Abstract #3765

Machine learning approach to classify Alzheimer disease and Vascular Dementia using MRI quantitative metrics

Elia Tagliani¹, Gloria Castellazzi^1,2, Andrea De Rinaldis^1,2, Fulvia Palesi^2,3, Letizia Casiraghi^2,4, Elena Sinforiani⁵, Paolo Vitali⁶, Nicoletta Anzalone⁷, Giovanni Magenes¹, Claudia AM Gandini Wheeler-Kingshott^2,8, Giuseppe Micieli⁹, and Egidio D'Angelo^2,4

¹Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, ²Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy, ³Department of Physics, University of Pavia, Pavia, Italy, ⁴Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, ⁵Neurology Unit, C. Mondino National Neurological Institute, Pavia, Italy, ⁶Brain MRI 3T research center, C. Mondino National Neurological Institute, Pavia, Italy, ⁷Scientific Institute H. S. Raffaele, Milan, Italy, ⁸NMR Research Unit, Queen Square MS Centre Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom, ⁹Department of Emergency Neurology, C. Mondino National Neurological Institute, Pavia, Italy

Despite the large number of studies on dementia, efforts to define a clear profile of cognitive impairment for Vascular Dementia (VaD),as well as its differentiation from Alzheimer Disease (AD), are still poor. In this study we tested the power of imaging metrics and adopted a data mining approach, based on Diffusion Tensor Imaging and resting state fMRI, to assess the reliability of machine learning approaches for the automated diagnosis of AD and VaD. Our results show that machine learning algorithms are able to discriminate VaD from AD, representing a suitable approach to build an automated diagnostic system for dementia-like diseases.

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