Advertisement

Detecting Abnormalities in Resting-State Dynamics: An Unsupervised Learning Approach

  • Meenakshi KhoslaEmail author
  • Keith Jamison
  • Amy Kuceyeski
  • Mert R. Sabuncu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11861)

Abstract

Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional connectivity, there has been a recent surge in examining the temporal patterns in these data. In this paper, we explore two strategies for capturing the normal variability in resting-state activity across a healthy population: (a) an autoencoder approach on the rs-fMRI sequence, and (b) a next frame prediction strategy. We show that both approaches can learn useful representations of rs-fMRI data and demonstrate their novel application for abnormality detection in the context of discriminating autism patients from healthy controls.

References

  1. 1.
    Abraham, et al.: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147, 736–745 (2017)CrossRefGoogle Scholar
  2. 2.
    Khosla, et al.: Machine learning in resting-state fMRI analysis. arXiv preprint arXiv:1812.11477 (2018)
  3. 3.
    Eyler, L.T., et al.: A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. Brain 135(3), 949–960 (2012)CrossRefGoogle Scholar
  4. 4.
    Hasan, M., et al.: Learning temporal regularity in video sequences. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  5. 5.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  6. 6.
    Krizhevsky, A., et al.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)Google Scholar
  7. 7.
    Liu, et al.: Chronnectome fingerprinting: identifying individuals & predicting higher cognitive function using dynamic brain connectivity patterns. Hum. Brain Mapp. 39, 902–915 (2018)Google Scholar
  8. 8.
    Liu, W., et al.: Future frame prediction for anomaly detection - a new baseline. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018)Google Scholar
  9. 9.
    Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of intrinsic brain architecture in autism. Mol. Psychiatry 19, 659 (2014)Google Scholar
  10. 10.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  11. 11.
    Shi, X., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: NIPS (2015)Google Scholar
  12. 12.
    Srivastava, N., Mansimov, E., Salakhutdinov, R.R.: Unsupervised learning of video representations using LSTMs. In: ICML (2015)Google Scholar
  13. 13.
    Suk, H.-I., Lee, S.-W., Shen, D.: A hybrid of deep network and hidden Markov model for MCI identification with resting-state fMRI. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 573–580. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24553-9_70CrossRefGoogle Scholar
  14. 14.
    Tian, L., et al.: Changes in dynamic functional connections with aging. Neuroimage 172, 31–39 (2018)CrossRefGoogle Scholar
  15. 15.
    Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002)CrossRefGoogle Scholar
  16. 16.
    Zeng, L.L., et al.: Unsupervised classification of major depression using functional connectivity MRI. Hum. Brain Mapp. 35(4), 1630–1641 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Meenakshi Khosla
    • 1
    Email author
  • Keith Jamison
    • 2
    • 3
  • Amy Kuceyeski
    • 2
    • 3
  • Mert R. Sabuncu
    • 1
    • 4
  1. 1.School of Electrical and Computer EngineeringCornell UniversityIthacaUSA
  2. 2.RadiologyWeill Cornell Medical CollegeNew YorkUSA
  3. 3.Brain and Mind Research InstituteWeill Cornell Medical CollegeNew YorkUSA
  4. 4.Nancy E. and Peter C. Meinig School of Biomedical EngineeringCornell UniversityIthacaUSA

Personalised recommendations