Fast bootstrapping and permutation testing for assessing reproducibility and interpretability of multivariate fMRI decoding models.

ABSTRACT: Multivariate decoding models are increasingly being applied to functional magnetic imaging (fMRI) data to interpret the distributed neural activity in the human brain. These models are typically formulated to optimize an objective function that maximizes decoding accuracy. For decoding models trained on full-brain data, this can result in multiple models that yield the same classification accuracy, though some may be more reproducible than others--i.e. small changes to the training set may result in very different voxels being selected. This issue of reproducibility can be partially controlled by regularizing the decoding model. Regularization, along with the cross-validation used to estimate decoding accuracy, typically requires retraining many (often on the order of thousands) of related

SUBMITTER: Conroy BR 

PROVIDER: S-EPMC3828388 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature