School of Computational Science and Enginnering Seminar with Vince D. Calhoun, president of Mind Research Network and a distinguished professor in the Department of Electrical and Computer Engineering at the University of Mexico

Talk Title: Maximizing Information in Brain Imaging Studies: Dynamics, Prediction, Data-fusion, Deep Learning, and Data Capture 

Abstract: 
Brain imaging studies typically involve a relatively small number of experiments, each of which produce large amounts of noisy, high-dimensional data which are often analyzed in isolation or using relatively simple models. In this talk I will share our experience with approaches that can capture and aggregate large data sets, discuss some of the challenges and success in the use of brain imaging data to perform single-subject prediction of, e.g. disease, treatment outcome, or cognitive scores, and discuss several ways to mine available data to learn more about the brain including dynamic connectivity, data-fusion, and deep-learning.  I will share results from a variety of studies and provide evidence that we can learn more from available data by approaching it in new ways. I will also discuss neuroinformatics tools for capturing, managing, and sharing data, including within a decentralized context. 

Biography: 
Vince D. Calhoun is currently President of the Mind Research Network and a Distinguished Professor in the Department of Electrical and Computer Engineering at the University of New Mexico. He is transitioning to Atlanta to direct the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) as a Georgia Research Alliance eminent scholar where he holds an appointment at Georgia State University as a Distinguished University Professor as well as appointments at Georgia Institution of Technology, and Emory University. He is the author of more than 650 full journal articles and over 750 technical reports, abstracts and conference proceedings. His work includes the development of flexible methods to analyze functional magnetic resonance imaging data such as independent component analysis (ICA), deep learning for neuroimaging, data fusion of multimodal imaging and genetics data, neuroinformatics tools, and the identification of biomarkers for disease. He leads an NIH P20 COBRE center grant on multimodal imaging of schizophrenia, bipolar disorder, and major depression as well as an NSF EPSCoR grant focused on brain imaging and epigenetics of adolescent development in addition to multiple NIH R01 grants. Dr. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers, The American Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, The American College of Neuropsychopharmacology, and the International Society of Magnetic Resonance in Medicine.