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Hyperalignment of Multi-subject fMRI Data by Synchronized Projections

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Machine Learning and Interpretation in Neuroimaging (MLINI 2013, MLINI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9444))

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Abstract

Group analysis of fMRI data via multivariate pattern methods requires accurate alignments between neuronal activities of different subjects in order to attain competitive inter-subject classification rates. Hyperalignment, a recent technique pioneered by Haxby and collaborators, aligns the activations of different subjects by mapping them into a common abstract high-dimensional space. While hyperalignment is very successful in terms of classification performance, its “anatomy free” nature excludes the use of potentially helpful information inherent in anatomy. In this paper, we present a novel approach to hyperalignment that allows incorporating anatomical information in a non-trivial way. Experiments demonstrate the effectiveness of our approach over the original hyperalignment and several other natural alternatives.

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Acknowledgments

The authors acknowledge the support of NSF grants FODAVA 808515 and DMS 1228304, AFOSR grant FA9550-12-1-0372, ONR grant N00014-13-1-0341, a Google research award, and the Max Plack Center for Visual Computing and Communications.

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Authors and Affiliations

  1. Computer Science Department, Stanford University, Stanford, USA

    Raif M. Rustamov & Leonidas Guibas

Authors
  1. Raif M. Rustamov
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  2. Leonidas Guibas
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Corresponding author

Correspondence to Raif M. Rustamov .

Editor information

Editors and Affiliations

  1. IBM T.J. Watson Research Center , Yorktown Heights, New York, USA

    Irina Rish

  2. Medical University of Vienna , Vienna, Austria

    Georg Langs

  3. University of California, Berkeley, California, USA

    Leila Wehbe

  4. T.J. Watson Research Center , Yorktown Heights, New York, USA

    Guillermo Cecchi

  5. Carnegie Mellon University , Pittsburgh, Pennsylvania, USA

    Kai-min Kevin Chang

  6. Queen's University Belfast , Belfast, United Kingdom

    Brian Murphy

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© 2016 Springer International Publishing AG

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Rustamov, R.M., Guibas, L. (2016). Hyperalignment of Multi-subject fMRI Data by Synchronized Projections. In: Rish, I., Langs, G., Wehbe, L., Cecchi, G., Chang, Km., Murphy, B. (eds) Machine Learning and Interpretation in Neuroimaging. MLINI MLINI 2013 2014. Lecture Notes in Computer Science(), vol 9444. Springer, Cham. https://doi.org/10.1007/978-3-319-45174-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-45174-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45173-2

  • Online ISBN: 978-3-319-45174-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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