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)


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.



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).


  1. 1.
    Robinson, E.C., Hammers, A., Ericsson, A., Edwards, A.D., Rueckert, D.: Identifying population differences in whole-brain structural networks: a machine learning approach. NeuroImage 50(3), 910–919 (2010)CrossRefGoogle Scholar
  2. 2.
    Sporns, O.: From simple graphs to the connectome: networks in neuroimaging. NeuroImage 62(2), 881–886 (2012)CrossRefGoogle Scholar
  3. 3.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  4. 4.
    Wee, C.Y., Yap, P.T., Li, W., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage 54(3), 1812–1822 (2011)CrossRefGoogle Scholar
  5. 5.
    Jie, B., Zhang, D., Wee, C.Y., Shen, D.: Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum. Brain Mapp. 35(7), 2876–2897 (2014)CrossRefGoogle Scholar
  6. 6.
    Liu, M., Zhang, D., Shen, D.: Relationship induced multi-template learning for diagnosis of Alzheimer disease and mild cognitive impairment. IEEE Trans. Med. Imaging 35(6), 1463–1474 (2016)CrossRefGoogle Scholar
  7. 7.
    Fei, F., Jie, B., Zhang, D.: Frequent and discriminative subnetwork mining for mild cognitive impairment classification. Brain Connect. 4(5), 347–360 (2014)CrossRefGoogle Scholar
  8. 8.
    Brier, M.R., Thomas, J.B., Fagan, A.M., Hassenstab, J., Holtzman, D.M., Benzinger, T.L., Morris, J.C., Ances, B.M.: Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiol. Aging 35(4), 757–768 (2014)CrossRefGoogle Scholar
  9. 9.
    Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002)CrossRefGoogle Scholar
  10. 10.
    Yan, X., Cheng, H., Han, J., Yu, P.S.: Mining significant graph patterns by leap search. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 433–444. ACM (2008)Google Scholar
  11. 11.
    Sanz-Arigita, E.J., Schoonheim, M.M., Damoiseaux, J.S., Rombouts, S., Maris, E., Barkhof, F., Scheltens, P., Stam, C.J., et al.: Loss of ‘small-world’ netowrks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PLoS ONE 5(11), e13788 (2010)CrossRefGoogle Scholar

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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