Maximizing Information in Brain Imaging Studies

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, multimodal and multitask 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.

Vince Calhoun received a bachelor’s degree in Electrical Engineering from the University of Kansas, Lawrence, Kansas, in 1991, master’s degrees in Biomedical Engineering and Information Systems from Johns Hopkins University, Baltimore, in 1993 and 1996, respectively, and the Ph.D. degree in electrical engineering from the University of Maryland Baltimore County, Baltimore, in 2002. He worked as a research engineer in the psychiatric neuroimaging laboratory at Johns Hopkins from 1993 until 2002. He then served as the director of medical image analysis at the Olin Neuropsychiatry Research Center and as an associate professor at Yale University. Dr. Calhoun is currently Executive Science Officer and Director of Image Analysis and MR Research at the Mind Research Network and is a Distinguished Professor in the Departments of Electrical and Computer Engineering (primary), Biology, Computer Science, Neurosciences, and Psychiatry at the University of New Mexico. He is the author of more than 375 full journal articles and over 460 technical reports, abstracts and conference proceedings. Much of his career has been spent on the development of data driven approaches for the analysis of brain imaging data. He has won over $85 million in NSF and NIH grants on the incorporation of prior information into independent component analysis (ICA) for functional magnetic resonance imaging, data fusion of multimodal imaging and genetics data, and the identification of biomarkers for disease, and leads a P20 COBRE center grant on multimodal imaging of schizophrenia, bipolar disorder, and major depression. Dr. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers, The Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, and the International Society of Magnetic Resonance in Medicine. He is also a member and regularly attends the Organization for Human Brain Mapping, the International Society for Magnetic Resonance in Medicine, the International Congress on Schizophrenia Research, and the American College of Neuropsychopharmacology. He is also a regular grant reviewer for NIH and NSF. He has organized workshops and special sessions at multiple conferences. He is currently chair of the IEEE Machine Learning for Signal Processing (MLSP) technical committee. He is a reviewer for many journals is on the editorial board of the Brain Connectivity and Neuroimage journals and serves as Associate Editor for Journal of Neuroscience Methods and several other journals.