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)


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.


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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  1. 1.
    Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C., Jiang, T.: Abnormal Cortical Network in Mild Cognitive Impairment and Alzheimer’s disease. PLoS Computational Biology 6(11), e1001006 (2010)Google Scholar
  2. 2.
    Sun, Y., Todorovic, S., Goodison, S.: Local-Learning-Based Feature Selection for High-Dimensional Data Analysis. IEEE Trans. PAMI 32(9), 1610–1626 (2010)CrossRefGoogle Scholar
  3. 3.
    Raj, A., Mueller, S.G., Young, K., Laxer, K.D., Weiner, M.: Network-level analysis of cortical thickness of the epileptic brain. NeuroImage 52(4), 1302–1313 (2010)CrossRefGoogle Scholar
  4. 4.
    Wee, C.Y., Yap, P.T., 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.
    Zijdenbos, A., Forghani, R., Evans, A.: Automatic quantification of MS lesions in 3D MRI brain data sets: Validation of INSECT. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 439–448. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Kim, J.S., Singh, V., Lee, J.K., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., Lee, J.M., Kim, S.I., Evans, A.C.: Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage 27(1), 210–221 (2005)CrossRefGoogle Scholar
  7. 7.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1/2), 273–324 (1997)CrossRefzbMATHGoogle Scholar
  8. 8.
    Liu, Y., Liang, M., Zhou, Y., He, Y., Hao, Y., Song, M., Yu, C., Liu, H., Liu, Z., Jiang, T.: Disrupted small-world networks in schizophrenia. Brain 131(4), 945–961 (2008)CrossRefGoogle Scholar
  9. 9.
    Chetelat, G., Landeau, B., Eustache, F., Mezenge, F., Viader, F., de La Sayette, V., Desgranges, B., Baron, J.C.: Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. NeuroImage 27(4), 934–946 (2005)CrossRefGoogle Scholar
  10. 10.
    Karas, G.B., Scheltens, P., Rombouts, S., Visser, P.J., Van Schijndel, R.A., Fox, N.C., Barkhof, F.: Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. Neuroimage 23(2), 708–716 (2004)CrossRefGoogle Scholar
  11. 11.
    Wilson, S.M., Ogar, J.M., Laluz, V., Growdon, M., Jang, J., Glenn, S., Miller, B.L., Weiner, M.W., Gorno-Tempini, M.L.: Automated MRI-based classification of primary progressive aphasia variants. Neuroimage 47(4), 1558–1567 (2009)CrossRefGoogle Scholar
  12. 12.
    Bozzali, M., Parker, G.J.M., Serra, L., Embleton, K., Gili, T., Perri, R., Caltagirone, C., Cercignani, M.: Anatomical connectivity mapping: A new tool to assess brain disconnection in Alzheimer’s disease. Neuroimage 54(3), 2045–2051 (2010)CrossRefGoogle Scholar

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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