Frontiers in Systems Neuroscience Systems Neuroscience

Abstract

non-Gaussianity of the source signals. Spatial domain ICA (sICA) can separate BOLD signal sources that represent reactions to externally cued task-activations, background activity within functional brain (i.e., resting state) networks (RSN), and various physiological noise and artifact sources (McKeown et al. ICA methodology yields results that are consistent with the results of other contemporary methods of detecting large scale temporally coherent networks from the BOLD signal data (Long et al., 2008). Recently it has been shown that at least some 42 robust RSNs can be separated from group ICA runs when the algorithm is given the task to search for high model order (Kiviniemi et al., 2009; Smith et al., 2009). When the model order of the ICA estimation is increased, the separated BOLD signal sources have been shown to split into several functional nodes (Li et al., 2007; Ma et al., 2007; Malinen et al., 2007; Eichele et al., 2008). Higher ICA model order (≈70) enables the detection of sub-networks and other independent sources not detected in lower model orders without overfitting the data (Ma et al., 2007; Malinen et al., 2007; Abou-Elseoud et al., 2010) Recently a large data collection of over 1000 subjects was able to show age-related differences in the brain networks (Biswal et al., 2010). There are few studies about the functional connectivity development