Title: Integrating complex correlated imaging data in multi-center studies
Abstract: While magnetic resonance imaging (MRI) studies are critical for the diagnosis, monitoring, and study of a wide variety of diseases, their use in quantitative analysis can be complex. An increasingly recognized issue involves the differences between MRI scanners that are used in large multi-center studies. To address this, the current state of the art is to ``regress out'' or ``adjust for'' scanner differences. Our group has found these methods to be insufficient, and has advocated for the adaptation of methods pioneered in genomics to help mitigate inter-scanner differences, which can vary across the brain and result in both mean and variance shifts. We further study the implications of differences in correlation structures across and between images, and how this affects downstream inference.
Speaker Bio: Dr. Shinohara is a Professor of Biostatistics and Epidemiology at the University of Pennsylvania Perelman School of Medicine, senior scholar at the Center for Clinical Epidemiology and Biostatistics, Affiliated Faculty at the Center for Biomedical Imaging Computing and Analytics (CBICA), and a full member of the Institute for Translational Medicine and Therapeutics (ITMAT). Dr. Shinohara's methodological research centers on the assessment of structural and functional changes in the brain throughout development and in neurological, psychiatric and developmental disorders. He is interested in describing complex processes and studying questions that have direct impacts on human health through clinical trials and observational studies. He pursues broad collaborative interests in medicine and public health, with particular emphasis in quantitative biomedical imaging and epidemiology.