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Abstract #2291

Searchlight Goes GPU - Fast Multi-Voxel Pattern Analysis of fMRI Data

Anders Eklund1, Malin Bjrnsdotter2, 3, Johannes Stelzer4, Stephen LaConte1, 5

1Virginia Tech Carilion Research Institute, Roanoke, VA, United States; 2University of Gothenburg, Gteborg, Sweden; 3Nanyang Technological University, Singapore, Singapore; 4Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; 5School of Biomedical Engineering & Sciences, Virginia Tech-Wake Forest University, Blacksburg, VA, United States

The searchlight algorithm is a popular choice for locally-multivariate decoding of fMRI data. A substantial drawback of searchlight is the increase in computational complexity, compared to the univariate general linear model. This is especially true for large searchlight spheres, non-linear classifiers, cross validation schemes and statistical permutation testing. Here we therefore present a graphics processing unit (GPU) implementation of the searchlight algorithm, to enable fast locally-multivariate fMRI analysis. The GPU implementation is 21 times faster than a multithreaded Matlab implementation. This makes it possible to apply 10 000 permutations with leave-one-out cross-validation in about 19 minutes.