Classifying Stages of Mild Cognitive Impairment via Augmented Graph Embedding

  • Haoteng Tang
  • Lei Guo
  • Emily Dennis
  • Paul M. Thompson
  • Heng Huang
  • Olusola Ajilore
  • Alex D. Leow
  • Liang ZhanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)


Mild Cognitive Impairment (MCI) is a clinically intermediate stage in the course of Alzheimer’s disease (AD). MCI does not always lead to dementia. Some MCI patients may stay in the MCI status for the rest of their life, while others will develop AD eventually. Therefore, classification methods that help to distinguish MCI from earlier or later stages of the disease are important to understand the progression of AD. In this paper, we propose a novel computational framework - named Augmented Graph Embedding, or AGE - to tackle this challenge. In this new AGE framework, the random walk approach is first applied to brain structural networks derived from diffusion-weighted MRI to extract nodal feature vectors. A technique adapted from natural language processing is used to analyze these nodal feature vectors, and a multimodal augmentation procedure is adopted to improve classification accuracy. We validated this new AGE framework on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Results show advantages of the proposed framework, compared to a range of existing methods.


Mild Cognitive Impairment Brain structural network Graph embedding Random walk Natural Language Processing Data augmentation 


  1. 1.
    Petersen, R.C., et al.: Current concepts in mild cognitive impairment. Arch. Neurol. 58(12), 1985–1992 (2001)CrossRefGoogle Scholar
  2. 2.
    Dawe, B., Procter, A., Philpot, M.: Concepts of mild memory impairment in the elderly and their relationship to dementia - a review. Int. J. Geriatr. Psychiatry 7(7), 473–479 (1992)CrossRefGoogle Scholar
  3. 3.
    Petersen, R.C.: : Clinical characterization and outcome (vol 56, pg 303, 1999). Arch. Neurol-Chic. 56(6), 760 (1999)Google Scholar
  4. 4.
    Lee, E.S., et al.: Default mode network functional connectivity in early and late mild cognitive impairment results from the Alzheimer’s disease neuroimaging initiative. Alzheimer Dis. Assoc. Disord. 30(4), 289–296 (2016)CrossRefGoogle Scholar
  5. 5.
    Aisen, P.S., et al.: Clinical core of the Alzheimer’s disease neuroimaging initiative: progress and plans. Alzheimer’s Dement. 6(3), 239–246 (2010)CrossRefGoogle Scholar
  6. 6.
    Goryawala, M., Zhou, Q., Barker, W., Loewenstein, D.A., Duara, R., Adjouadi, M.: Inclusion of neuropsychological scores in atrophy models improves diagnostic classification of alzheimer’s disease and mild cognitive impairment. Comput. Intell. Neurosci. 2015, 865265 (2015)CrossRefGoogle Scholar
  7. 7.
    Shakeri, M., Lombaert, H., Tripathi, S., Kadoury, S.: Deep spectral-based shape features for Alzheimer’s disease classification. In: Reuter, M., Wachinger, C., Lombaert, H. (eds.) SeSAMI 2016. LNCS, vol. 10126, pp. 15–24. Springer, Cham (2016). Scholar
  8. 8.
    Korolev, S., Safiullin, A., Belyaev, M., Dodonova, Y.: Residual and plain convolutional neural networks for 3D brain MRI classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 835–838. IEEE (2017)Google Scholar
  9. 9.
    Jessen, F., et al.: AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimer’s Dement. 10(1), 76–83 (2014)CrossRefGoogle Scholar
  10. 10.
    Hett, K., Ta, V.-T., Giraud, R., Mondino, M., Manjón, José V., Coupé, P.: Patch-based DTI grading: application to Alzheimer’s disease classification. In: Wu, G., Coupé, P., Zhan, Y., Munsell, Brent C., Rueckert, D. (eds.) Patch-MI 2016. LNCS, vol. 9993, pp. 76–83. Springer, Cham (2016). Scholar
  11. 11.
    Singh, S., et al.: Deep-learning-based classification of FDG-PET data for Alzheimer’s disease categories. In: 13th International Conference on Medical Information Processing and Analysis, 2017, vol. 10572, p. 105720 J. International Society for Optics and Photonics (2017)Google Scholar
  12. 12.
    Tripathi, S., Nozadi, S.H., Shakeri, M., Kadoury, S.: Sub-cortical shape morphology and voxel-based features for Alzheimer’s disease classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 991–994. IEEE (2017)Google Scholar
  13. 13.
    Nozadi, S.H., Kadoury, S., The Alzheimer’s Disease Neuroimaging Initiative: Classification of Alzheimer’s and MCI patients from semantically parcelled PET images: a comparison between AV45 and FDG-PET. Int. J. Biomed. Imaging 2018, 1247430 (2018)Google Scholar
  14. 14.
    La Rocca, M., Amoroso, N., Monaco, A., Bellotti, R., Tangaro, S.: A novel approach to brain connectivity reveals early structural changes in Alzheimer’s disease. Physiol. Meas. 39(7), 074005 (2018)Google Scholar
  15. 15.
    Wang, Q., et al.: The added value of diffusion-weighted MRI-derived structural connectome in evaluating mild cognitive impairment: a multi-cohort validation1. J. Alzheimers Dis. 64(1), 149–169 (2018)CrossRefGoogle Scholar
  16. 16.
    Prasad, G., Joshi, S.H., Nir, T.M., Toga, A.W., Thompson, P.M., Alzheimer’s Disease Neuroimaging Initiative: Brain connectivity and novel network measures for Alzheimer’s disease classification. Neurobiol. Aging 36(Suppl. 1), S121–S131 (2015)Google Scholar
  17. 17.
    Zhan, L., et al.: Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM). In: O’Donnell, L., Nedjati-Gilani, G., Rathi, Y., Reisert, M., Schneider, T. (eds.) Computational Diffusion MRI. Mathematics and Visualization, pp. 35–44. Springer, Cham (2014). Scholar
  18. 18.
    Kurmukov, A., et al.: Classifying phenotypes based on the community structure of human brain networks. In: Cardoso, M.J., et al. (eds.) GRAIL/MFCA/MICGen -2017. LNCS, vol. 10551, pp. 3–11. Springer, Cham (2017). Scholar
  19. 19.
    Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)Google Scholar
  20. 20.
    Zhan, L., et al.: Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease. Front Aging Neurosci. 7, 48 (2015)CrossRefGoogle Scholar
  21. 21.
    Lazar, M., et al.: White matter tractography using diffusion tensor deflection. Hum. Brain Mapp. 18(4), 306–321 (2003)CrossRefGoogle Scholar
  22. 22.
    Conturo, T.E., et al.: Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. U. S. A. 96(18), 10422–10427 (1999)CrossRefGoogle Scholar
  23. 23.
    Aganj, I., et al.: A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography. Med. Image Anal. 15(4), 414–425 (2011)CrossRefGoogle Scholar
  24. 24.
    Parker, G.J., Haroon, H.A., Wheeler-Kingshott, C.A.: A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J. Magn. Reson. Imaging 18(2), 242–254 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haoteng Tang
    • 1
  • Lei Guo
    • 1
  • Emily Dennis
    • 2
  • Paul M. Thompson
    • 3
  • Heng Huang
    • 1
  • Olusola Ajilore
    • 4
  • Alex D. Leow
    • 4
  • Liang Zhan
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA
  2. 2.Harvard Medical SchoolHarvard UniversityBostonUSA
  3. 3.Institute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesUSA
  4. 4.Department of PsychiatryUniversity of Illinois at ChicagoChicagoUSA

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