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Introduction from Chair - Peter Strick, PhD
Advocate Remarks - Peter B. Sack
The Potential to Modify the Course of Parkinson’s Disease - Howard J. Federoff, MD, PhD
Parkinson’s disease is currently treated symptomatically but we are compelled to modify natural history. This presentation will feature several areas of investigation that enable discussion of disease modifying approaches. Among issues to be covered are when disease begins, the role of neuroinflammation, the impact of genetics and genomics and promising preclinical therapeutic strategies.
Brain-Machine Interfaces - Andrew Schwartz, PhD
The emphasis on neural populations as the substrate for information processing is the most important recent advance in systems neuroscience. The change in emphasis from the single neuron to the neural ensemble has made it possible to extract high-fidelity information about movements that will occur in the near future. This ability is due to the distributed nature of information processing in the brain. Neurons encode many parameters simultaneously, but the fidelity of encoding at the level of individual neurons is weak. Because encoding is a redundant parameter, representation in individual neurons is weak but consistent across the population--extraction methods based on multiple neurons are capable of generating a faithful representation of intended movement. The realization that useful information is embedded in the population has spawned the current success of brain-controlled interfaces. Since multiple movement parameters are encoded simultaneously in the same population of neurons, we have been gradually increasing the degrees of freedom (DOF) that a subject can control through the interface. Our early work showed that 3-dimensions could be controlled in a virtual reality task. We then demonstrated control of an anthropomorphic physical device with 4 DOF in a self-feeding task. Currently, monkeys in our laboratory are using this interface to control a 7-DOF arm, wrist and hand to grasp objects in different locations and orientations. Our recent data show that we can extract 11-DOF to add hand shape and dexterity to our control set.
The portion of this session given by Stephen Strittmatter is not included in this video.
Peter Strick, PhD is a Professor of Neurobiology at University of Pittsburgh. The Strick laboratory investigates 3 major topics about normal brain structure and function: (i) the organization and function of the cortical motor areas, (ii) basal ganglia and cerebellar contributions to movement, cognition and affect, and (iii) the neural basis of motor skill acquisition and retention. Ongoing experiments and findings also relate to the pathophysiology of a wide range of motor and neuropsychiatric disorders such as Parkinson’s disease, obsessive-compulsive disorder, Tourette’s syndrome, attention deficit-hyperactivity disorder, depression, and autism.
Peter B. Sack is a Parkinson’s advocate.
Howard J. Federoff, MD, PhD is Executive Vice President for Health Services and Executive Dean, School of Medicine at Georgetown University. As Executive Vice President for Health Sciences at Georgetown University and Executive Dean of the School of Medicine, Howard Federoff, MD, PhD, oversees a $250 million research and educational enterprise with $159 million in sponsored research funding in 2010. As EVP, he is responsible for advancing the educational and research missions of Georgetown University Medical Center (GUMC), and working effectively with the leadership of MedStar Health, its clinical partner. GUMC comprises a School of Medicine (founded in 1851), a School of Nursing & Health Studies, the Biomedical Graduate Research Organization (BGRO), and Lombardi Comprehensive Cancer Center.
Andrew Schwartz, PhD is a Professor of Neurobiology at University of Pittsburgh. Schwartz developed the ability to capture a high fidelity representation of movement intention from the motor cortex and has applied that work to neural cortical prosthetics. In addition to the prosthetics work, he has continued to utilize the neural trajectory representation to better understand the transformation from intended to actual movement using motor illusions in a virtual reality environment.