Temporal Correlation Structure Learning for MCI Conversion Prediction

  • Xiaoqian Wang
  • Weidong Cai
  • Dinggang Shen
  • Heng HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)


In Alzheimer’s research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer’s. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention and diagnosis of Alzheimer’s. Traditional methods use various classification models to distinguish MCI converters from non-converters, while the performance is usually limited by the small number of available data. Moreover, previous methods only use the data at baseline time for training but ignore the longitudinal information at other time points along the disease progression. To tackle with these problems, we propose a novel deep learning framework that uncovers the temporal correlation structure between adjacent time points in the disease progression. We also construct a generative framework to learn the inherent data distribution so as to produce more reliable data to strengthen the training process. Extensive experiments on the ADNI cohort validate the superiority of our model.


Deep learning Temporal correlation structure MCI conversion prediction Alzheimer’s disease 


  1. 1.
    Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7), e0130140 (2015)CrossRefGoogle Scholar
  2. 2.
    Chongxuan, L., Xu, T., Zhu, J., Zhang, B.: Triple generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 4091–4101 (2017)Google Scholar
  3. 3.
    Fiorini, S., Verri, A., Barla, A., Tacchino, A., Brichetto, G.: Temporal prediction of multiple sclerosis evolution from patient-centered outcomes. In: Machine Learning for Healthcare Conference, pp. 112–125 (2017)Google Scholar
  4. 4.
    Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)Google Scholar
  5. 5.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  6. 6.
    Hu, K., Wang, Y., Chen, K., Hou, L., Zhang, X.: Multi-scale features extraction from baseline structure MRI for MCI patient classification and AD early diagnosis. Neurocomputing 175, 132–145 (2016)CrossRefGoogle Scholar
  7. 7.
    Huang, C., Wahlund, L.O., Svensson, L., Winblad, B., Julin, P.: Cingulate cortex hypoperfusion predicts Alzheimer’s disease in mild cognitive impairment. BMC Neurol. 2(1), 9 (2002)CrossRefGoogle Scholar
  8. 8.
    Kabani, N.J.: 3D anatomical atlas of the human brain. Neuroimage 7, P-0717 (1998)Google Scholar
  9. 9.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  10. 10.
    Lemos, L.: Discriminating Alzheimer’s disease from mild cognitive impairment using neuropsychological data. Age (M \(\pm \) SD) 70(8.4), 73 (2012)Google Scholar
  11. 11.
    Liu, S., et al.: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans. Biomed. Eng. 62(4), 1132–1140 (2015)CrossRefGoogle Scholar
  12. 12.
    Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: International Conference on Machine Learning (ICML), vol. 30 (2013)Google Scholar
  13. 13.
    Nowrangi, M.A., Rosenberg, P.B.: The fornix in mild cognitive impairment and alzheimers disease. Front. Aging Neurosci. 7, 1 (2015)CrossRefGoogle Scholar
  14. 14.
    Salimans, T., Kingma, D.P.: Weight normalization: a simple reparameterization to accelerate training of deep neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 901–909 (2016)Google Scholar
  15. 15.
    Schmitter, D.: An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. NeuroImage: Clin. 7, 7–17 (2015)CrossRefGoogle Scholar
  16. 16.
    Shen, D., Davatzikos, C.: Hammer: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21(11), 1421–1439 (2002)CrossRefGoogle Scholar
  17. 17.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar
  18. 18.
    Wang, H., et al.: Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics 28(2), 229–237 (2011)CrossRefGoogle Scholar
  19. 19.
    Wang, Y., et al.: Knowledge-guided robust MRI brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PloS One 9(1), e77810 (2014)CrossRefGoogle Scholar
  20. 20.
    Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D.: Robust deformable-surface-based skull-stripping for large-scale studies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6893, pp. 635–642. Springer, Heidelberg (2011). Scholar
  21. 21.
    Wei, R., Li, C., Fogelson, N., Li, L.: Prediction of conversion from mild cognitive impairment to Alzheimer’s disease using MRI and structural network features. Front. Aging Neurosci. 8, 76 (2016)CrossRefGoogle Scholar
  22. 22.
    Weiner, M.W., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s Dement. 9(5), e111–e194 (2013)CrossRefGoogle Scholar
  23. 23.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaoqian Wang
    • 1
  • Weidong Cai
    • 2
  • Dinggang Shen
    • 3
  • Heng Huang
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia
  3. 3.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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