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 studies. A crucial step consists in the choice of methods for the estimation of responses and their statistical significance. However, a systematic comparison of these and their adequacy to predominant experimental design is missing.\n\nIn the current study, we compared three pattern estimation methods: Least-Squares Unitary (LSU), based on run-wise estimation, Least-Squares All (LSA) and Least-Squares Separate (LSS), which rely on trial-wise estimation. We compared the efficiency of these methods in an experiment where sustained activity had to be isolated from zero-duration events as well as in a block-design approach and in an event-related design. We evaluated the sensitivity of the t-test in comparison with two non-parametric methods based on permutation testing: one proposed in Stelzer et al. (2013), equivalent to performing a permutation in each voxel separately and the Threshold-Free Cluster Enhancement (Smith and Nichols, 2009).\n\nLSS resulted the most accurate 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 with the other approaches were now marked as informative regions.\n\nOur results provide evidence that LSS is the most accurate approach for unmixing events with different duration and large overlap of signal, consistent with previous studies showing better handling of collinearity in LSS. Moreover, Stelzers potentiates this better estimation with its larger sensitivity.