Data-Driven Analysis and Fusion of Medical Imaging Data

Data-driven methods such as independent component analysis (ICA) have proven quite effective for the analysis of functional magnetic resonance (fMRI) data and for discovering associations between fMRI and other medical imaging data types such as electroencephalography (EEG) and structural MRI data. Without imposing strong modeling assumptions, these methods efficiently take advantage of the multivariate nature of fMRI data and are particularly attractive for use in cognitive paradigms where detailed a priori models of brain activity are not available.

This talk reviews major data-driven methods that have been successfully applied to fMRI analysis and fusion, and presents examples of their successful application for studying brain function in both healthy individuals and those suffering from mental disorders such as schizophrenia.

Tülay Adalı received the Ph.D. degree in electrical engineering from
North Carolina State University, Raleigh, in 1992 and joined the faculty at
the University of Maryland Baltimore County (UMBC), Baltimore, the same
year where she currently is a Professor in the Department of Computer Science
and Electrical Engineering.

Prof. Adali is a Fellow of the IEEE and the AIMBE, and the recipient of a 2010 IEEE
Signal Processing Society Best Paper Award and an NSF CAREER Award.
She is a Distinguished Lecturer of the IEEE Signal Processing Society for
2012 and 2013. Her research interests are in the areas of statistical signal
processing, machine learning for signal processing, and biomedical data analysis.