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. 2019 Feb 4;29(11):4595–4612. doi: 10.1093/cercor/bhy336

Figure 2.

Figure 2.

The effect of denoising on RSFC-CBP and on voxel functional properties. (A) Most stable hippocampal parcellations across all levels of partition (k = 2–7) were obtained with FIX + WM/CSF, GSR, and WM/CSF regression as denoising approaches. Bars indicate mean ARI (±standard errors). Independent of denoising technique the highest stability was acquired for 6 clusters. All comparisons were statistically significant. (B) Seed voxels’ time course similarity was reduced after the application of denoising. No significant difference was observed between FIX + GSR and FIX + WM/CSF, whereas all the other comparisons were significant. (C) Denoising resulted in an increase in seed voxels’ dissimilarity in comparison to uncleaned data. FIX-related strategies demonstrated the strongest effect of connectivity profile dissimilarity. (D) FIX + WM/CSF showed the highest stability across all levels of partition (k = 2–7) compared with other denoising techniques. Mean ARI (±standard errors). All comparisons were statistically significant.