The PhD thesis of Alessio Basti focused on the development of computational methods for functional neuroimaging. The thesis first introduces the reader to the concept of functional connectivity and the multivariate nature of neural signals. A new data analysis method called Multivariate Phase Slope Index (MPSI) is then described. The MPSI is a generalised version of the already defined Phase Slope Index (PSI) and it is able to assess the directionality of phase interactions between brain regions by using multivariate neural signals. Finally, an approach for reliably estimating linear transformations between multivariate-fMRI data in pairs of regions of interests is described. Based on these linear mappings, three novel metrics: goodness-of-fit, sparsity and pattern deformation are introduced. In conclusion the thesis describes new and improved analysis methods for assessing the inter-areal functional connectivity specifically addressing the multivariate nature of neural signals.