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[Colloquium] Fusion of multi-task and multi-modal brain imaging and genetic data: An integrated approach and several examples

October 5, 2007

Watch Colloquium: 

  • Date: Friday, October 5th, 2007 
  • Time: 1 pm — 2:30 pm 
  • Place: ME 218

Vince Calhoun
Department of Electrical and Computer Engineering
Univeristy of New Mexico

Abstract: Brain imaging techniques available today provide multiple sources of complementary information. Most research studies do collect several types of brain images on the same individuals. However the vast majority of studies analyze each data set separately, and do not attempt to directly examine the inter-relationships between the different data types. Such approaches are not straightforward due to the high dimensionality of the data (tens of thousands of voxels or timepoints or genetic factors). In this talk I will present an approach for jointly analyzing brain imaging data using independent component analysis, and present examples from structural and functional MRI, Event-related potential data, and genetic data.  The examples will illustrate situations in which we learn something new by combining multiple data types which would not have been revealed in a separate analysis.

Bio: 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 is currently the Director of Image Analysis and MR Research at the MIND Institute and an Associate Professor in the Electrical and Computer Engineering Dept. at the University of New Mexico. He is the author of 69 full journal articles, over 30 technical reports, and over 110 abstracts and conference proceedings. Much of his career has been spent on the development of data driven approaches for the analysis of functional magnetic resonance imaging (fMRI) data.  He has three R01 grants from the National Institute of Health on the development of various data driven methods and multimodal data fusion approaches applied to mental illness. In addition he has an NSF grant for developing methods to analyze complex-valued imaging data.