Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study
This work addresses methodological challenges for researchers using causal discovery in fMRI studies, but it is incremental as it reviews and synthesizes existing issues rather than introducing new solutions.
The paper tackles the complexity of applying causal discovery to fMRI data by describing nine challenges, discussing decision spaces, reviewing a case study, and identifying gaps for new methods, noting that while causal discovery shows promise and superiority over traditional functional connectivity methods, current approaches need improvement.
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.