of whole networks without the need for selection of a seed region of interest and can better account for artifacts compared to seed-based methods which can be influenced by spatial confounds [1]. ICA with subsequent analysis using the dual regression module of FSL software (www.fmrib.ox.ac.uk/fsl/) considers both amplitude and shape of the signal time-course of resting state networks [1,14]. Head motion can mimic amplitude effects, and dual regression can more accurately localize these amplitude effects of motion and thus avoid misinterpreting them as differences in connectivity [14].
