Collaborative Research: SEI: Independent Component Analysis of Complex-Valued Brain Imaging Data

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Independent component analysis (ICA) has emerged as an attractive analysis tool for discovering hidden factors in observed data and has been successfully applied for data analysis in a wide array of applications such as biomedicine, communications, finance, and remote sensing. In a good number of these application domains, the data are typically complex valued. This is also the case in biomedical image analysis where ICA has been recognized as a promising tool for studying the brain function. Most biomedical image analysis techniques, however, use only the magnitude information and discard the phase, resulting in an unnecessary loss of information. Moreover, most brain imaging studies collect multiple data types where each existing modality for imaging the brain reports upon a limited domain and provides complementary information. Thus processing of imaging data in its native, complex form and by utilizing multiple modality images promises significant advances in our understanding of the brain function. We propose to develop a class of complex ICA algorithms, in particular for analysis of biomedical imaging data and demonstrate the power of joint data analysis as well as performing the analysis on the complete set of data, i.e., by utilizing both the magnitude and the phase information. We focus upon three image types, functional magnetic resonance imaging (fMRI), structural MRI (sMRI) and diffusion tensor imaging (DTI). These three imaging data provide complementary information about brain connectivity, and all can benefit from the incorporation of a complex-valued data processing approach.The broad impact of the proposed work lies in its potential to substantially impact science and information technology as well as in its educational features. Study of human brain connectivity is a very challenging and rich problem. The ICA-based fusion approach as well as the use of imaging data in its native, complex form, we believe is the key for achieving significant advances in the field. Successful demonstration of our approach for medical imaging data will also benefit other areas of science and technology where data from multiple sources and/or data in complex form need to be jointly analyzed for inferences. A significant broader impact of our proposal is to stimulate research at the interface between medical imaging and information processing by making the tools for the study of brain connectivity widely available through a toolbox and a medical imaging database.