Novel Effective Connectivity Network Inference for MCI Identification

  • Yang Li
  • Hao YangEmail author
  • Ke Li
  • Pew-Thian Yap
  • Minjeong Kim
  • Chong-Yaw Wee
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10541)


Inferring effective brain connectivity network is a challenging task owing to perplexing noise effects, the curse of dimensionality, and inter-subject variability. However, most existing network inference methods are based on correlation analysis and consider the datum points individually, revealing limited information of the neuron interactions and ignoring the relations amongst the derivatives of the data. Hence, we proposed a novel ultra group-constrained sparse linear regression model for effective connectivity inference. This model utilizes not only the discrepancy between observed signals and the model prediction, but also the discrepancy between the associated weak derivatives of the observed and the model signals for a more accurate effective connectivity inference. What’s more, a group constraint is applied to minimize the inter-subject variability and the proposed modeling was validated on a mild cognitive impairment dataset with superior results achieved.


  1. 1.
    Sporns, O.: Towards network substrates of brain disorders. Brain 137, 2117–2118 (2014). doi: 10.1093/brain/awu148 CrossRefGoogle Scholar
  2. 2.
    Lee, H.L.D., Kang, H., Kim, B.N., Chung, M.K.: Sparse brain network recovery under compressed sensing. IEEE Trans. Med. Imaging 30(5), 1154–1165 (2011)CrossRefGoogle Scholar
  3. 3.
    Wee, C.Y., Yap, P.T., Zhang, D., Wang, L., Shen, D.: Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Struct. Funct. 219(2), 641–656 (2014). doi: 10.1007/s00429-013-0524-8 CrossRefGoogle Scholar
  4. 4.
    Li, Y., Cui, W.G., Guo, Y.Z., Huang, T., Yang, X.F., Wei, H.L.: Time-varying system identification using an ultra-orthogonal forward regression and multiwavelet basis functions with applications to EEG. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–13 (2017). doi: 10.1109/TNNLS.2017.2709910 Google Scholar
  5. 5.
    Li, Y., Wee, C.Y., Jie, B., Peng, Z.W., Shen, D.G.: Sparse multivariate autoregressive modeling for mild cognitive impairment classification. Neuroinformatics 12(3), 455–469 (2014). doi: 10.1007/s12021-014-9221-x CrossRefGoogle Scholar
  6. 6.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010). doi: 10.1016/j.neuroimage.2009.10.003 CrossRefGoogle Scholar
  7. 7.
    Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., Hamprecht, F.A.: A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10 (2009)Google Scholar
  8. 8.
    Nir, T.M., Jahanshad, N., Villalon-Reina, J.E., Toga, A.W., Jack, C.R., Weiner, M.W., Thompson, P.M., Alzheimer’s Disease Neuroimaging Initiative (ADNI): Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. Neuroimage Clin 3, 180–195 (2013). doi: 10.1016/j.nicl.2013.07.006 CrossRefGoogle Scholar
  9. 9.
    Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., Castiglioni, I., Alzheimer’s Disease Neuroimaging Initiative: Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front. Neurosci. 9, 307 (2015). doi: 10.3389/Fnins.2015.00307 CrossRefGoogle Scholar
  10. 10.
    Jie, B., Zhang, D.Q., Gao, W., Wang, Q., Wee, C.Y., Shen, D.G.: Integration of network topological and connectivity properties for neuroimaging classification. IEEE Trans. Bio Med. Eng. 61(2), 576–589 (2014). doi: 10.1109/Tbme.2013.2284195 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yang Li
    • 1
  • Hao Yang
    • 1
    Email author
  • Ke Li
    • 2
  • Pew-Thian Yap
    • 3
  • Minjeong Kim
    • 3
  • Chong-Yaw Wee
    • 4
  • Dinggang Shen
    • 3
  1. 1.School of Automation Sciences and Electrical EngineeringBeihang UniversityBeijingChina
  2. 2.School of Aeronautic Science and EngineeringBeihang UniversityBeijingChina
  3. 3.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore

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