Our overall research objectives are to better understand the neural bases of human motor control, motor disorders, and spatial learning and memory. Our approach is to study both normal human performance and its breakdown following dysfunction of specific brain systems. We develop and use contemporary technologies for 3D motion analysis, robotics, and immersive virtual reality in conjunction with noninvasive brain imaging (EEG, fMRI, combined EEG-fMRI). We have developed unique capabilities for simultaneously recording movements of the limbs, body, head, and eyes and EEG while subjects interact in large-scale immersive virtual environments.
We are using these novel technologies to examine the nature of the sensorimotor deficits in Parkinson's disease and the effects of drug versus surgical therapies in ameliorating these deficits. Our goal is to better understand the functional roles of basal ganglia-cortical circuits in motor control and sensorimotor learning, and, in the process, to provide quantitative, objective assessments of motor dysfunction and specific effects of therapies.
Brain Processes Underlying Spatial Learning and Memory
We are studying a type of learning referred to as unsupervised learning, in which an individual builds up internal representations of the environment through self-exploration, without explicit feedback or instruction. In one project, we are combining immersive virtual reality, motion capture, and simultaneously recorded EEG to examine neural mapping of space in humans, decision making, and the cortical dynamics underlying motor control. These technological developments open up entirely new possibilities for investigating the cortical substrates of cognition.
Lee, D., Henriques, D.Y.P., Snider, J., Song, D.D., Poizner, H. (2013). Reaching to kinesthetically defined targets in Parkinson’s disease: Effects of deep brain stimulation therapy, Neuroscience, 244, 99-112. (Article featured on the cover).
Snider, J., Plank, M., Lee, D.,Poizner, H. (2013). Simultaneous neural and movement recordings in large-scale immersive virtual environments, IEEE Transactions on Biomedical Circuits and Systems, Epub before print.
Poizner H., Lancaster, J., Tunik, E., Narayana, S., Franklin, C., Rogers, W., Li, X., Fox, P.T., Robin, D.A. (2012). Towards a healthy human model of neural disorders of movement, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20, 853-857.
Torres, E., Heilman, K.M.,Poizner, H. (2011). Impaired endogenously evoked automated reaching in Parkinson's disease, Journal of Neuroscience, 31, 17848-1786.
Peterson, D., Lotz, D., Halgren, E., Sejnowski, T.J.,Poizner, H. (2011). Decision making modulates neural dynamics and prediction error processing during rewarded learning, Neuroimage, 54, 1385-1394.
Torres, E., Raymer, A., Gonzalez-Rothi, L.J., Heilman, K.M.,Poizner, H. (2010). Sensory-spatial transformations in the left posterior parietal cortex may contribute to reach timing, Journal of Neurophysiology, 104, 2375-2388.