fMRI resting state networks define distinct modes of long-distance interactions in the human brain

By M. De Luca, Christian F. Beckmann, Nicola De Stefano, Paul M. Matthews, Stephen M. Smith. NeuroImage, 2006

1,280 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d435ebe894cbef8c03b055eec2ce47e7a66f6525

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In Cognitive Neuroscience

Citations in the corpus

Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies (2009, neutral): “…can be clearly rejected as significance of correlations between rM1 seed voxel signal and contralateral M1 and S2 was only slightly higher compared to functionally non-related regions like the visual cortex, an area that has been shown to be part of a different RSN (De Luca et al., 2006).”
Contributions and challenges for network models in cognitive neuroscience (2014, methodology): “RSNs can be detected with seedbased correlations 30 , independent component analysis 31 , connectivitybased cluster analysis 32 and networkbased community detection 33 .”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “work is highly consistent across various studies and spans much of the occipital cortex (Fig 1C).(2-6) An auditory network consisting of From the Mallinckrodt Institute of Radiology (M.”
EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement (2009, positive): “ICA within this study reproduced five resting state networks which have been reported elsewhere (Mantini et al., 2007b; De Luca et al., 2006; Greicius et al., 2003).”
Anticorrelations in resting state networks without global signal regression (2011, positive): “Resting-state fMRI has revealed that signals in functionally related brain regions correlate with each other even in the absence of external stimuli (Beckmann et al., 2005; Biswal et al., 1995; De Luca et al., 2006; Fox et al., 2005; Fransson, 2005; Greicius et al., 2003).”
Hand classification of fMRI ICA noise components (2017, neutral): “They are described both at group level (Beckmann et al., 2005; De Luca et al., 2006), and at the single subject level (Beckmann, 2012; De Martino et al., 2007; Kelly et al., 2010; Rummel et al., 2013; Salimi-Khorshidi et al., 2014; Smith et al., 2013).”
The intrinsic functional organization of the brain is altered in autism (2008, background): “…attention networks (Fox et al., 2006b), recollection memory networks (Vincent et al., 2006)), including the TPN (Fox et al., 2005, 2006b; Fransson, 2005) and TNN (Greicius et al., 2003; Greicius and Menon, 2004; Fox et al., 2005; Fransson, 2005, 2006; Damoiseaux et al., 2006; De Luca et al., 2006).”
Functional segmentation of the brain cortex using high model order group PICA (2009, methodology): “It has been suggested that 10 to 12 RSNs can be detected from the brain cortex from resting state BOLD data, using ICA with a component dimensionality, i.e. a model order, around 25–40 components [Beckmann et al., 2005; De Luca et al., 2006; Damoiseaux et al., 2006].”
Disease state prediction from resting state functional connectivity (2009, background): “Resting-state functional connectivity (FC) exists in a variety of known brain networks (1,5) and is consistent across subjects (6,7).”
The role of physiological noise in resting-state functional connectivity (2012, positive): “Nevertheless, the resulting networks appear to be consistent, reliable, and conform to known task-related networks (Damoiseaux et al., 2006; De Luca et al., 2006; Shehzad et al., 2009; Smith et al., 2009).”
CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. (2007, background): “1), or resting-state network components [21] (Component 9 in Fig.”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, methodology): “ICA-based methods (Beckmann et al., 2005; Calhoun et al., 2001; De Luca et al., 2006; van de Ven et al., 2004) are perhaps the most commonly used and have been reported to show a high level of consistency (Damoiseaux et al., 2006).”
Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control (2007, methodology): “The results highlight the distributed regional architecture and functional relevance of two ICNs critical for adaptive human behavior.”