Graph Hyperalignment for Multi-subject fMRI Functional Alignment

  • Weida Li
  • Fang Chen
  • Daoqiang ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)


In fMRI analysis, the scientist seeks to aggregate multi-subject fMRI data so that inferences shared across subjects can be achieved. The challenge is to eliminate the variability of anatomical structure and functional topography of the human brain, which calls for aligning fMRI data across subjects. However, the existing methods do not exploit the geometry of the stimuli, which can be inferred by using certain domain knowledge and then serve as a priori. In this paper, such geometry is encoded in a graph matrix, and we propose an algorithm named Graph Hyperalignment for leveraging it. Specifically, a kernel-based optimization is developed to allow for non-linear feature extraction. To tackle overfitting caused by the high-spatial-and-low-temporal resolution of fMRI, the data in the new feature space are assumed to lie in a low-dimensional affine subspace, which can be implicitly integrated into the proposed optimization. Unlike other iterative existing methods, GHA reaches an optimal solution directly. Examining over four real datasets, Graph Hyperaligment achieves superior results to other methods.


Brain decoding Hyperalignment Graph embedding 



This work was supported by the National Natural Science Foundation of China (Nos. 61876082, 61861130366, 61703301) and the Royal Society-Academy of Medical Sciences Newton Advanced Fellowship (No. NAF\R1\180371).


  1. 1.
    Haxby, J.V., Connolly, A.C., Guntupalli, J.S.: Decoding neural representational spaces using multivariate pattern analysis. Ann. Rev. Neurosci. 37, 435–456 (2014)CrossRefGoogle Scholar
  2. 2.
    Turek, J.S., Willke, T.L., Chen, P.H., Ramadge, P.J.: A semi-supervised method for multi-subject FMRI functional alignment. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1098–1102 IEEE (2017)Google Scholar
  3. 3.
    Turek, J.S., Ellis, C.T., Skalaban, L.J., Turk-Browne, N.B., Willke, T.L.: Capturing shared and individual information in FMRI data. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 826–830 IEEE (2018)Google Scholar
  4. 4.
    Haxby, J.V., et al.: A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72(2), 404–416 (2011)CrossRefGoogle Scholar
  5. 5.
    Chen, P.H.C., Chen, J., Yeshurun, Y., Hasson, U., Haxby, J., Ramadge, P.J.: A reduced-dimension FMRI shared response model. In: Advances in Neural Information Processing Systems (NIPS), pp. 460–468 (2015)Google Scholar
  6. 6.
    Lorbert, A., Ramadge, P.J.: Kernel hyperalignment. In: Advances in Neural Information Processing Systems (NIPS), pp. 1790–1798 (2012)Google Scholar
  7. 7.
    Xu, H., Lorbert, A., Ramadge, P.J., Guntupalli, J.S., Haxby, J.V.: Regularized hyperalignment of multi-set fmri data. In: 2012 IEEE Statistical Signal Processing Workshop (SSP), pp. 229–232. IEEE (2012)Google Scholar
  8. 8.
    Kettenring, J.R.: Canonical analysis of several sets of variables. Biometrika 58(3), 433–451 (1971)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chung, F.R., Graham, F.C.: Spectral Graph Theory, vol. 92. American Mathematical Society, Providence (1997)Google Scholar
  10. 10.
    Chen, P.H., Guntupalli, J.S., Haxby, J.V., Ramadge, P.J.: Joint SVD-hyperalignment for multi-subject FMRI data alignment. In: 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6 IEEE (2014)Google Scholar
  11. 11.
    Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539), 2425–2430 (2001)CrossRefGoogle Scholar
  12. 12.
    Foerde, K., Knowlton, B.J., Poldrack, R.A.: Modulation of competing memory systems by distraction. Proc. Nat. Acad. Sci. 103(31), 11778–11783 (2006)CrossRefGoogle Scholar
  13. 13.
    Carlin, J.D., Kriegeskorte, N.: Adjudicating between face-coding models with individual-face FMRI responses. PLoS Comput. Biol. 13(7), e1005604 (2017)CrossRefGoogle Scholar
  14. 14.
    Schonberg, T., Fox, C.R., Mumford, J.A., Congdon, E., Trepel, C., Poldrack, R.A.: Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an fmri investigation of the balloon analog risk task. Front. Neurosci. 6, 80 (2012)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer Science and Technology, MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing University of Aeronautics and AstronauticsNanjingChina

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