Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data

  • Ning Qiang
  • Bao GeEmail author
  • Qinglin Dong
  • Fangfei Ge
  • Tianming Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11977)


It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a new challenge of neural network architecture design has also attracted attention: due to the high dimension of fMRI volume images, the manual process of network model design is very time-consuming and error prone. To tackle this problem, we proposed a Particle Swarm Optimization (PSO) based neural architecture search (NAS) framework for a deep belief network (DBN) that models volumetric fMRI data, named NAS-DBN. The core idea is that the particle swarm in our NAS framework can temporally evolve and finally converge to a feasible optimal solution. Experimental results showed that the proposed NAS-DBN framework can find robust architecture with minimal testing loss. Furthermore, we compared functional brain networks derived by NAS-DBN with general linear model (GLM), and the results demonstrated that the NAS-DBN is effective in modeling volumetric fMRI data.


Neural Architecture Search (NAS) Particle swarm optimization (PSO) Deep Belief Network Task fMRI 



We thank all investigators contributing data to the HCP project. Bao Ge was supported by NSFC61976131.


  1. 1.
    Logothetis, N.K.: What we can do and what we cannot do with fMRI. Nature 453(7197), 869 (2008)CrossRefGoogle Scholar
  2. 2.
    Beckmann, C.F., Smith, S.M.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23(2), 137–152 (2004)CrossRefGoogle Scholar
  3. 3.
    Beckmann, C.F., Jenkinson, M., Smith, S.M.: General multilevel linear modeling for group analysis in FMRI. Neuroimage 20(2), 1052–1063 (2003)CrossRefGoogle Scholar
  4. 4.
    Lv, J., Jiang, X., Li, X., Zhu, D., Zhang, S., Zhao, S., et al.: Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function. IEEE Trans. Biomed. Eng. 62(4), 1120–1131 (2015)CrossRefGoogle Scholar
  5. 5.
    Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning (2016)Google Scholar
  6. 6.
    Abraham, A., Pedregosa, F., Eickenberg, M., et al.: Machine learning for neuroimaging with scikit-learn. Front. Neuroinformatics 8, 14 (2014)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J.: Particle swarm optimization. Encycl. Mach. Learn. 4, 760–766 (2010)Google Scholar
  8. 8.
    Barch, D.M., Burgess, G.C., Harms, M.P., Petersen, S.E., Schlaggar, B.L., Corbetta, M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ning Qiang
    • 1
  • Bao Ge
    • 1
    Email author
  • Qinglin Dong
    • 2
  • Fangfei Ge
    • 2
  • Tianming Liu
    • 2
  1. 1.Shaanxi Normal UniversityXi’anChina
  2. 2.The University of GeorgiaAthensUSA

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