The functioning of complex systems — such as the human brain, the climate, and many more — is characterised by emergent phenomena, which is a consequence of non-linear interactions between various of its constituent units. Crucially, the resulting dynamics observed in such systems is qualitatively different from the sum of the dynamics of each part. A promising way to deepen our understanding of these systems is to leverage existing datasets of these complex systems; however, the analysis of these data requires specific analytic tools to make sense of their highly non-trivial interplay. Several tools and frameworks have been developed to look at different statistical dependencies that can be observed in these fascinating multivariate datasets. Among these, information theory offers a powerful and versatile framework; notably it allows to detect and quantify high-order interactions: namely, the characteristic of the joint informational role of a group of variables. However, metrics of high-order interactions are challenging to visualize, and the lack of adequate visualization paradigms is hindering their potential impact in numerous research fields. While visualization is easy and well explored for local measures and pairwise connectivity ones, it becomes trickier when it comes to higher order measures. In fact, a lot of the appeal and understandability of these metrics data analyses (in general, and in particular in neuroimaging) relies on their visual representation, with or without an anatomical overlay.
Experience in statistics and neuroimaging data analysis is a plus, but not a requirement.