Several methods have previously been proposed for mapping and enabling the understanding of the brain’s organization. A widely used class of such methods consists in reconstructing brain functional connectivity networks from imaging data, such as fMRI data, which is then analysed with appropriate graph theory algorithms. If the imaging datasets are acquired at high resolution, the complexity of the problem both in spatial as well as temporal terms becomes very high. In this work, brain images were acquired using high-field scanners that produce very high resolution fMRI datasets. In order to address the resulting complexity issues, we developed a tool that is able to reconstruct the brain connectivity network from the high resolution images and analyse it in terms of the network’s information flowing efficiency and also of the network’s organization in functional modules. We were able to see that, although the networks are very complex, there is an apparent underlying organization. The corresponding structure allows the information to flow from one point to another in a very efficient manner. We were also able to see that these networks have a modular structure, which is in accordance with previous findings.