Figure 10.

Identifiability of network structure after denoising. Although denoising approaches remove motion artifacts from BOLD time series, it is possible that they also remove signal of interest. We quantified the retention of signal of interest using the modularity quality of the denoised connectome. (A) The modularity quality in a 264‐node network [Power et al., 2011] following confound regression. (B) The modularity quality in a second, 333‐node network [Gordon et al., 2014]. ICA‐, GSR‐, and tissue class‐based models performed relatively well, while models that allowed substantial noise to be retained (tCompCor, 6P, wmLocal, 24P; see Fig. 8) were less able to identify network substructure. Reprinted with permission from Ciric et al. [2017].