Multiple stages classification of Alzheimers disease based on structural brain networks using Generalized Low Rank Approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimers disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has used machine learning to make inferences about variations in brain networks in the progression of the Alzheimers disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimers disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity. Zhan, Jin, Jahanshad,Prasad and Thompson Imaging Genetics Center, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA, e-mail: [email protected] Nie, Ye and Wang School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA de Zubicaray and McMahon fMRI Laboratory, University of Queensland, Brisbane, Australia Martin and Wright Berghofer Queensland Institute of Medical Research, Australia The final publication will be available at http://link.springer.com/book/10.1007/978-3-319-111827. Computational Diffusion MRI. MICCAI Workshop, Boston, USA, September 18, 2014.. L. O’Donnell, G.Nedjati-Gilani,, Y. Rathi, M. Reisert, and T. Schneider. (Eds.), Springer-Verlag 2015.