Advertisement

Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net

  • Wei Zhang
  • Lin Zhao
  • Qing Li
  • Shijie Zhao
  • Qinglin Dong
  • Xi Jiang
  • Tuo Zhang
  • Tianming LiuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11766)

Abstract

The hierarchical organization of brain function has been an established concept in the neuroscience field. Recently, these hierarchical organizations have been extensively investigated in terms of how such hierarchical functional networks are constructed in the human brain via a variety of deep learning models. However, a key problem of how to determine the optimal neural architecture (NA), e.g., hyper parameters, of deep model has not been solved yet. To address this question, in this work, a novel Hybrid Spatiotemporal Neural Architecture Search Net (HS-NASNet) is proposed by jointly using Evolutionary Optimizer, Deep Belief Networks (DBN) and Deep LASSO to reasonably determine the NA, thus revealing the latent hierarchical spatiotemporal features based on the Human Connectome Project (HCP) 900 fMRI datasets. Briefly, this HS-NASNet can automatically search the global optimal NA of DBN given the search space, and then the optimized DBN can extract the weights between two adjacent layers of the optimal NA, which are then treated as the hierarchical temporal dictionaries for Deep LASSO to identify the corresponding hierarchical spatial maps. Our results demonstrate that the optimized deep model can achieve accurate fMRI signal reconstruction and identify spatiotemporal functional networks exhibiting multiscale properties that can be well characterized and interpreted based on current neuroscience knowledge.

Keywords

Hierarchical organization Neural architecture search Evolutionary optimization Deep Belief Network Task-based fMRI LASSO 

References

  1. 1.
    Friston, K.J., et al.: Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210 (1995)CrossRefGoogle Scholar
  2. 2.
    Bassett, D.S., et al.: Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci. 28(37), 9239–9248 (2008)CrossRefGoogle Scholar
  3. 3.
    Ferrarini, L., et al.: Hierarchical functional modularity in the resting-state human brain. Hum. Brain Mapp. 30, 2220–2231 (2009)CrossRefGoogle Scholar
  4. 4.
    Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. arXiv preprint arXiv:1802.01548 (2018)
  5. 5.
    Liu, C., et al.: Progressive neural architecture search. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 19–35. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01246-5_2CrossRefGoogle Scholar
  6. 6.
    Cortes, C., Gonzalvo, X., Kuznetsov, V., Mohri, M., Yang, S.: AdaNet: adaptive structural learning of artificial neural networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 874–883. JMLR.org, August 2017Google Scholar
  7. 7.
    Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2011).  https://doi.org/10.1007/978-0-387-30164-8CrossRefGoogle Scholar
  8. 8.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hu, X., et al.: Latent source mining in FMRI via restricted Boltzmann machine. Hum. Brain Mapp. 39(6), 2368–2380 (2018)CrossRefGoogle Scholar
  10. 10.
    Zhang, W., et al.: Experimental comparisons of sparse dictionary learning and independent component analysis for brain network inference from fMRI data. IEEE Trans. Biomed. Eng. 66(1), 289–299 (2018)CrossRefGoogle Scholar
  11. 11.
    Huang, H., et al.: Modeling task fMRI data via deep convolutional autoencoder. IEEE Trans. Med. Imaging 37(7), 1551–1561 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wei Zhang
    • 1
  • Lin Zhao
    • 1
  • Qing Li
    • 2
  • Shijie Zhao
    • 3
  • Qinglin Dong
    • 1
  • Xi Jiang
    • 4
  • Tuo Zhang
    • 3
  • Tianming Liu
    • 1
    Email author
  1. 1.Department of Computer ScienceThe University of GeorgiaAthensUSA
  2. 2.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  3. 3.Department of AutomationNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  4. 4.MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations