Statistical Methods for Whole-Brain Connectivety Networks

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DESCRIPTION (provided by applicant): My goal for the K25 award is to establish myself as an independent neuroimaging researcher with expertise in brain network analysis and an integral member of multidisciplinary research teams devoted to addressing diseases of the brain. Attaining these objectives will require focused didactic training and research guidance. Research We will develop new methodology to improve whole-brain connectivity analyses of normal and abnormal brain function. The launching of the Human Connectome Project by the NIH in 2009 underscores the importance of whole-brain connectivity analyses. Appropriately conducting these analyses is paramount in our understanding normal brain function as well as alterations due to conditions such as aging, dyslexia, and substance abuse. Before we can glean useful information from functional brain network differences in these conditions, methods need to be developed in order to permit 1) assessing several network properties simultaneously while also accounting for the complex dependence structure of the networks; 2) making predictions about the presence and strength (weight) of connections between brain regions based on disease status; 3) determining whether task related changes in brain networks are associated with clinical outcomes. The novel methods proposed here will address these needs, providing more appropriate techniques for the emerging area of whole-brain connectivity analysis. This research, along with my proposed training experiences and strong mentoring team, will facilitate my progression toward becoming an independent neuroimaging researcher with expertise in brain network analysis and enable me to make unique contributions to brain research. Training The proposed training plan consists of four elements: 1) a didactic component aimed at establishing a basic foundation in computational neuroscience and image analysis; 2) career guidance in methodological development and collaborative neuroimaging research through planned on and off-site mentoring by neuroscientists and network and neuroimaging statisticians; 3) conducting innovative research utilizing the gained neuroscientific and image analytic knowledge and previous statistical training; and 4) participating in the exchange of ideas in statistics and the neurosciences through conference and workshop attendance. The planned training activities will focus on deepening my understanding of the brain as a complex system, enabling me to reasonably model and evaluate this system within its biological context. The combination of my knowledge in network-based brain imaging statistics, computational neuroscience, and image analysis will be a valuable asset that will not be confined to a single brain disorder. While the data analyses proposed here will focus on aging and brain degeneration, dyslexia, and substance abuse, the skills and knowledge that I will gain will position me to collaborate with investigators that study a broad range of clinical brain disorders.