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. Author manuscript; available in PMC: 2018 Jul 1.
Published in final edited form as: Neuroimage. 2017 Mar 14;154:174–187. doi: 10.1016/j.neuroimage.2017.03.020

Figure 6. Correlation between subject motion and modularity quality.

Figure 6

Motion affects network modularity to varying degrees for different de-noising approaches. We quantified the retention of signal of interest as the modularity quality of the de-noised connectome. A, The correlation between subject motion and modularity quality in a 264-node network defined by Power et al. (2011) following confound regression. B, The correlation between subject motion and modularity quality in a second, 333-node network defined by Gordon et al. (2016). In general, GSR- and ICA-based methods most effectively decoupled network structure from artifact.