  Designing studies that apply causal discovery requires navigating many
researcher degrees of freedom. This complexity is exacerbated when the study
involves fMRI data. In this paper we (i) describe nine challenges that occur
when applying causal discovery to fMRI data, (ii) discuss the space of
decisions that need to be made, (iii) review how a recent case study made those
decisions, (iv) and identify existing gaps that could potentially be solved by
the development of new methods. Overall, causal discovery is a promising
approach for analyzing fMRI data, and multiple successful applications have
indicated that it is superior to traditional fMRI functional connectivity
methods, but current causal discovery methods for fMRI leave room for
improvement.
