Regularized hyperalignment of multi-set fMRI data

Hao Xu, Alexander Lorbert, Peter J. Ramadge, J. Swaroop Guntupalli, James V. Haxby

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations


Inter-subject correspondence is an important aspect of multi-subject fMRI studies. Recently, a new approach, called hyperalignment, has shown very promising results in fMRI functional alignment. Hyperalignment is based on Procrustean rotations and is connected, mathematically, to canonical correlation analysis. We review the core details of each approach, relate them through an SVD analysis, and indicate why they can yield different levels of performance. We then examine the effectiveness of regularization in mediating between the extremes of these methods. An inter-subject classification experiment based on functional aligned fMRI datasets illustrates the resulting improved performance.

Original languageEnglish (US)
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Number of pages4
StatePublished - 2012
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012


Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Country/TerritoryUnited States
CityAnn Arbor, MI

All Science Journal Classification (ASJC) codes

  • Signal Processing


  • Alignment
  • Canonical Correlation
  • Procrustes Problems
  • fMRI


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