CONTEXT: The brain is an incredibly complex highly inter-connected organ. Each existing modality for imaging the living brain can only report upon a limited domain. For example, functional imaging provides information about dynamic blood flow changes in response to a stimulus, whereas electroencephalography (EEG) provides information about the electrical activity of the brain with centimeter spatial and millisecond temporal resolution. Finally, gene array imaging can assess specific differences at the chromosomal level that are present in individuals, some of which have functional consequences. Even though all three of these modalities can easily be collected on the same set of individuals, methods for effectively combining these different types of information are still in their infancy. All of these modalities typically involve thousands of data points per subject, and thus simple correlative approaches are of very limited nature for uncovering hidden patterns and associations in these data and can easily be computationally overwhelming. ? ? INTELLECTUAL MERIT: The goal of this project is to examine associations among fMRI, EEG, and genetic variations related to healthy and abnormal brain function. We propose to develop a set of tools based on independent component analysis (ICA) that can effectively fuse the information provided by multiple imaging modalities to span a vast range of spatial and temporal scales. The tools we develop will thus maximally exploit the information provided by multiple images and thus enable significant advances in the challenging problem of the study of brain function. We will develop these tools and apply to a large data set of healthy individuals in order to explore healthy brain function. In the last two years of the grant, we will apply our approaches to data collected from patients with schizophrenia as an example of impaired brain connectivity, and examine where the fMRl/EEG/genetic relationships have deviated from that of the healthy brain. We have recently introduced an ICA-based framework to jointly analyze data from multiple imaging sources and showed the richness of information conveyed by such a joint optimization approach. We have also introduced a number of approaches to effectively incorporate prior information into the ICA estimation in order to improve the performance. In the proposed study, we will bring these two research areas together, develop an ICA based fusion framework that enables the incorporation of prior information, specific for each data type, and will demonstrate the power of joint data analysis both between pairwise data types, and then for more than two data types for joint fusion. We will also extend the fusion ICA framework to one that incorporates prior information depending on each image type to improve the performance of joint information extraction. We will focus upon three image types, fMRI, EEG and genetic array imaging of single nucleotide polymorphisms. These three image types provide complementary information about brain function, and all can benefit from the incorporation of prior information. ? ? BROADER IMPACT: The broad impacts of the proposed work lie in its potential to substantially impact science and information technology. It also has the potential to impact public health by providing new information about healthy and abnormal brain which can then lead to new strategies for protecting and improving health. The study of human brain function is a very challenging and rich problem. The ICA-based fusion approach we believe is the key for achieving significant advances in the field. 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 function widely available. We plan to develop a toolbox that enables fusion and analysis of various types of imaging data and allows the incorporation of prior information and make it available to the research community though a website. ? ?
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