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Title: Supervised modeling of multiple networks for multimodal neuroimaging data Authors:  Sharmistha Guha - Texas A&M University (United States) [presenting]
Abstract: Novel Bayesian methodologies are developed to combine information across multiple data sources to better characterize complex physical and biological systems that are inadequately explained by considering one data source at a time. The motivation comes from ever-growing brain-imaging data from multiple modalities or sources which can together offer an in-depth understanding of the human brain in health and disease.