Exploring human brain activation via nested sparse coding and functional operators

Shu Zhang, Xiang Li, Lei Guo, Tianming Liu

Abstract: Traditional task-based fMRI activation detection methods, such as the general linear model (GLM), assume that the fMRI signals of activated brain regions follow the external stimulus paradigm. Typically, these activated regions are detected independently in a voxel-wise fashion, and the interaction among voxels is nevertheless neglected. Despite the wide use and remarkable success of GLM, the temporal and spatial relationships among activated regions remain unveiled. In response to this challenge, we present a novel method that combines two-stage sparse representation framework and the operator modulations (integral and derivative) to explore the temporal and spatial organizations underlying fMRI-derived activations in the brain. The two-stage sparse representation framework is designed to deal with big data and the functional operator is focused on finding the refined activation areas in the brain under task performances. Experiments demonstrated that diverse temporal and spatial organizations between activated regions exist and different functional operators may lead to different activation areas, thus significantly supplementing to the available principle of GLM that has been widely used in the human brain mapping field.