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.

翻译：设计应用因果发现的研究需要应对诸多研究者自由度带来的复杂性。当研究涉及fMRI数据时，这种复杂性会进一步加剧。本文（i）描述了将因果发现应用于fMRI数据时遇到的九个挑战，（ii）探讨了所需决策的空间，（iii）回顾了近期案例研究如何做出这些决策，（iv）并指出了可通过开发新方法解决的现存空白。总体而言，因果发现是分析fMRI数据的一种有前景的方法，多项成功应用表明其优于传统的fMRI功能连接方法，但当前针对fMRI的因果发现方法仍有提升空间。