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DESCRIPTION:Click for Latest Location Information: http://smartdata2015.dataversity.net/sessionPop.cfm?confid=91&proposalid=7893\nBrain 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.
DTSTART:20150818T130000
SUMMARY:Maximizing Information in Brain Imaging Studies
DTEND:20150818T134459
LOCATION: See Description
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