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Gradient-Based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11258)

Abstract

Representational Similarity Analysis (RSA) aims to explore similarities between neural activities of different stimuli. Classical RSA techniques employ the inverse of the covariance matrix to explore a linear model between the neural activities and task events. However, calculating the inverse of a large-scale covariance matrix is time-consuming and can reduce the stability and robustness of the final analysis. Notably, it becomes severe when the number of samples is too large. For facing this shortcoming, this paper proposes a novel RSA method called gradient-based RSA (GRSA). Moreover, the proposed method is not restricted to a linear model. In fact, there is a growing interest in finding more effective ways of using multi-subject and whole-brain fMRI data. Searchlight technique can extend RSA from the localized brain regions to the whole-brain regions with smaller memory footprint in each process. Based on Searchlight, we propose a new method called Spatiotemporal Searchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm to the whole-brain data. Further, our approach can handle some computational challenges while dealing with large-scale, multi-subject fMRI data. Experimental studies on multi-subject datasets confirm that both proposed approaches achieve superior performance to other state-of-the-art RSA algorithms.

Keywords

RSA Gradient Searchlight Whole-brain fMRI data 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant (61876082, 61861130366, 61703301, and 61473149), the Fundamental Research Funds for the Central Universities and the Foundation of Graduate Innovation Center in NUAA (kfjj20171609).

References

  1. 1.
    Kriegeskorte, N., Goebel, R., Bandettini, P.: Information-based functional brain mapping. Proc. Natl. Acad. Sci. U. S. A. 103(10), 3863–3868 (2006)CrossRefGoogle Scholar
  2. 2.
    Connolly, A.C., et al.: The representation of biological classes in the human brain. J. Neurosci. 32(8), 2608–2618 (2012)CrossRefGoogle Scholar
  3. 3.
    Kriegeskorte, N., Mur, M., Bandettini, P.A.: Representational similarity analysis-connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008)CrossRefGoogle Scholar
  4. 4.
    Yousefnezhad, M., Zhang, D.: Anatomical pattern analysis for decoding visual stimuli in human brains. Cogn. Comput. 10(2), 284–295 (2018)CrossRefGoogle Scholar
  5. 5.
    Peelen, M.V., Caramazza, A.: Conceptual object representations in human anterior temporal cortex. J. Neurosci. 32(45), 15728–15736 (2012)CrossRefGoogle Scholar
  6. 6.
    Kravitz, D.J., Peng, C.S., Baker, C.I.: Real-world scene representations in high-level visual cortex: it’s the spaces more than the places. J. Neurosci. 31(20), 7322–7333 (2011)CrossRefGoogle Scholar
  7. 7.
    Cai, M.B., Schuck, N.W., Pillow, J.W., Niv, Y.: A Bayesian method for reducing bias in neural representational similarity analysis. In: Advances in Neural Information Processing Systems, pp. 4951–4959 (2016)Google Scholar
  8. 8.
    Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)CrossRefGoogle Scholar
  9. 9.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 68(1), 49–67 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wasserman, E.A., Chakroff, A., Saxe, R., Young, L.: Illuminating the conceptual structure of the space of moral violations with searchlight representational similarity analysis. NeuroImage 159, 371–387 (2017)CrossRefGoogle Scholar
  12. 12.
    Handjaras, G., et al.: How concepts are encoded in the human brain: a modality independent, category-based cortical organization of semantic knowledge. Neuroimage 135, 232–242 (2016)CrossRefGoogle Scholar
  13. 13.
    Huth, A.G., Nishimoto, S., Vu, A.T., Gallant, J.L.: A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron 76(6), 1210–1224 (2012)CrossRefGoogle Scholar
  14. 14.
    Su, L., Fonteneau, E., Marslen-Wilson, W., Kriegeskorte, N.: Spatiotemporal searchlight representational similarity analysis in EMEG source space. In: 2012 International Workshop on Pattern Recognition in Neuroimaging (PRNI), pp. 97–100. IEEE (2012)Google Scholar
  15. 15.
    Tamir, D.I., Thornton, M.A., Contreras, J.M., Mitchell, J.P.: Neural evidence that three dimensions organize mental state representation: rationality, social impact, and valence. Proc. Natl. Acad. Sci. 113(1), 194–199 (2016)CrossRefGoogle Scholar
  16. 16.
    Chavez, R.S., Heatherton, T.F.: Representational similarity of social and valence information in the medial pFC. J. Cogn. Neurosci. 27(1), 73–82 (2015)CrossRefGoogle Scholar
  17. 17.
    Oswal, U., Cox, C., Lambon-Ralph, M., Rogers, T., Nowak, R.: Representational similarity learning with application to brain networks. In: International Conference on Machine Learning, pp. 1041–1049 (2016)Google Scholar

Copyright information

© 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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