Network-Based Classification Using Cortical Thickness of AD Patients

  • Dai Dai
  • Huiguang He
  • Joshua Vogelstein
  • Zengguang Hou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

In this article we propose a framework for establishing individual structural networks. An individual network is established for each subject using the mean cortical thickness of cortical regions as defined by the AAL atlas. Specifically, for each subject, we compute a similarity matrix of mean cortical thickness between pairs of cortical regions, which we refer to hereafter as the individual’s network. Such individual networks can be used for classification. We use a combination of two types of feature selection approaches to search for the most discriminative edges. These edges serve as the input to a support vector machine (SVM) for classification. We demonstrate the utility of the proposed method by a comparison with classifying the raw cortical thickness data, and individual networks, using a publically available dataset. In particular, 83 subjects from the OASIS database were chosen to validate this approach, 39 of which were diagnosed with either mild cognitive impairment (MCI) or moderate Alzheimer’s disease (AD) and the remaining were age-matched controls. While using an SVM on the raw cortical thickness data or individual networks without hybrid feature selection resulted in less than or nearly 80% classification accuracy, our approach yielded 90.4% classification accuracy in leave-one-out analysis.

Keywords

Support Vector Machine Feature Selection Mild Cognitive Impairment Cortical Thickness Feature Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dai Dai
    • 1
  • Huiguang He
    • 1
  • Joshua Vogelstein
    • 2
  • Zengguang Hou
    • 1
  1. 1.State Key Laboratory for Intelligent Control and Management of Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Department of Applied Mathematics and StatisticsJohns Hopkins UniversityUSA

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