Functional network from ICA using fMRI data has been applied to identify biomarkers of brain disorders. However, the networks from ICA might be slightly different, making the comparison of results across different studies/diseases difficult. We propose a data-driven framework to estimate functional network maps and their inter-connectivity for linking neuromarkers among different disorders and studies. Our method is capable of computing functional networks which are optimized for independence based on each coming individual-subject data, and remaining their correspondence across different subjects by using unbiased templates. The results show this approach is an effective method for studying and classifying multiple-disorders.