A spatial-constrained multi-target regression model for human brain activity prediction

Analyzing functional magnetic resonance imaging (fMRI) data from the encoding perspective provides a powerful tool to explore human vision. Using voxel-wise encoding models, previous studies predicted the brain activity evoked by external stimuli successfully. However, these models constructed a regularized regression model for each single voxel separately, which overlooked the intrinsic spatial property of fMRI data. In this work, we proposed a multi-target regression model that predicts the activities of adjacent voxels simultaneously. Different from the previous models, the spatial constraint is considered in our model. The effectiveness of the proposed model is demonstrated by comparing it with two state-of-the-art voxel-wise models on a publicly available dataset. Results indicate that the proposed method can predict voxel responses more accurately than the competing methods.