The functioning of complex systems (i.e. the brain, and many others) depends on the interaction between different units; crucially, the resulting dynamics is different from the sum of the dynamics of the parts. In order to deepen our understanding of these systems, we need to make sense of these interdependencies. Several tools and frameworks have been developed to look at different statistical dependencies among multivariate datasets. Among these, information theory offers a powerful and versatile framework; notably, it allows detecting high-order interactions that determine the joint informational role of a group of variables.
The goal of this project is to collect and refine existing metrics of high-order interactions currently implemented in Matlab or Java, for example
- GitHub - brincolab/High-Order-interactions: High-Order interactions
- GitHub - danielemarinazzo/dynamic_O_information: code for dynamic O information
- GitHub - jlizier/jidt: JIDT: Java Information Dynamics Toolkit for studying information-theoretic measures of computation in complex systems
and integrate them in an unified python-based toolbox.
The main deliverable of the project is a toolbox, whose inputs are the measurements of different variables plus some parameters, and whose outputs are the measures of higher order interdependencies. Ideally, the toolbox will interface with visualisation & processing platforms of neuroimaging data, such as MNE — MNE 0.22.0 documentation fMRIPrep: A Robust Preprocessing Pipeline for fMRI Data — fmriprep version documentation and become a docker container too. A parallel project would focus on the visualization of these higher order measures.
Experience in statistics and neuroimaging data analysis is a plus, but not a requirement.
Skills: neuroimaging, statistics, Python, Matlab, Java, MNE, fMRIprep