Task-based fMRI has been widely studied to investigate individual behavioral and cognitive traits. Integrating multiparadigm fMRI has been proven powerful in analyzing brain development, where a variety of multi-view learning methods have been developed. Among them, collaborative regression (CoRe) combines linear regression with canonical correlation analysis (CCA) to jointly analyze multiple views of data. CoRe links multi-paradigm imaging data with phenotypical information while also enforces their agreement across multiple views. However, CoRe overlooks group structures within regions of interest (ROIs) within the brain. To address this, we proposed a novel model, namely structure-enforced collaborative regression (SCoRe), to take advantage of group structures within each view of fMRI. The model was obtained by imposing a sparse group LASSO penalty on the regression term. Our model was validated on the Philadelphia Neurodevelopmental Cohort dataset by combining multi-task fMRI data to study an individual’s cognitive skills. Specifically, we adopted Wide Range Assessment Test 4 (WRAT4) scores to divide 338 participants into two groups (limited and proficient cognitive skill) and applied SCoRe to identify significant brain regions that can separate them. Through data resampling and significance analysis, we identified 17 brain regions from two paradigms of fMRI as biomarkers associated with an individual’s academic ability. Among them, 5 ROIs are shared by both paradigms. The study may also help understand the mechanisms underlying brain development.