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Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalization

Hervé Abdi, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini, Joseph P. Dunlop, James V. Haxby
2012 Computational and Mathematical Methods in Medicine  
We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants  ...  Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories.  ...  Conclusion Multiple subject barycentric discriminant analysis is particularly well suited for the analysis of neuroimaging data because it does not require brains to be spatially normalized.  ... 
doi:10.1155/2012/634165 pmid:22548125 pmcid:PMC3328164 fatcat:vmtqh4iztzbuhmq2gpczkykdlm

Decoding Neural Representational Spaces Using Multivariate Pattern Analysis

James V. Haxby, Andrew C. Connolly, J. Swaroop Guntupalli
2014 Annual Review of Neuroscience  
For personal use only.  ...  A major challenge for systems neuroscience is to break the neural code.  ...  Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): how to assign scans to categories without using spatial normalization. Comp. Math.  ... 
doi:10.1146/annurev-neuro-062012-170325 pmid:25002277 fatcat:ah6sfup2mrct7bkg2kel37z3w4