As the focus of neuroscience shifts from studying individual brain regions to entire networks of regions, methods for statistical inference have also become geared toward network analysis. The purpose of the present review is to survey the multivariate statistical techniques that have been used to study neural interactions. We have selected the most common techniques and developed a taxonomy that instructively reflects their assumptions and practical use. For each family of analyses, we describe their application and the types of experimental questions they can address, as well as how they relate to other analyses both conceptually and mathematically. We intend to show that despite their diversity, all of these techniques offer complementary information about the functional architecture of the brain.