Statistical Methods for Whole-Brain Dynamic Connectivity Analysis

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My objective for the K25 award is to establish myself as an independent neuroimaging statistician, with expertise in whole-brain network analyses and an integral member of multidisciplinary research teams devoted to addressing diseases of the brain. Attaining these goals will require didactic training and research guidance. Research We will develop new methodology to improve whole-brain dynamic connectivity analyses of normal and abnormal brain function, which is vital for understanding various brain disorders, such as Alzheimer’s Disease, and may help identify biomarkers and inform early prevention and treatment. Previous studies are largely based on one average network constructed using data from an entire brain scan (i.e., static connectivity), but emerging evidence suggests network topology exhibits meaningful variations on the second to minute scale, creating a gap in understanding unless these variations are quantified. While several methods have been proposed to address this new direction in the field, there does not yet exist a unifying framework that accurately estimates whole-brain networks, as well as the dynamics of network change across a functional magnetic resonance imaging (fMRI) experiment, while a) accounting for variables of interest and motion- induced artifacts and b) allowing for individual estimates of dynamics. The novel methods proposed here will address these needs and provide a set of tools for future dynamic brain network analysis research. This research, along with my proposed training plan, will facilitate my progression toward becoming an independent neuroimaging statistician with expertise in brain network analysis. Training The proposed training program involves four components: 1) career guidance and neuroscience and network analysis training from a mentoring committee; 2) an educational component to establish fundamental knowledge in computational neuroscience and image analysis; 3) performing innovative research using the skills gained from the proposed training plan and; 4) participating in the exchange of knowledge and ideas with other statisticians and neuroscientists through workshops, conferences, seminar series, and journal clubs. The training will enable me to shift from an early career statistician to an established, independent, neuroimaging statistician with expertise in whole-brain network analyses. The training in computational neuroscience and image analysis will allow me to become a multidisciplinary research team scientist dedicated to studying the human brain. The growth gained through this 5-year period will lead to a skill set, and a confidence, that allows me to be more well-versed in the neuroscience and biology behind the data I am analyzing. This will ultimately lead to more effective communication with neuroscientists and clinicians, improved study design, more informed statistical analyses, and a more comprehensive interpretation of the results in my future work.