Posts Tagged motor disorders

[WEB SITE] Neuromotor Behavior and Neurorehabilitation Engineering Lab

Welcome

Welcome to the home page of the Neuromotor Behavior and Neurorehabilitation Engineering Lab headed by Dr. Sergei Adamovich. The focus of the lab is to study neural control of movement in health and disease, and to design, develop and test novel systems and technology-based approaches to neurorehabilitation. The long-term objective is to translate the principles of neuroscience to evidence-based interventions that can be used by clinicians to rehabilitate patients with motor disorders.  We are located in the Department of Biomedical Engineering at New Jersey Institute of Technology and in the Department of Rehabilitation and Movement Science at Rutgers University.

Current Research

Using Robotics and Virtual Reality in Stroke Rehabilitation

Dr. Adamovich, in collaboration with Dr. Alma Merians (Rutgers University),  was one of the first to initiate the use of interactive virtual environments combined with instrumented gloves and hand exoskeletons for upper extremity rehabilitation, broadening the group of people that can utilize VR and gaming technology for motor rehabilitation and incorporating adaptive algorithms, activity scaling, haptic and visual effects to target specific skill re-acquisition. The group has developed a library of  simulations that incorporated these devices. They use various modes of haptic feedback and distortions of visual feedback in virtual reality to allow patients with severe paralysis and even lack of volitional movement to begin training very early after a stroke. Central to this, they aim to study the underlying neural mechanisms that can be engaged with this type of training, whether these circuits in stroke patients are also mediating training-induced recovery, and if circuits’ integrity can be used to predict the responsiveness to the gain / mirror therapy. This work is currently supported in part by the NIH grant R01HD58301.

Brain Imaging and Stimulation

Dr. Adamovich has many years of experience studying brain representations of motor actions, the neural mechanisms of online correction of movement errors arising from visual and proprioceptive channels, and how we learn to reduce such error through anticipatory control. In collaboration with Dr. Eugene Tunik (Northeastern University),  he has addressed these questions using a combination of various technologies (TMS, fMRI, EEG, VR, robotics) and patient-based experimentation in stroke, Parkinson’s, deafferented patients and healthy individuals. His findings highlight the critical interplay between central and peripheral mechanisms of motor control and identifies important interactions among various brain areas such as the parietal, premotor and primary motor cortices in incorporating feedback into online error correction and learning. This work is currently supported by the NIH grant R01 NS085122.

 

Center for Rehabilitation Robotics   Sergei Adamovich and Richard Foulds, co-directors

This center is currently (2017) comprised of 8 projects applying robotics and virtual reality to improve the lives of individuals with disabilities.  The largest of these is an NIH project (2017-2022, $3,571,000) using a unique combination of robotics and virtual reality for neurorehabilitation of people who have arm limitations resulting from a recent stroke.  Five smaller projects on wearable robots are supported by an NIDILRR center grant (2015-2020, $4,625,000) and address lower extremity exoskeletons to restore walking by individuals with stroke, epidural electrical stimulation to increase spinal cord transmission and improve the use of exoskeletons by people with spinal cord injury, and the study of new robotic technology for stroke therapy to be used in the home. Two development projects are designing new human-robot interfaces allowing users to control exoskeletons in a biologically natural way.  An NSF grant is developing a new lower extremity exoskeleton for advanced research.  And, a translation project supported by the Parent Project Muscular Dystrophy allows the Center to equip 30 young men with Duchenne Muscular Dystrophy with NJIT-developed exoskeletons that will extend the use of their arms for up to 5 years.  The Kessler Foundation and Rutgers Department of Rehabilitation and Movement Science are major collaborators. As of November 2017, grants total $9,210,500.

Past Research

Cerebral Palsy

The major goal of this study was to demonstrate that robot-assisted VR therapy will improve clinical and biomechanical outcomes in children with cerebral palsy, that these improvements will be larger when compared to that of the conventional therapy, and that they will transfer to real world reach-to-grasp movements.  This work was supported by NIDRR grant H133EO50011, from 2005 to 2011.

 

Visit site —> Home

, , , , , , , ,

Leave a comment

[ARTICLE] Motor Learning in Stroke – Full Text

Background and Objective: Stroke rehabilitation assumes motor learning contributes to motor recovery, yet motor learning in stroke has received little systematic investigation. Here we aimed to illustrate that despite matching levels of performance on a task, a trained patient should not be considered equal to an untrained patient with less impairment. Methods: We examined motor learning in healthy control participants and groups of stroke survivors with mild-to-moderate or moderate-to-severe motor impairment. Participants performed a series of isometric contractions of the elbow flexors to navigate an on-screen cursor to different targets, and trained to perform this task over a 4-day period. The speed-accuracy trade-off function (SAF) was assessed for each group, controlling for differences in self-selected movement speeds between individuals. Results: The initial SAF for each group was proportional to their impairment. All groups were able to improve their performance through skill acquisition. Interestingly, training led the moderate-to-severe group to match the untrained (baseline) performance of the mild-to-moderate group, while the trained mild-to-moderate group matched the untrained (baseline) performance of the controls. Critically, this did not make the two groups equivalent; they differed in their capacity to improve beyond this matched performance level. Specifically, the trained groups had reached a plateau, while the untrained groups had not. Conclusions: Despite matching levels of performance on a task, a trained patient is not equal to an untrained patient with less impairment. This has important implications for decisions both on the focus of rehabilitation efforts for chronic stroke, as well as for returning to work and other activities.

Stroke is a leading cause of adult disability, leaving 30% to 66% of patients with lasting motor impairment.1,2 It has long been proposed that motor recovery following stroke is a form of relearning3,4 and that there is considerable overlap between the brain regions involved in both processes.57 However, while acquiring skill at a task may allow a patient to perform at the same level as an individual with lesser impairment, this does not necessarily make them equal. For example, well-recovered stroke patients can match the performance of healthy controls on a motor task, but differences exist in the neural networks that underlie performance for each group.8 Furthermore, matched performance does not necessarily imply that both groups have the same ability to continue improving given the opportunity for practice. These differences can complicate judgments regarding patients’ capacity to return to work and other activities,9 and which rehabilitation activities they should focus on. In this article, we propose that acquiring skill through motor training raises a similar issue—a patient who has trained on a task may “appear better,” masking categorical differences in his or her abilities. Consider two hypothetical patients—Patient A, who has mild motor impairment, and Patient B, who is more severely impaired. Patient A performs better in a movement task than Patient B. Patient B then trains at the task, reaching the same performance level as Patient A. If Patient B is now equal to Patient A, he or she should have a similar capacity for further improvement with training. If this is not the case (eg, if Patient B has reached a performance plateau beyond which further training has a limited effect), then a categorical difference remains between these patients despite their matching task performance.

In comparison to healthy individuals, stroke patients select slower voluntary movement speeds when performing movement tasks.10 As speed and accuracy are inherently linked,11 a confound arises when comparing the accuracy of movements performed at different speeds. This limitation makes it difficult to interpret previous results, such as cases where patients improve their accuracy yet decrease their speed.12 In such cases, it is impossible to determine whether a patient improved his or her ability to perform the task (through skill acquisition) or whether he or she simply changed the aspect of performance on which they focused (eg, sacrificed speed for accuracy while remaining at the same overall level of ability). The only way to disambiguate these alternatives is to first derive the speed-accuracy trade-off function (SAF13) for a given task; participants are required to complete the task in a fixed time, allowing accuracy to be measured without the confounding effects of differences in speed. Once derived, skill represents a shift in the SAF.1315

Here we introduce a serial voluntary isometric elbow force task, a modified version of the serial voluntary isometric pinch task (SVIPT). This task is based on an established laboratory-based model of motor learning in which participants learn to control a cursor by producing isometric forces.1319 In the task used in the present study, participants controlled a cursor by exerting forces with their elbow flexor muscles, allowing comparisons of performance across participants with greater ranges of impairment than would be possible with the standard (hand controlled) SVIPT paradigm. To control for differences in movement speeds across groups, performance was assessed by comparing the speed-accuracy trade-off pre and post training, using measures of task-level performance (ie, binary success/failure to complete all specified aspects of the task)1318 and trial-level measures of endpoint error and variability.20 We predicted that the severity of a participant’s motor impairment would limit his or her ability to perform the task and that training may allow him or her to achieve a similar level of performance as an individual with lesser impairment. However, we hypothesized that despite their matching performance, there would be a categorical difference between these individuals; the previously untrained participant with lesser impairment would be able to make large, rapid improvements through training, while the trained participant would not.

Figure 1. Experimental setup and procedure. (A) Participants sat with their (affected) arm supported by a robotic exoskeleton. A force transducer measured contractions of their elbow flexors. (B) On screen display. Contracting the elbow flexors moved the cursor (white circle) to the right, while relaxing moved the cursor to the home position (grey square). A “go” indicator (used in training trials) indicated to participants that they could begin a trial when ready (illustrated here as a green circle). Each trial involved navigating the cursor through the sequence Home-1–Home-2–Home-3–Home-4–Home-5. Target positions and sequence order remained fixed throughout the study. (C) Procedure. Participants first completed a pretraining skill assessment, performing the task at trial durations set by an auditory metronome (indicated by tempos presented in beats per minute—see main text for further detail). One “run” of the task involved completing 10 trials at each tempo in a pseudorandom order. This procedure was repeated to generate 2 runs of data (ie, a total of 20 trials for each tempo). Participants later trained to perform the task over consecutive days, aiming to complete the sequence as quickly and as accurately as possible. Finally, on a separate day, participants completed a posttraining skill assessment.

Continue —> Motor Learning in Stroke – Oct 27, 2016

, , , , , , , ,

Leave a comment

[Abstract] Motor Learning in Stroke. Trained Patients Are Not Equal to Untrained Patients With Less Impairment

Abstract

Background and Objective: Stroke rehabilitation assumes motor learning contributes to motor recovery, yet motor learning in stroke has received little systematic investigation. Here we aimed to illustrate that despite matching levels of performance on a task, a trained patient should not be considered equal to an untrained patient with less impairment.

Methods: We examined motor learning in healthy control participants and groups of stroke survivors with mild-to-moderate or moderate-to-severe motor impairment. Participants performed a series of isometric contractions of the elbow flexors to navigate an on-screen cursor to different targets, and trained to perform this task over a 4-day period. The speed-accuracy trade-off function (SAF) was assessed for each group, controlling for differences in self-selected movement speeds between individuals.

Results: The initial SAF for each group was proportional to their impairment. All groups were able to improve their performance through skill acquisition. Interestingly, training led the moderate-to-severe group to match the untrained (baseline) performance of the mild-to-moderate group, while the trained mild-to-moderate group matched the untrained (baseline) performance of the controls. Critically, this did not make the two groups equivalent; they differed in their capacity to improve beyond this matched performance level. Specifically, the trained groups had reached a plateau, while the untrained groups had not.

Conclusions: Despite matching levels of performance on a task, a trained patient is not equal to an untrained patient with less impairment. This has important implications for decisions both on the focus of rehabilitation efforts for chronic stroke, as well as for returning to work and other activities.

Source: Motor Learning in Stroke

, , , , , , , , , ,

Leave a comment

%d bloggers like this: