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Integrating Multiple Network Properties for MCI Identification

  • Biao Jie
  • Daoqiang Zhang
  • Heung-Il Suk
  • Chong-Yaw Wee
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

Recently, machine learning techniques have been actively applied to the identification of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, most of the existing methods focus on using only single network property, although combination of multiple network properties such as local connectivity and topological properties may be more powerful. Employing the kernel-based method, we propose a novel classification framework that attempts to integrate multiple network properties for improving the MCI classification. Specifically, two different types of kernel (i.e., vector-kernel and graph-kernel) extracted from multiple sub-networks are used to quantify two different yet complementary network properties. A multi-kernel learning technique is further adopted to fuse these heterogeneous kernels for MCI classification. Experimental results show that the proposed multiple-network-properties based method outperforms conventional single-network-property based methods.

Keywords

Mild Cognitive Impairment Cluster Coefficient Classification Framework Graph Kernel Automate Anatomical Label 
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 International Publishing Switzerland 2013

Authors and Affiliations

  • Biao Jie
    • 1
    • 2
  • Daoqiang Zhang
    • 1
  • Heung-Il Suk
    • 2
  • Chong-Yaw Wee
    • 2
  • Dinggang Shen
    • 2
  1. 1.Dept. of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Dept. of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

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