Multiple Stages Classification of Alzheimer’s Disease Based on Structural Brain Networks Using Generalized Low Rank Approximations (GLRAM)

Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


To classify each stage for a progressing disease such as Alzheimer’s 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 Alzheimer’s 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 Alzheimer’s 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.


Mild Cognitive Impairment Linear Discriminant Analysis Brain Network Health Control Structural Brain Network 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Neurology, Imaging Genetics Center, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA
  3. 3.fMRI LaboratoryUniversity of QueenslandBrisbaneAustralia
  4. 4.Berghofer Queensland Institute of Medical ResearchBrisbaneAustralia

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