The Neuroimaging core will acquire and process multimodal imaging data to enhance significantly our understanding of the effects of serotonergic signaling in the brain. It will provide the extensive expertise of its faculty and staff for conducting brain imaging research in mice, newborn babies, children, and adolescents using structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), perfusion MRI, MR spectroscopy (MRS), and high density EEG. By applying validated and reliable imaging and statistical methods, the core will ensure uniformity, consistency, and economy of scale across all projects of the center. The Core will contribute significantly to accelerate our understanding of the disturbances in the brain caused by serotonin availability at various stages during early brain development. To quantify disturbances in various cortical and subcortical brain circuits, research projects will acquire and analyze multimodal MR data, and compute various brain measures, including EEG power and coherence, local volumes of brain regions, and between-region connectivity. These measures will be correlated with genotype data to discern the gene-brain-behavior correlates across species, and age ranges from infancy to adulthood. Furthermore, the core will ensure data integrity by (1) quantifying identical brain measures using the same imaging and statistical methods across all projects;(2) rigorously evaluating methods developed to address better the challenges in analyzing data from a wide age range of participants;and (3) exploiting economy of scale by applying common components of image and statistical analyses across all projects, thereby greatly enhancing the efficiency and productivity of the center.
The Neuroimaging core will support the integrative and translational research of the center by ensuring uniformity, consistency, and economy of scale across all projects, thereby accelerating the study of the effects of serotonergic signaling in the brain via various manipulations, including animal models and genotype, behavioral, and imaging data across several populations and species.
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|Talati, Ardesheer; Weissman, Myrna M; Hamilton, Steven P (2013) Using the high-risk family design to identify biomarkers for major depression. Philos Trans R Soc Lond B Biol Sci 368:20120129|
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