“…Thus, faithful reconstruction and quantitative modeling of those concurrent neural networks from noisy fMRI data has been of a major neuroscientific research topic for years (Bullmore and Sporns, 2009; Dosenbach et al, 2006; Duncan, 2010; Fedorenko et al, 2013; Fox et al, 2005; Huettel, Scott A., Allen W. Song, 2004; Pessoa et al, 2012). Popular brain network reconstruction techniques based on fMRI data include general linear model (GLM) (Friston et al, 1994; Worsley, 1997) for task-based fMRI (tfMRI), independent component analysis (ICA) (Beckmann et al, 2005; Calhoun et al, 2004) for resting state fMRI (rsfMRI), and dictionary learning/sparse representation (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2016, 2015) for both tfMRI and rsfMRI, all of which can effectively reconstruct concurrent network maps from whole brain fMRI data. For instance, by using the dictionary learning and sparse coding algorithms (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Mairal et al, 2010; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2015), several hundred of concurrent functional brain networks, characterized by both spatial maps and associated temporal time series, can be effectively decomposed from either tfMRI or rsfMRI data of an individual brain.…”