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Ordinal Patterns for Connectivity Networks in Brain Disease Diagnosis

  • Mingxia Liu
  • Junqiang Du
  • Biao Jie
  • Daoqiang ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)

Abstract

Brain connectivity networks have been widely used for diagnosis of brain-related diseases, e.g., Alzheimer’s disease (AD), mild cognitive impairment (MCI), and attention deficit hyperactivity disorder (ADHD). Although several network descriptors have been designed for representing brain connectivity networks, most of them not only ignore the important weight information of edges, but also cannot capture the modular local structures of brain connectivity networks by only focusing on individual brain regions. In this paper, we propose a new network descriptor (called ordinal pattern) for brain connectivity networks, and apply it for brain disease diagnosis. Specifically, we first define ordinal patterns that contain sequences of weighted edges based on a functional connectivity network. A frequent ordinal pattern mining algorithm is then developed to identify those frequent ordinal patterns in a brain connectivity network set. We further perform discriminative ordinal pattern selection, followed by a SVM classification process. Experimental results on both the ADNI and the ADHD-200 data sets demonstrate that the proposed method achieves significant improvement compared with state-of-the-art brain connectivity network based methods.

Notes

Acknowledgment

This study was supported by National Natural Science Foundation of China (Nos. 61422204, 61473149, 61473190, 61573023), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), and the NUAA Fundamental Research Funds (No. NE2013105).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mingxia Liu
    • 1
  • Junqiang Du
    • 1
  • Biao Jie
    • 1
  • Daoqiang Zhang
    • 1
    Email author
  1. 1.School of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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