The acquisition of multiple imaging modalities on the same individual in brain imaging studies has become a common practice. In functional studies, often several different tasks are performed on the same person. This motivates an analysis method that directly looks for the joint (shared) information lying within multimodal datasets. The present work uses a data fusion framework called joint independent component analysis (jICA) to yield joint (multimodal), maximally independent components (ICs) which capture the joint information from multiple modalities and enable identification of brain imaging biomarkers. We thus propose the use of a divergence metric on the estimated group distributions as an optimization factor for this framework, thus characterizing the differences in the across-group distribution functions for each modality individually and jointly as well. Special attention is being devoted to the behavior aspects of the J-divergence and Alpha divergence (with α = 0.5) due to their metric property and optimality, respectively.