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The brain is consisted of billions of neurons and their collective actions determine our internal thoughts and behaviors. Needless to say, understanding how these neurons interact on a network level is arguably one of the greatest scientific challenges of our time. Several cutting-edge neuroimaging techniques have shown promises in elucidating the connectivity between and among networks of neurons and how their interplays shape the output of the brain. My research program will focus on mapping rodent brain networks, understanding relevant mechanisms in addiction models, developing machine learning technologies to streamline analyses, and translating preclinical findings into humans. By using high-field MRI technologies, I demonstrated the structure and function of large-scale functional networks in the rodent brain by using a variety of analytical approaches, including modularity analysis, independent component analysis, network-based statistics, and dynamic analysis. These works provide a framework to further explore the physiological basis and behavioral relevance of the large-scale functional networks in the rodent brain. Using cutting-edge fMRI technologies, I investigate how the innate, individual differences in large-scale network interaction relates and predicts addiction withdraw behavior in animal models of nicotine addiction. These results offer novel and translatable fingerprints of nicotine addiction in the brain and provide insights into nicotine addiction management in humans. My overall research goal is to link these preclinical findings to humans and ultimately aid development of treatment strategies for brain disorders.