Advertisement

Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and Exploring Impact of Neuroimaging

  • Yaroslav Shmulev
  • Mikhail Belyaev
  • the Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11044)

Abstract

Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimers Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit on the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does have an effect on conversion prediction, however cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is \(\text {ACC} = 0.76 \pm 0.01\) and the area under the ROC curve – \(\text {ROC AUC} = 0.86 \pm 0.01\).

Keywords

Image classification Similarity learning Disease progression CNN MRI 

Notes

Acknowledgements

The obtained results has been obtained under support of the Russian Science Foundation grant 17-11-0139.

References

  1. 1.
    Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449–459 (2017)CrossRefGoogle Scholar
  2. 2.
    Alzheimer’s Disease Neuroimaging Initiative (2003). http://adni.loni.usc.edu/. Accessed 22 May 2018
  3. 3.
    Avants, B., et al.: Evaluation of an open-access, automated brain extraction method on multi-site multi-disorder data. In: 16th Annual Meeting for the Organization of Human Brain Mapping (2010)Google Scholar
  4. 4.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)Google Scholar
  5. 5.
    Cheng, B., Liu, M., Shen, D., Li, Z., Zhang, D., Alzheimer’s Disease Neuroimaging Initiative: Multi-domain transfer learning for early diagnosis of Alzheimer’s disease. Neuroinformatics 15(2), 115–132 (2017)CrossRefGoogle Scholar
  6. 6.
    Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. Aging 32(12), 2322–e19 (2011)CrossRefGoogle Scholar
  7. 7.
    He, K., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, December 2015Google Scholar
  8. 8.
    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
  9. 9.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)zbMATHGoogle Scholar
  10. 10.
    Roberson, E.D., Mucke, L.: 100 years and counting: prospects for defeating Alzheimer’s disease. Science 314(5800), 781–784 (2006)CrossRefGoogle Scholar
  11. 11.
    Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease structural MRI data by deep learning convolutional neural networks. arXiv preprint arXiv:1607.06583 (2016)
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  13. 13.
    Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010)CrossRefGoogle Scholar
  14. 14.
    Tustison, N.J., et al.: The ANTs longitudinal cortical thickness pipeline. In: Proceedings of SPIE (2013)Google Scholar
  15. 15.
    Ustinova, E., Lempitsky, V.: Learning deep embeddings with histogram loss. In: Advances in Neural Information Processing Systems, pp. 4170–4178 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yaroslav Shmulev
    • 1
    • 2
  • Mikhail Belyaev
    • 1
    • 2
  • the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Kharkevich Institute for Information Transmission ProblemsMoscowRussia
  2. 2.Skolkovo Institute of Science and TechnologyMoscowRussia

Personalised recommendations