Fig. 6. The static Gaussian null model can explain the spatial similarity between high-amplitude frames based on a seed node and the corresponding correlation map—a core feature of coactivation patterns (CAPs).

a For each seed node (i.e. parcel), the frames are sorted in descending order based on the BOLD activity of the seed (horizontal axis). The Pearson correlation is computed between each frame and the seed-based correlation map (i.e., the column of the nFC matrix corresponding to the seed). The curves represent the average similarity over all 200 seed nodes and over 100 HCP subjects. As predicted analytically, the similarity is proportional to the BOLD activity and it decreases as lower-amplitude frames are considered (lower percentiles). The null model is in excellent agreement with the empirical results. b Same as in panel (a), but the BOLD activity of all the frames above a given threshold (percentile) are averaged before computing the correlation with the seed-based FC map. Only a small fraction of high-amplitude frames is required to explain most of the nFC variance, reproducing a well-known result in the CAPs literature¹².