Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns

Recent studies have used pattern classification algorithms to predict or decode task parameters from individual fMRI activity patterns. For fMRI decoding, it is important to choose an appropriate set of voxels (or features) as inputs to the decoder, since the presence of many irrelevant voxels could lead to poor generalization performance, a problem known as overfitting. Although individual voxels could be chosen based on univariate statistics, the resulting set of voxels could be suboptimal if correlations among voxels carry important information. Here, we propose a novel linear classification algorithm, called sparse logistic regression (SLR), that automatically selects relevant voxels while estimating their weight parameters for classification. Using simulation data, we confirmed that SLR can automatically remove irrelevant voxels and thereby attain higher classification performance than other methods in the presence of many irrelevant voxels. SLR also proved effective with real fMRI data obtained from two visual experiments, successfully identifying voxels in corresponding locations of visual cortex. SLR-selected voxels often led to better performance than those selected based on univariate statistics, by exploiting correlated noise among voxels to allow for better pattern separation. We conclude that SLR provides a robust method for fMRI decoding and can also serve as a stand-alone tool for voxel selection.