A Multi-stage Sparse Coding Framework to Explore the Effects of Prenatal Alcohol Exposure

In clinical neuroscience, task-based fMRI (tfMRI) is a popular method to explore the brain network activation difference between healthy controls and brain diseases like Prenatal Alcohol Exposure (PAE). Traditionally, most studies adopt the general linear model (GLM) to detect task-evoked activations. However, GLM has been demonstrated to be limited in reconstructing concurrent heterogeneous networks. In contrast, sparse representation based methods have attracted increasing attention due to the capability of automatically reconstructing concurrent brain activities. However, this data-driven strategy is still challenged in establishing accurate correspondence across individuals and characterizing group-wise consistent activation maps in a principled way. In this paper, we propose a novel multi-stage sparse coding framework to identify group-wise consistent networks in a structured method. By applying this novel framework on two groups of tfMRI data (healthy control and PAE), we can effectively identify group-wise consistent activation maps and characterize brain networks/regions affected by PAE.