Bokkyu Kim, PT, PhD
Brain Structural Plasticity
Motor Control and Motor Learning
ASSOCIATIONS / MEMBERSHIPS
My research team aims to expand our knowledge of the brain-behavior relationship in humans. This new knowledge regarding the relationship between human brain and motor behavior will help us to develop a more effective neurorehabilitation interventions for people with neurological conditions, such as stroke.
Research project 1. Transcranial Photobiomodulation (tPBM) treatment - Illuminating the brain to enhance motor skill learning
Transcranial photobiomodulation (tPBM) treatment using near-infrared light-emitting diodes (NIR LED) is a relatively new non-invasive brain stimulation method that is proven to be safe and effective to induce functional activation changes of cerebral cortex in humans. Several basic and clinical studies have shown that the irradiation of NIR light on the scalp can deliver the light to the cerebral cortex and the NIR light can increase the functional activation of the cerebral cortex. Further, several clinical studies have shown that the NIR light irradiation on the scalp can promote behavioral recovery in people with neurological conditions, such as stroke or traumatic brain injury. What remains lacking, however, is knowledge of the effectiveness of tPBM treatment on upper extremity motor performance and the neural mechanism underlying the effects of tPBM treatment on motor performance improvement in chronic stroke survivors. There is, therefore, a critical need to define the neural mechanisms and therapeutic efficacy of tPBM on motor performance improvement in chronic stroke survivors. Without such information, the promise of this new class of non-invasive brain stimulation treatment for post-stroke individuals will likely remain limited.
Our long-term goal is to help develop a low-cost, easy-to-apply, and safe non-invasive brain stimulation treatment in conjunction with stroke rehabilitation interventions of sensorimotor deficits in chronic stroke survivors (in clinical and home-based rehabilitation settings). Our overall objective in this application, which is the next step toward the attainment of our long-term goal, is to elucidate the neural mechanism(s) underlying transcranial photobiomodulation (tPBM) treatment for improvement in UE motor performance in chronic stroke survivors.
Research project 2. Fine hand motor skill learning and experience-dependent brain plasticity
Motor skill learning is a result of motor skill practice. Underlying neural mechanisms of motor skill learning have been studied for decades. Current neuroscientific evidence supports that experience-dependent neuroplasticity of the sensorimotor brain regions is the key neural mechanism of motor skill learning. My research team aims to determine how brain structure and function change after an intensive long-term upper extremity motor skill practice using MRI and neurophysiological assessments. Specifically, I am interested in the changes in brain structural connectivity of the sensorimotor brain regions in both non-disabled adults and people with stroke. I utilize graph theory-based brain network analysis to investigate how brain structure and functional connectivity changes are associated with upper extremity motor skill learning. Furthermore, we use neurophysiological assessment, such as electroencephalogram (EEG) and transcranial magnetic stimulation (TMS) to investigate how brain functional connectivity will affect improvement in motor behavior after an intensive upper extremity motor skill training in non-disabled adults and stroke survivors. This research will determine the neural mechanisms underlying fine hand motor skill learning that would inform us to develop a better interventions promoting the experience-dependent brain plasticity that can lead to further improvement in motor function after stroke.
In this project, our research team is using the chopsticks task as a fine hand motor skill task. This task is requiring high level of hand dexterity for dynamic control of several fingers to manipulate a pair of chopsticks to pick up a small object. We are developing a clinical motor outcome measure using chopsticks. Further, we use this task to practice in order to induce experience dependent brain plasticity.
Research project 3. Effects of task conditions on upper extremity kinematics and compensatory movement strategies in chronic stroke survivors
Motor compensation is a learned motor behavior commonly observed in chronic stroke survivors. Due to the motor impairment, chronic stroke survivors develop movement strategies to substitute the motor impairment. Based on dynamic systems theory of motor control, individual, environmental, and task factors contribute to the choice of movement control strategies in chronic stroke survivors. We compare upper extremity kinematics and compensatory trunk movements during goal-directed arm reaches in different task conditions. Further, we also investigate how the environmental constraints can influence the arm reaching kinematics and trunk compensation.
Research project 4. Machine learning-based prediction modeling for motor recovery after stroke
My research aims to develop a robust and accurate predictive model for post-stroke sensorimotor recovery using clinical outcome scores and neurological biomarkers derived from neuroimaging assessments. It is estimated that direct and indirect costs of stroke healthcare in 2030 will be about $ 70 billion dollars. Best way to reducing the stroke healthcare costs is to develop an accurate prognosis of sensorimotor recovery after stroke. Improving prognosis of sensorimotor recovery will help therapists to set realistic and achievable rehabilitation goals and to choose the most optimal therapeutic approach for each post-stroke individual. Thus, my research will contribute to reducing costs of stroke healthcare. Prognosis research project is about the development of multimodal prediction model for functional recovery after stroke using machine learning technique. Advance in information techonolgy and computer science allows us to generate data depositories for stroke rehabilitaiton research. Currently, there are several stroke rehabilitation research data depositories that contain more than 300 stroke survivors' brain MRIs and clinical motor outcome assessments data. Using big data of stroke rehabilitation research, I will be able to develop a more accurate multimodal prediction model using machine learning-based multivariate modeling approaches. Results from this project will be used for developing new hypotheses and writing extramural research grants.