Gradient Hyperalignment for Multi-subject fMRI Data Alignment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11012)


Multi-subject fMRI data analysis is an interesting and challenging problem in human brain decoding studies. The inherent anatomical and functional variability across subjects make it necessary to do both anatomical and functional alignment before classification analysis. Besides, when it comes to big data, time complexity becomes a problem that cannot be ignored. This paper proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional alignment method that is suitable for multi-subject fMRI datasets with large amounts of samples and voxels. The advantage of Gradient-HA is that it can solve independence and high dimension problems by using Independent Component Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using multi-classification tasks on big data demonstrates that Gradient-HA method has less time complexity and better or comparable performance compared with other state-of-the-art functional alignment methods.


Hyperalignment Gradient ICA Brain decoding Classification 



This work was supported in part by the National Natural Science Foundation of China (61473149, 61422204, and 61732006).


  1. 1.
    Logothetis, N.K.: The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal. Philos. Trans. R. Soc. Lond. 357(1424), 1003 (2002)CrossRefGoogle Scholar
  2. 2.
    Haxby, J.V., Connolly, A.C., Guntupalli, J.S.: Decoding neural representational spaces using multivariate pattern analysis. Ann. Rev. Neurosci. 37(37), 435–456 (2014)CrossRefGoogle Scholar
  3. 3.
    Yousefnezhad, M., Zhang, D.: Local discriminant hyperalignment for multi-subject fMRI data alignment. In: 34th AAAI Conference on Artificial Intelligence, pp. 59–61, San Francisco, USA (2017)Google Scholar
  4. 4.
    Chen, P.H., Chen, J., Yeshurun, Y., Hasson, U., Haxby, J.V., Ramadge, P.J.: A reduced-dimension fMRI shared response model. In: 28th Advances in Neural Information Processing Systems, Canada, pp. 460–468 (2015)Google Scholar
  5. 5.
    Chen, P.H., Guntupalli, J.S., Haxby, J.V., Ramadge, P.J.: Joint SVD-Hyperalignment for multi-subject FMRI data alignment. In: 24th IEEE International Workshop on Machine Learning for Signal Processing, France, pp. 1–6 (2014)Google Scholar
  6. 6.
    Haxby, J.V., Guntupalli, J.S., Connolly, A.C., et al.: A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72(2), 404–416 (2011)CrossRefGoogle Scholar
  7. 7.
    Laitinen, L.: Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system: an approach to cerebral imaging. Clin. Neurol. Neurosurg. 91(3), 277–278 (1989)CrossRefGoogle Scholar
  8. 8.
    Watson, J.D.R., Myers, R., Frackowiak, R.S., et al.: Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cereb. Cortex 3(2), 79–94 (1993)CrossRefGoogle Scholar
  9. 9.
    Rademacher, J., Caviness, V.S., Steinmetz, H., et al.: Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology. Cereb. Cortex 3(4), 313–329 (1993)CrossRefGoogle Scholar
  10. 10.
    Xu, H., Lorbert, A., Ramadge, P.J., Guntupalli, J.S., Haxby, J.V.: Regularized hyperalignment of multi-set fMRI data. In: IEEE Statistical Signal Processing Workshop, USA, pp. 229–232 (2012)Google Scholar
  11. 11.
    Guntupalli, J.S., Hanke, M., Halchenko, Y.O., et al.: A model of representational spaces in human cortex. Cereb. Cortex 26(6), 2919–2934 (2016)CrossRefGoogle Scholar
  12. 12.
    Lorbert, A., Ramadge, P.J.: Kernel hyperalignment. In: 25th Advances in Neural Information Processing Systems, Harveys, pp. 1790–1798 (2012)Google Scholar
  13. 13.
    Chen, P.H., et al.: A convolutional autoencoder for multi-subject fMRI data aggregation. In: 29th Workshop of Representation Learning in Artificial and Biological Neural Networks, Barcelona (2016)Google Scholar
  14. 14.
    Zhang, H., Chen, P.H., et al.: A searchlight factor model approach for locating shared information in multi-subject fMRI analysis. arXiv preprint arXiv:1609.09432 (2016)
  15. 15.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis, 7th edn. Wiley, New York (2001)CrossRefGoogle Scholar
  16. 16.
    Yousefnezhad, M., Zhang, D.: Deep Hyperalignment. In: Conference on Neural Information Processing Systems, USA (2017)Google Scholar
  17. 17.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Gower, J.C., Dijksterhuis, G.B.: Procrustes Problems, 1st edn. Oxford University Press, Oxford (2004)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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