Multivariate Pattern Connectivity

Whenever we engage in a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural mechanisms that make behavior possible. The majority of the investigations of interactions between brain regions have focused on the overall univariate responses in the regions. However, in the context of ‘static’ analyses, drastic advantages have derived from the application of multivariate techniques considering the fine-grained spatial structure of responses within each region (multivariate pattern analysis MVPA). In the present article, we introduce and apply a technique to study connectivity in terms of the relations between multivariate patterns of responses within brain regions: multivariate pattern connectivity (MVPC). Considering the fusiform face area (FFA) as a seed region, we show that MVPC provides novel information about the interactions between regions that goes beyond univariate functional connectivity analyses.