Biomedical subjects

Susan J Harkema

Publications and source records attributed to Susan J Harkema.

6 recordsLinked to original sources

PubMed2005

Locomotor training progression and outcomes after incomplete spinal cord injury.

BACKGROUND AND PURPOSE: The use of locomotor training with a body-weight-support system and treadmill (BWST) and manual assistance has increased in rehabilitation. The purpose of this case report is to describe the process for retraining walking in a person with an incomplete spinal cord injury (SCI) using the BWST and transferring skills from the BWST to overground assessment and community ambulation. CASE DESCRIPTION: Following discharge from rehabilitation, a man with an incomplete SCI at C5-6 and an American Spinal Injury Association (ASIA) Impairment Scale classification of D participated in 45 sessions of locomotor training. OUTCOMES: Walking speed and independence improved from 0.19 m/s as a home ambulator using a rolling walker and a right ankle-foot orthosis to 1.01 m/s as a full-time ambulator using a cane only for community mobility. Walking activity (mean+/-SD) per 24 hours increased from 1,054+/-543 steps to 3,924+/-1,629 steps. DISCUSSION: In a person with an incomplete SCI, walking ability improved after locomotor training that used a decision-making algorithm and progression across training environments.

Activities of Daily Living↗

PubMed2004

The human spinal cord interprets velocity-dependent afferent input during stepping.

We studied the motor response to modifying the rate of application of sensory input to the human spinal cord during stepping. We measured the electromyographic (EMG), kinematic and kinetic patterns of the legs during manually assisted or unassisted stepping using body weight support on a treadmill (BWST) in eight individuals with spinal cord injury (SCI). At various treadmill speeds (0.27-1.52 m/s), we measured the EMG activity of the soleus (SOL), medial gastrocnemius (MG), tibialis anterior (TA), medial hamstrings (MH), vastus lateralis (VL), rectus femoris (RF) and iliopsoas (ILIO); the hip, knee and ankle joint angles; the amount of body weight support (BWS); and lower limb loading. The EMG amplitude and burst duration of the SOL, MG, TA, MH, VL, RF and ILIO were related to the step cycle duration during stepping using BWST. EMG mean amplitudes increased at faster treadmill speeds, and EMG burst durations shortened with decreased step cycle durations. Muscle stretch of an individual muscle could not account for the EMG amplitude modulation in response to stepping speed. The effects on the EMG amplitude and burst duration were similar in subjects with partial and no detectable supraspinal input. We propose that the human spinal cord can interpret complex step-related, velocity-dependent afferent information to contribute to the neural control of stepping.

Adolescent↗

PubMed2004

Locomotor activity in spinal cord-injured persons.

After a spinal cord injury (SCI) of the cat or rat, neuronal centers below the level of lesion exhibit plasticity that can be exploited by specific training paradigms. In individuals with complete or incomplete SCI, human spinal locomotor centers can be activated and modulated by locomotor training (facilitating stepping movements of the legs using body weight support on a treadmill to provide appropriate sensory cues). Individuals with incomplete SCI benefit from locomotor training such that they improve their ability to walk over ground. Load- or hip joint-related afferent input seems to be of crucial importance for both the generation of a locomotor pattern and the effectiveness of the training. However, it may be a critical combination of afferent signals that is needed to generate a locomotor pattern after severe SCI. Mobility of individuals after a SCI can be improved by taking advantage of the plasticity of the central nervous system and can be maintained with persistent locomotor activity. In the future, if regeneration approaches can successfully be applied in human SCI, even individuals with complete SCI may recover walking ability with locomotor training.

Afferent Pathways↗

PubMed2003

Clonus after human spinal cord injury cannot be attributed solely to recurrent muscle-tendon stretch.

Clonus, presented behaviorally as rhythmic distal joint oscillation, is a common pathology that occurs secondary to spinal cord injury (SCI) and other neurological disabilities. There are two predominant theories as to the underlying mechanism of clonus. The prevailing one is that clonus results from recurrent activation of stretch reflexes. An alternative hypothesis is that clonus results from the action of a central oscillator. We present evidence that the mechanism underlying clonus in individuals with SCI is not solely related to muscle stretch. We studied electromyography (EMG) of the soleus (SOL), medial gastrocnemius (MG), tibialis anterior (TA), medial and lateral hamstrings, vastus medialis, vastus lateralis, and rectus femoris from subjects with clinically complete and clinically incomplete SCI during stretch-induced ankle clonus, stepping, and non-weight-bearing standing. Clonic EMG of the SOL, MG, and TA occurred synchronously and were not consistently related to muscle-tendon stretch in any of the conditions studied. Further, EMG activity during stretch-induced ankle clonus, stepping, and non-weight-bearing standing had similar burst frequency, burst duration, silent period duration, and coactivation among muscles, indicating that clonic EMG patterns occurred over a wide range of kinematic and kinetic conditions, and thus proprioceptive inputs. These results suggest that the repetitive clonic bursts could not be attributable solely to immediate afferent feedback such as recurrent muscle stretch. However, these results support the theory that the interaction of central mechanisms and peripheral events may be responsible for clonus.

Adult↗

PubMed2003

A Bayesian change-point analysis of electromyographic data: detecting muscle activation patterns and associated applications.

Many facets of neuromuscular activation patterns and control can be assessed via electromyography and are important for understanding the control of locomotion. After spinal cord injury, muscle activation patterns can affect locomotor recovery. We present a novel application of reversible jump Markov chain Monte Carlo simulation to estimate activation patterns from electromyographic data. We assume the data to be a zero-mean, heteroscedastic process. The variance is explicitly modeled using a step function. The number and location of points of discontinuity, or change-points, in the step function, the inter-change-point variances, and the overall mean are jointly modeled along with the mean and variance from baseline data. The number of change-points is considered a nuisance parameter and is integrated out of the posterior distribution. Whereas current methods of detecting activation patterns are deterministic or provide only point estimates, ours provides distributional estimates of muscle activation. These estimates, in turn, are used to estimate physiologically relevant quantities such as muscle coactivity, total integrated energy, and average burst duration and to draw valid statistical inferences about these quantities.

Bayes Theorem↗

PubMedDate not supplied

Tools for understanding and optimizing robotic gait training.

This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.

Computer Simulation↗