2012
DOI: 10.1016/j.jneumeth.2012.05.007
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A Fast-FENICA method on resting state fMRI data

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Cited by 27 publications
(26 citation statements)
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“…As expected, some typical RSNs reported by many previous researches Damoiseaux et al, 2006;Schöpf et al, 2010; A c c e p t e d M a n u s c r i p t 38 Wang et al, 2012aWang et al, , 2013 were successfully discovered such as VIN (a predominantly occipital network, Fig.11A), LVN (a lateral visual network involving the lateral and superior occipital gyrus, Fig.11B), DMN (a default mode network involving primarily precuneus and prefrontal lobe and parietal regions, Fig.11C), AUN (a network involving the auditory regions, Fig.11D), ECN (an executive control network including superior and middle prefrontal cortex, anterior cingulate and paracingulate gyri, Fig.11E), DPN1 and DPN2 (networks mainly involving dorsal parietal cortex, In addition, Fig. 11K depicted a network including the superior temporal (BA 22) and…”
Section: Resting-state Data Resultssupporting
confidence: 82%
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“…As expected, some typical RSNs reported by many previous researches Damoiseaux et al, 2006;Schöpf et al, 2010; A c c e p t e d M a n u s c r i p t 38 Wang et al, 2012aWang et al, , 2013 were successfully discovered such as VIN (a predominantly occipital network, Fig.11A), LVN (a lateral visual network involving the lateral and superior occipital gyrus, Fig.11B), DMN (a default mode network involving primarily precuneus and prefrontal lobe and parietal regions, Fig.11C), AUN (a network involving the auditory regions, Fig.11D), ECN (an executive control network including superior and middle prefrontal cortex, anterior cingulate and paracingulate gyri, Fig.11E), DPN1 and DPN2 (networks mainly involving dorsal parietal cortex, In addition, Fig. 11K depicted a network including the superior temporal (BA 22) and…”
Section: Resting-state Data Resultssupporting
confidence: 82%
“…Recently, the independent component analysis (ICA, spatial/temporal ICA) has been widely used to analyze the blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data, aiming at detecting functional connectivity among discrete cortical brain regions at single-subject level or multi-subject level, due to its fully data-driven feature without the prior neuroscience knowledge or experience (McKeown et al, 1998;Biswal et al, 1999;Beckmann & Smith, 2004Calhoun et al, 2001aCalhoun et al, , 2001bSchöpf et al, 2010;Wang et al, 2012a). In contrast to the univariate general linear model (GLM), which is performed on a voxel-by-voxel basis (Bagarinao et al, 2003), the ICA method is intrinsically a multivariate approach, which could extract multiple brain networks that are engaged in various elements of cognitive processing.…”
Section: Ica and Fmri Source Separationmentioning
confidence: 99%
“…7. Although the RSNs detected by our method are slightly less than those of [38,39], our results were obtained directly on the data after preprocessing without any other data transformation. For the same data, the results of FCM were unfavorable and the corresponding RSNs were difficult to identify.…”
Section: Arv-fwsc Fcmmentioning
confidence: 88%
“…Some typical RSNs (described in [38,39]) were discovered such as the default network (DMN), the visual network (VIN), the left working memory network (LWMN), the right working memory network (RWMN), the auditory network (AUN) and the basal ganglia network (BGN), which are shown in Fig. 7.…”
Section: Arv-fwsc Fcmmentioning
confidence: 99%
“…Due to its fully data-driven property, ICA (independent component analysis) has recently become a widely-used FC detection tool for fMRI data, [1][2][3][4][5][6][7][8][9]. ICA has been used to explore functional networks of the brain under different conditions such as the resting state [6][7][8]10], undergoing a cognitive task [11], or the mental disorder condition, e.g., autism spectrum disorder [12] and dementia [13]. Usually, after the ICs are extracted by ICA, the post-processing inference using statistical tricks is normally applied to ICs to obtain the SPMs (statistical parametric map), e.g., the z-score scale maps [1], which contribute to the real active brain regions inference as to certain cognitive activity.…”
Section: A Ica and Functional Connectivitymentioning
confidence: 99%