Influence of activation pattern estimates and statistical significance tests in fMRI decoding analysis

Abstract

The use of Multi-Voxel Pattern Analysis (MVPA) has increased considerably in recent functional magnetic resonance imaging (fMRI) studies. A crucial step consists in the choice of a method for the estimation of responses. However, a systematic comparison of the different estimation alternatives and their adequacy to predominant experimental design is missing. In the current study we contrasted three pattern estimation methods: Least- Squares Unitary (LSU), based on run-wise estimation, and Least-Squares All (LSA) and Least-Squares Separate (LSS), which rely on trial-wise estimation. We contrasted the efficiency of these methods in an experiment where sustained activity needed to be isolated from zero-duration events as well as in a block- design approach and in a event-related design. We evaluated the sensitivity of the t-test with two non-parametric methods based on permutation testing: one proposed in Stelzer et al. (2013), equivalent to perform a permutation in each voxel separately and the Threshold-Free Cluster Enhancement. LSS resulted the most reliable approach to address the large overlap of signal among close events in the event-related designs. We found a larger sensitivity of Stelzers method in all settings, especially in the event-related designs, where voxels close to surpass the statistical threshold in other approaches were now marked as informative regions. Our results provide evidence that LSS is the most reliable approach for unmixing events with different duration and large overlap of signal. This is consistent with previous studies where LSS handles large collinearity better than other methods. Moreover, Stelzers potentiates this better estimation with its large sensitivity.