I started my thesis by improving decomposition techniques for fMRI. We introduced new scalable algorithms to extract functional networks from fMRI recordings. These algorithms can decompose recent massive datasets (eg HCP) in a reasonable time (~10h). They have a theoretical grounding and can be applied to other problems (e.g. collaborative filtering) as well.
I am now involved in setting up richer decoding models for task fMRI datasets, that operates at the repository level to learn joint models of cognition.
Please visit amensch.fr for more details on my work !