As described above, univariate analyses carry some limitations that multivariate analysis is able to overcome. One of the most important MPVA findings is that representations are not restricted to specific areas and they are characterized by idiosyncratic patterns related to distinct tasks (or stimuli). However, in order to distinguish and to describe different mental content is also important to consider when and how neurons interact with each other (Vaadia et al., 1995). Neural activity, and by extension neural codes, are constrained by connectivity. Nowadays, it is clear that brain regions do not work in isolation, and information processing depends on continuous local and long-range interactions. The most common approach to investigate how specific neurons communicate with each other over time is functional connectivity. Generally, this term refers to the temporal covariance between distinct brain regions activity, which is assumed to be driven by their interaction. Traditional application of functional connectivity involves first the selection of “seed” regions of interest, based on their activity in specific tasks or coarse anatomical parcellation of the brain, and then the analysis of correlations between these regions and other voxels. This approach has provided many insights into the neural architecture, and identified several neural networks enabled during specific task. Still, it has some limitations. First, seeds are often defined on the basis of activation, and this procedure has the same disadvantage of univariate analysis, that is to assume that regions with greater activation (or activation differences) are most interactive or that their interactions are most informative., but voxels activation may differ between stimuli (or from baseline) in the absence of differences in the averaged time-locked amplitude. Some MVPA studies tried to address this issue by classifying patterns of correlations among multiple regions. Anyway, regardless of which method is used to analyze data (univariate or multivariate), seed-based functional connectivity is affected by the small number of regions selected (“seeds”) with respect to the total number of voxels, resulting in loss of information. Doing so, only a small subset of possible interactions is considered. Despite its limitations, this approach is widely used because it allows to avoid statistical challenges related to big data and to test specific models with greater power. Another reason is that comparing every voxel with one another is computationally demanding (50.000 voxels mean 1.249.975.000 unique voxel pairs to be tested). Notwithstanding, recent advances in computer engineering opened up new possibilities. One innovative method that takes into account the full set of voxels is the Full Correlation Matrix Analysis (FCMA) (Wang et al., 2015). To surmount the seed-based limitations, FCMA performs unbiased multivariate analysis of the whole-brain.