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Machine Vision and Applications

, Volume 24, Issue 7, pp 1445–1457 | Cite as

Accurate prediction of AD patients using cortical thickness networks

  • Dai Dai
  • Huiguang HeEmail author
  • Joshua T. Vogelstein
  • Zengguang Hou
Special Issue Paper

Abstract

It is widely believed that human brain is a complicated network and many neurological disorders such as Alzheimer’s disease (AD) are related to abnormal changes of the brain network architecture. In this work, we present a kernel-based method to establish a network for each subject using mean cortical thickness, which we refer to hereafter as the individual’s network. We construct individual networks for 83 subjects, including AD patients and normal controls (NC), which are taken from the Open Access Series of Imaging Studies database. The network edge features are used to make prediction of AD/NC through the sophisticated machine learning technology. As the number of edge features is much more than that of samples, feature selection is applied to avoid the adverse impact of high-dimensional data on the performance of classifier. We use a hybrid feature selection that combines filter and wrapper methods, and compare the performance of six different combinations of them. Finally, support vector machines are trained using the selected features. To obtain an unbiased evaluation of our method, we use a nested cross validation framework to choose the optimal hyper-parameters of classifier and evaluate the generalization of the method. We report the best accuracy of 90.4 % using the proposed method in the leave-one-out analysis, outperforming that using the raw cortical thickness data by more than 10 %.

Keywords

Classification Alzheimer’s disease Network Cortical thickness 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61271151, 61228103, 61175076), and the Sci. & Tech. Aiding the Disabled Program of the Chinese Academy of Sciences (Grant #KGCX2-YW-618). We thank Dr. Hai Jiang for proof-reading.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dai Dai
    • 1
  • Huiguang He
    • 1
    Email author
  • Joshua T. Vogelstein
    • 2
  • Zengguang Hou
    • 1
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Johns Hopkins UniversityBaltimoreUSA

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