Relationship of eye movements and proprioception after stroke
After stroke, a common rehabilitation strategy is using vision to guide limb movement. We have recently found that many stroke survivors have difficulty using vision to guide limb position and movement during proprioceptive tasks (Semrau et al. 2018, Cortex). To better understand the integration of sensorimotor information from vision and the upper limb, we aim to investigate the relationship of eye movements and proprioceptive function of the upper limb after stroke.
Proprioceptive learning after stroke
One big challenge facing the field of stroke recovery and rehabilitation is understanding the mechanisms of recovery and relearning after stroke. While we know that rehabilitation after stroke is effective, little is known about how people learn immediately after stroke, and why rehabilitation is successful in some individuals and less successful in others. Recently, we have found that impairments in proprioceptive function are common and can be independent of motor impairment (Dukelow et al. 2010, Neurorehab Neural Repair; Semrau et al. 2013, Stroke; Semrau et al. 2015, Stroke). While we know that neurologically intact humans can easily learn new proprioceptive information, proprioceptive learning in stroke remains altogether unexamined. This projects aims to implement novel robotic learning paradigms and games to improve proprioception after stroke. Further, this project aims to use transcranial magnetic stimulation (TMS) to determine if neurostimulation can improve proprioceptive learning after stroke.
Multi-modal learning after stroke
Building upon work in proprioceptive learning, we are interested in examining modality-specific learning (i.e., visual, motor, proprioceptive) that may impact the recovery process post-stroke. We have observed that stroke survivors can have differential impairments in motor and proprioceptive function and recovery after stroke (Semrau et al. 2015, Stroke). However, it is unknown how these modality-specific learning processes are affected in the weeks and months following a stroke. Here, it is likely that stroke survivors have different profiles of learning abilities (e.g., impaired motor learning, but intact proprioceptive learning), that can significantly affect recovery and functional outcome after stroke. For this project, it is our goal is to examine patterns of learning using robotics coupled with neuroimaging techniques (fMRI) to better understand the behavioral and neural mechanisms governing relearning after stroke. Ultimately, this information will allow us to optimize relearning and rehabilitation programs after stroke and other neurologic injuries.
Robotics for rehabilitation
In the field of neurorehabilitation, it is often thought that more rehabilitation is better. However, little research has examined the effects of training specific sensorimotor functions (e.g., motor, visual, proprioceptive) with innovative technology, such as robotics. One of the long-term goals of the lab is to be able to both identify (via robotic assessment) and train (via robotic treatment) modality-specific impairments. Here, we hope to achieve more complete recovery for stroke survivors through highly targeted, personalized robotic rehabilitation.