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Abstract #3043

Sex and Hemispheric Dependent DTI-based Network Analysis of an Alzheimer’s Disease Mouse Model

David C Hike1,2, Casey P Weiner3,4, Scott E Boebinger5,6, Tara N Palin3, and Samuel Colles Grant1,2

1National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL, United States, 2Chemical & Biomedical Engineering, FAMU-FSU College of Engineering, Tallahassee, FL, United States, 3Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, 4Applied Mathematics & Statistics, Johns Hopkins University, Baltimore, MD, United States, 5Wallace H. Cooulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States, 6Wallace H. Coulter Department of Biomedical Engineeing, Emory University, Atlanta, GA, United States

This study utilizes DTI and graph theory as a novel way for early detection of pathology and connectivity changes related to Alzheimer’s Disease. As a function of phenotype, age and sex, DTI studies were performed on APP/PS1 mouse brains and age-matched wild type controls at 11.75 T. Current hemisphere-dependentdata shows differences between hemispheres within age and phenotype for the parameters observed.

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