Resting-state functional Magnetic Resonance Imaging (fMRI) is widely used to infer brain functional connectivity networks. Such networks correlate neural signals to connect brain regions, which consist in groups of dependent voxels. Previous work has focused on aggregating variables within predefined regions. However, it can be shown the presence of within-region correlations has noticeable impacts on inter-regional correlation detection, and thus edge identification. To alleviate them, we propose to leverage the large-scale correlation screening literature, and derive simple and practical characterizations of the mean number of correlation discoveries that flexibly incorporate intra-regional dependence structures. This novel approach for handling arbitrary intra-regional correlation is shown to improve false positive and true positive rates. A connectivity network inference framework is then presented. First, inter-regional correlation distributions are estimated. Then, correlation thresholds are constructed for each edge, with false discovery control that can be tailored to one's application. Finally, the proposed framework is implemented on a real-world dataset.