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Modeling Personalized Brain Development with Big Data

Big data offer an opportunity to study specific control populations (age / sex / environmental factors /
demographics / genetics) and identify substantive homogeneous sub-cohorts so that one may understand the roles that potential factors play in brain development, differentiating abnormal trajectories from normal development. The image processing, statistical, and informatics tools to effectively and efficiently use big data imaging archives for quantitative population-level research and personalized medicine do not yet exist. This research will enable discovery science on a scale considerably larger than routinely possible with traditional study designs by creating novel informatics resources that tie archives of 3-D images into accessible research databases.

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Contact: Bennett Allan LandmanEmail