RESEARCH

 
 

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The ability to coordinate the movement and stabilization of the many joints in the upper extremity is critically important for many tasks of daily living. Consider the challenge of bringing a spoonful of soup to your mouth. An inability to stabilize the hand as it moves would defeat the task and diminish independent living. Our multidisciplinary team of biomedical engineers, clinicians and neuroscientists seek to understand how the brain uses sensory information to control motion of the arms and hands. By understanding how the sensorimotor control systems degrade due to neurodevelopmental disorders or following stroke, we hope to provide clinicians the knowledge and tools needed to develop and deliver individualized therapies that optimize motor performance and quality of life for their patients.

The following list samples ongoing projects in the NeuroMotor Control Lab:

Coordination and Discoordination of Limb Posture and Movement Following Stroke

This project analyzes disordered control of posture and movement during reaching in patients with stroke resulting from middle cerebral artery occlusion. Prior studies have shown that control of posture and movement may both be impaired, and contribute importantly to disability. Our recent work has shown that normal reaching requires control of both posture and movement, that these two functions are specified by different neural mechanisms, and that accuracy is dependent on adaptation (learning) and coordination between the two. Based on our preliminary observations, we hypothesized that stroke degrades the ability to recruit and relax the balanced muscle co-contractions needed both to overcome limb postural bias associated with hypertonia and to stabilize the hand against unsteady loads across the workspace. We further hypothesized that major impairment in reaching post-stroke arises from improper scaling and timing of muscle coactivation, thus limiting the independence of arm trajectory and position control.


Using a planar robot arm and novel EMG biofeedback methods, we characterize the stroke-related changes in: 1) the ability to maintain postural stability throughout the workspace via graded coactivation of antagonist muscles; 2) the latency and developmental time course of different levels of coactivation, and 3) the use of proprioceptive feedback to counteract reflex abnormalities during regulation of limb position in the presence of mechanical perturbations. We also are examining stroke-related changes in the integration of posture and movement control. Using the planar robot we: 1) explore transfer of learning between posture and movement tasks, assessing whether coupling between the two controllers increases post-stroke, and 2) determine whether trajectory planning adapts post-stroke to account for the biomechanical effects of posture regulation.



Related Publications

  1. 1.Scheidt RA, Ghez C, Asnani S. (2011) Patterns of hypermetria and terminal co-contraction during point-to-point movements demonstrate independent action of trajectory and postural controllers. J Neurophysiol. 106(5), 2368-2382.

  2. 2.Simo LS, Ghez C, Botzer L, Scheidt RA (2011) A quantitative and standardized robotic method for the evaluation of arm proprioception after stroke. Conf Proc IEEE EMBS Soc, Boston, MA: 8227-30.

  3. 3.Conrad M, Scheidt RA, Schmit BD. (2011) Effects of wrist tendon vibration on arm tracking in people post-stroke. In press: J. Neurophysiol

  4. 4.Stoeckmann T, Sullivan K, Scheidt RA. (2009) Elastic, viscous, and mass load effects on post-stroke muscle recruitment and cocontraction during reaching: A pilot study. Phys Ther 89:1-14.

  5. 5.Scheidt RA, Ghez C (2007) Separate adaptive mechanisms for controlling trajectory and final position in reaching. J. Neurophysiol. 98: 3600–3613

  6. 6.Ghez C Scheidt RA, Heijink H (2007) Different learned coordinate frames for planning trajectories and final positions in reaching. J. Neurophysiol. 98: 3614-3626

  7. 7.Scheidt RA, Stoeckmann T (2007) Reach adaptation and final position control amid environmental uncertainty following stroke. J. Neurophysiol. 97: 2824-2836.

Visual and Proprioceptive Contributions to Motor Adaptation During Reach

Improvements in the performance of a task due to motor adaptation are likely to be dependent on a variety of feedback sources including vision and proprioceptive sensors (sensory receptors that signal the physical state of the limb: muscle spindle receptors, Golgi tendon organs and mechanoreceptors in the skin). It is not clear how the central nervous system integrates the various forms of sensory information to drive improvements in task performance, although experimental evidence has shown that motor adaptation is driven strongly by both visual and proprioceptive feedback of kinematic features of movement including the curvature and/or smoothness of reaching movements. The goal of this research is to characterize how the central nervous system combines sensory feedback of motor performance to optimize motor commands during reaching. Understanding the role of the different sensory modalities in the motor adaptation process will likely be critical to the development of new technologies intended to facilitate motor relearning and rehabilitation of patients following neural injury. Such devices should be designed based on which sensory modalities produce the best motor relearning effects.



Related Publications

  1. 1.Scheidt RA, Judkins T, Goetz-Haswell T. Visual and proprioceptive contributions to adaptation of the human reach. In Revision: J Neurophysiol.

  2. 2.Patton J, Wei Y, Bajaj P, Scheidt RA. (2012) Visuomotor learning enhanced by augmenting instantaneous trajectory error feedback during reaching. In press: PLoS One.

  3. 3.Scheidt RA, Zimbelman J, Salowitz N, Suminski A, Simo L, Houk J, Mosier KM. (2012) Remembering forward: Neural correlates of memory and prediction in human motor adaptation. NeuroImage 59: 582-600. DOI: 10.1016/j.neuroimage.2011.07.072.

  4. 4.Scheidt RA, Lillis KP, Emerson SJ. (2010) Visual, motor and attentional influences on proprioceptive discrimination between straight and curved hand paths in reaching. Exp. Brain Res. 204:239-254.

  5. 5.Scheidt RA, Conditt M, Secco EL, Mussa-Ivaldi FA (2005) Interaction of visual and proprioceptive feedback during adaptation of human reaching movements J Neurophysiol 93: 3200-13.

  6. 6.Scheidt RA, Dingwell JB, Mussa-Ivaldi FA. (2001) Learning to move amid uncertainty. J Neurophysiol 86, 971-985.

Visuospatial Guidance of Movement in High-Functioning Children with Autism

The long-term goal of this work is to test the hypotheses that children with autism exhibit dysfunction in adapting motor behaviors to conditions of changing environmental uncertainty and that such deficits have an identifiable neurophysiological basis in the memory-based prediction of future environmental states. To do so, we have brought together a multidisciplinary team with expertise in motor control, robotics, brain imaging, systems identification, computer science, autism and clinical psychology.



Related Publications

  1. 1.Salowitz NMG, Eccarius P, Karst J, Meyer A, Schohl K, Stevens S, Vaughan Van Hecke A, Scheidt RA. (2012) Brief Report: Visuo-spatial guidance of movement during gesture imitation and mirror drawing in children with autism spectrum disorders. In press: J Autism Develop Disord.

  2. 2.Scheidt RA, Zimbelman J, Salowitz N, Suminski A, Simo L, Houk J, Mosier KM. (2012) Remembering forward: Neural correlates of memory and prediction in human motor adaptation. NeuroImage 59: 582-600. DOI: 10.1016/j.neuroimage.2011.07.072.

Sensory and Motor Representations of Space During Goal-Directed Movements

An important question in the study of goal-directed movement is how the brain learns to coordinate changes within the set of highly-redundant control variables (eg. motor cortical pyramidal cells, spinal stretch reflex thresholds, muscle forces, joint torques, etc.) to produce desired changes in the low-dimensional state of a controlled element  (eg. hand kinematics and/or kinetics). To capture a target with a single point-to-point movement, the brain must not only discriminate between control variables that influence task performance from those that do not, but it must also learn how much those task-relevant control variables should change to bring about a desired performance. These two problems require 'structural' and 'parametric' learning, respectively. The long-term goal of this work is to understand how the nervous system learns the sensory and motor “maps” of space that defined and spanned by the task goals.



Related Publications

  1. 1.Salowitz NMG, Eccarius P, Karst J, Meyer A, Schohl K, Stevens S, Vaughan Van Hecke A, Scheidt RA. (2012) Brief Report: Visuo-spatial guidance of movement during gesture imitation and mirror drawing in children with autism spectrum disorders. In press: J Autism Develop Disord

  2. 2.Liu X, Mosier KM, Mussa-Ivaldi FA, Casadio M, Scheidt RA. (2011) Reorganization of finger coordination patterns during adaptation to rotation and scaling of a newly-learned sensorimotor transformation. J Neurophysiol. 105:454-473.

  3. 3.Scheidt RA, Lillis KP, Emerson SJ. (2010) Visual, motor and attentional influences on proprioceptive discrimination between straight and curved hand paths in reaching. Exp. Brain Res. 204:239-254.

  4. 4.Liu X, Scheidt RA. (2008) Contributions of online visual feedback to the learning and generalization of novel finger coordination patterns. J. Neurophysiol 99:2546-2557.

  5. 5.Mosier KM, Scheidt RA, Acosta S, Mussa-Ivaldi FA (2005) Remapping hand movements in a novel geometrical environment. J Neurophysiol. 94: 4362–4372.