Abstract #3043

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

David C Hike^1,2, Casey P Weiner^3,4, Scott E Boebinger^5,6, Tara N Palin³, and Samuel Colles Grant^1,2

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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