These results suggest distinctive patterns of FC change related to the use of alcohol and nicotine. One limitation of these important results is the use of predetermined regions of interest or seeds. As well described by Li, et al. (Li et al., 2009), seed-based methodology implies that detected effects in FC are sensitive to the selected seed and the knowledge used to determine the seed. Thus, it is also important to complement previous work with approaches that are less dependent on a specific seed assumption. In this work we utilize group independent component analysis (gICA) (Calhoun et al., 2001) to obtain a data driven parcellation of the brain into spatial-temporal components. Brain regions are segregated into maximally spatially independent components or networks (Erhardt et al., 2011a) in a fully data-driven manner. Using resting state fMRI data as input, the gICA algorithm provides a set of resting state networks (RSN), each composed of coherently interacting brain areas, each of which is a reflection of ‘within network’ connectivity (Joel et al., 2011). The temporal correlation between RSN time courses constitutes a measure of “among network” connectivity, called functional network connectivity (Jafri et al., 2008).
