal velocity peak and trajectory smoothness for the spastic ankle during a movement from full plantar-flexion (PF) to full dorsi-flexion (DF) at the patient's maximum speed. Dorsi- and plantar-flexor muscle strength was quantified by isometric maximal voluntary contraction (MVC). Clinical assessments were also performed using the Timed Up and Go (TUG), the 10-meter walk (10MWT) and the 6-minute walk (6MWT) tests. All evaluations were performed both before and after the training and were compared to a control group of fifteen iSCI patients. RESULTS: After the Lokomat training, the active range of motion, the maximal velocity, and the movement smoothness were significantly improved in the voluntary movement. Patients also exhibited an improvement in the MVC for their ankle dorsi- and plantar-flexor muscles. In terms of functional activity, we observed an enhancement in the mobility (TUG) and the over-ground gait velocity (10MWT) with training. Correlation tests indicated a significant relationship between ankle voluntary movement performance and the walking clinical assessments. CONCLUSIONS: The improvements of the kinematic and kinetic parameters of the ankle voluntary movement, and their correlation with the functional assessments, support the therapeutic effect of robotic-assisted locomotor training on motor impairment in chronic iSCI.

Prediction of gait recovery in spinal cord injured individuals trained with robotic gait orthosis.

BACKGROUND: Motor impairment is a major consequence of spinal cord injury (SCI). Earlier studies have shown that robotic gait orthosis (e.g., Lokomat) can improve an SCI individual's walking capacity. However, little is known about the differential responses among different individuals with SCI. The present longitudinal study sought to characterize the distinct recovery patterns of gait impairment for SCI subjects receiving Lokomat training, and to identify significant predictors for these patterns. METHODS: Forty SCI subjects with spastic hypertonia at their ankles were randomly allocated to either control or intervention groups. Subjects in the intervention group participated in twelve 1-hour Lokomat trainings over one month, while control subjects received no interventions. Walking capacity was evaluated in terms of walking speed, functional mobility, and endurance four times, i.e. baseline, 1, 2, and 4 weeks after training, using the 10-Meter-Walking, Timed-Up-and-Go, and 6-Minute-Walking tests. Growth Mixture Modeling, an analytical framework for stratifying subjects based on longitudinal changes, was used to classify subjects, based on their gait impairment recovery patterns, and to identify the effects of Lokomat training on these improvements. RESULTS: Two recovery classes (low and high walking capacity) were identified for each clinical evaluation from both the control and intervention groups. Subjects with initial high walking capacity (i.e. shorter Timed-Up-and-Go time, higher 10-Meter-Walking speed and longer 6-Minute-Walking distance) displayed significant improvements in speed and functional mobility (0.033 m/s/week and-0.41 s/week respectively); however no significant change in endurance was observed. Subjects with low walking capacity exhibited no significant improvement. The membership in these two classes-and thus prediction of the subject's gait improvement trajectory over time-could be determined by the subject's maximum voluntary torque at the ankle under both plantar-and dorsi-flexion contractions determined prior to any training. CONCLUSION: Our findings demonstrate that subjects responded to Lokomat training non-uniformly, and should potentially be grouped based on their likely recovery patterns using objective criteria. Further, we found that the subject's ankle torque can predict whether he/she would benefit most from Lokomat training prior to the therapy. These findings are clinically significant as they can help individualize therapeutic programs that maximize patient recovery while minimizing unnecessary efforts and costs.

Robotic-locomotor training as a tool to reduce neuromuscular abnormality in spinal cord injury: the application of system identification and advanced longitudinal modeling.

In this study, the effect of the LOKOMAT, a robotic-assisted locomotor training system, on the reduction of neuromuscular abnormalities associated with spasticity was examined, for the first time in the spinal cord injury (SCI) population. Twenty-three individuals with chronic incomplete SCI received 1-hour training sessions in the LOKOMAT three times per week, with up to 45 minutes of training per session; matched control group received no intervention. The neuromuscular properties of the spastic ankle were then evaluated prior to training and after 1, 2, and 4 weeks of training. A parallel-cascade system identification technique was used to determine the reflex and intrinsic stiffness of the ankle joint as a function of ankle position at each time point. The slope of the stiffness vs. joint angle curve, i.e. the modulation of stiffness with joint position, was then calculated and tracked over the four-week period. Growth Mixture Modeling (GMM), an advanced statistical method, was then used to classify subjects into subgroups based on similar trends in recovery pattern of slope over time, and Random Coefficient Regression (RCR) was used to model the recovery patterns within each subgroup. All groups showed significant reductions in both reflex and intrinsic slope over time, but subjects in classes with higher baseline values of the slope showed larger improvements over the four weeks of training. These findings suggest that LOKOMAT training may also be useful for reducing the abnormal modulation of neuromuscular properties that arises as secondary effects after SCI. This can advise clinicians as to which patients can benefit the most from LOKOMAT training prior to beginning the training. Further, this study shows that system identification and GMM/RCR can serve as powerful tools to quantify and track spasticity over time in the SCI population.

Prediction of stroke motor recovery using reflex stiffness measures at one month.

This study characterizes the recovery patterns of motor impairment after stroke, and uses neuromuscular measures of the elbow joint at one month after the event to predict the ensuing recovery patterns over 12 months. Motor impairment was assessed using the Fugl-Meyer Assessment (FMA) of the upper extremity at various intervals after stroke. A parallel-cascade system identification technique characterized the intrinsic and reflex stiffness at various elbow angles. We then used "growth-mixture" modeling to identify three distinct recovery classes for FMA. While class 1 and class 3 subjects both started with low FMA, those in class 1 increased FMA significantly over 12-month recovery period, whereas those in class 3 presented no improvement. Class 2 subjects started with high FMA and also exhibited significant FMA improvement, but over a smaller range and at a slower recovery rate than class 1. Our results showed that the one-month reflex stiffness was able to distinguish between classes 1 and 3 even though both showed similarly low month-1 FMA. These findings demonstrate that, using reflex stiffness, we were able to accurately predict arm function recovery in stroke subjects over one year and beyond. This information is clinically significant and can be helpful in developing targeted therapeutic interventions.

The effects of locomotor training with a robotic-gait orthosis (Lokomat) on neuromuscular properties in persons with chronic SCI.

We studied the effects of robotic-assisted locomotor (LOKOMAT) training on neuromuscular abnormality associated with spasticity in persons with incomplete Spinal Cord Injury (SCI). LOKOMAT training was performed 3 days/week for 4 weeks, with up to 45 minutes of training per session. Subjects were evaluated before and after 1, 2, and 4 weeks of training, and the effects of training on the intrinsic (muscular) and reflexive components of the neuromuscular properties were quantified over the ankle range-of-motion. A linear (slope&intercept) regression was fit to the stiffness-angle curve. "Growth mixture" modeling was used to identify recovery classes for these parameters over the training period. Two distinct classes were observed. Class 1 subjects had initially higher reflex stiffness parameters (i.e., intercept and slope vs. ankle position) and reduced significantly over the training period. Class 2 subjects initially had lower reflex stiffness parameters and experienced non-significant reductions. Similar results were observed for the intrinsic stiffness intercept; however, intrinsic slope showed no significant improvement over training for either class. These findings demonstrate that LOKOMAT training is effective in reducing reflex and intrinsic stiffness (which abnormally increase in SCI) and improving the abnormal modulation of reflexes over the ankle range-of-motion.

Effect of coordination biofeedback on (re)learning preferred postural patterns in post-stroke patients.

After stroke, ankle-hip coordination during stance is characterized by changes in the postural system dynamics, specifically the disappearance of the in-phase pattern and the reduced stability of the anti-phase pattern. This study was conducted to assess the success of a coordination visual biofeedback for the (re)learning of the two preferred patterns, and to explore the effect of this treatment on postural and functional abilities. Twenty four patients were randomly assigned to one of two experimental groups or to a control group. During one month, patients from experimental groups followed a training protocol on the two preferred postural patterns using the biofeedback device. These two groups improved their in-phase coordination after the (re)learning compared with control group, and showed a related improvement of the functional independence measure. Results suggest that (re)learning the in-phase pattern is possible and seems to improve independence in poststroke patients.

Characterizing the dynamics of postural sway in humans using smoothness and regularity measures.

We investigate human postural sway velocity time series by computing two dynamical statistics quantifying the smoothness (the central tendency measure or CTM) and the regularity (the sample entropy or SampEn) of their underlying dynamics. The purpose of the study is to investigate the effect of aging and vision on the selected measures and to explore the nature of postural dynamics by performing surrogate data tests. A group of 14 young subjects was compared to a group of 11 older healthy subjects in two visual conditions: with eyes open (EO) and with eyes closed (EC). The results suggest that vision and age do not influence the two statistics of the velocity data in the same way. More specifically, the smoothness statistic is able to detect the aging effect. The regularity measure is sensitive to the visual feedback removal. In contrast with some findings in the literature, the results of the surrogate data tests indicate that the center of pressure velocity dynamics are stochastic and are not produced by a purely deterministic behavior. Finally, we discuss some potential implications of our results in terms of postural control mechanisms.

Changes in preferred postural patterns following stroke during intentional ankle/hip coordination.

We compared the spatio-temporal postural organization between stroke patients and healthy controls in a bipedal standing task where participants had to intentionally produce two specific ankle/hip coordination patterns: in-phase and anti-phase. The pattern to reproduce was visually represented by a ankle-hip Lissajous figure, and a real-time biofeedback displayed the current coordination sur-imposed to the expected coordination. Contrary to the healthy participants who were successful at reproducing the two patterns, stroke patients were unable to produce the in-phase pattern. In addition, when the anti-phase pattern was required, a reduction of stability was observed for the stroke group. The impairment of postural capacities following stroke was thus accompanied by a disappearance of one of the two preferred patterns found in healthy participants, a result that have consequences for understanding the etiology of postural pattern formation and the elaboration of rehabilitation programs.