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Abstract #3747

Atlas-Based Quantification with Machine-Learning Based Characterization of DTI from Patients with Mild Cognitive Impairment and Alzheimers Disease

Kenichi Oishi1, Michelle M. Mielke2, 3, Michael I. Miller4, Marilyn S. Albert5, Constantine G. Lyketsos2, Susumu Mori, 6

1Radiology, Johns Hopkins University, Baltimore, MD, United States; 2Psychiatry and Behavioral Sciences, Johns Hopkins University; 32Division of Epidemiology, College of Medicine, Mayo Clinic; 4Center for Imaging Science, Johns Hopkins University; 5Neurology, Johns Hopkins University; 6F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute

We applied a machine-learning framework to characterize anatomical alterations of early-stage Alzheimers disease (AD), and to investigate a classifier that can predict conversion from mild cognitive impairment (MCI) to AD within 36 months. The Eve atlas was used to measure the fractional anisotropy (FA) and mean diffusivity (MD) of 148 brain structures, followed by principal component analysis (PCA) and support vector machine-based classification. PCA detected subtle but widespread FA&MD alterations related to AD. The trained classifier could differentiate MCI-converters from non-converters with sensitivity of 0.67 and specificity of 1, supporting the potential of DTI in identifying early-stage AD patients.