Multivariate feature selection for fMRI analysis
Eralp Kumbasar, Dr. Tülay Adali, Department of Computer Science and Electrical Engineering.
fMRI has become a widely used imaging tool for exploring the normal neural functions as well as disordered brain functions like schizophrenia. Among all fMRI data analysis strategies, data-driven-based methods have a unique advantage of capturing the whole picture of available information since they effectively minimize assumptions imposed on the brain activity. With the increasing number of multimodal data and multisite data, the problem of balancing the computation cost and analysis performance is becoming more important than ever before. In this project, our interest is in identifying the most informative multivariate features when analyzing multiple fMRI datasets. Our goal is the development of flexible new decomposition methods as well as identifying the best feature extraction strategy for a given problem.