Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM.

ABSTRACT: Permutation testing is a non-parametric method for obtaining the max null distribution used to compute corrected p-values that provide strong control of false positives. In neuroimaging, however, the computational burden of running such an algorithm can be significant. We find that by viewing the permutation testing procedure as the construction of a very large permutation testing matrix, T, one can exploit structural properties derived from the data and the test statistics to reduce the runtime under certain conditions. In particular, we see that T is low-rank plus a low-variance residual. This makes T a good candidate for low-rank matrix completion, where only a very small number of entries of T (∼0.35% of all entries in our experiments) have to be computed to obtain a good estimate. Bas

SUBMITTER: Gutierrez-Barragan F 

PROVIDER: S-EPMC5671355 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature