Network centrality in the human functional connectome.

By Xi-Nian Zuo, Ross C Ehmke, Maarten Mennes, Davide Imperati, F. Castellanos, Olaf Sporns, Michael P. Milham. Cerebral Cortex, 2012

1,147 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/873af2ddbd126938bb3eb9b01e8d5a13e052c8ac

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

The structural-functional connectome and the default mode network of the human brain (2014, positive): “Later, the concept was extended to include functional connectivity, i.e. the functional connectome (Biswal et al., 2010; Zuo et al., 2011).”
Changes in structural and functional connectivity among resting-state networks across the human lifespan (2014, background): “…attributes, including small-worldness (Achard and Bullmore, 2006; Gong et al., 2009), hubs and cores (Achard et al., 2006; Hagmann et al., 2008; Zuo et al., 2012), a structural rich club (van den Heuvel and Sporns, 2011), modular architecture (Meunier et al., 2009, 2010), and economicwiring…”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Furthermore, other commonly used indexes for measuring the nodal centrality include betweenness, closeness, and eigenvector, participation coeﬃcient, and PageRank (Boccaletti et al., 2006; Rubinov and Sporns, 2010; Zuo et al., 2012; Figure 5B ).”
Making big data open: data sharing in neuroimaging (2014, methodology): “The 1,000 Functional Connectomes project 25 serves as an excellent example; data from this project have been used in a number of papers to demonstrate the application of new methods 26–29 .”
Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, result): “Two other recent studies took a different approach and included a summary measure of subject motion as a confounding variable in the group-level regression (Zuo et al., 2010; Zuo et al., 2011).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “The human functional connectome is conceived as encompassing of brain functional networks (Biswal et al., 2010; Kelly et al., 2012; Milham, 2012; Zuo et al., 2012), and has been extensively explored by using resting state functional connectivity (RSFC) approaches (Biswal et al.”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, neutral): “Fourth, we set a p threshold for degree centrality calculation to maintain a constant probability of false positives across datasets, despite differing number of time points (Zuo et al., 2012).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, background): “While some work has focused on parcellating the brain into a small number of (less than twenty) functional systems (van den Heuvel et al. 2008; Bellec et al. 2010; Power et al. 2011; Lee et al. 2012; Zuo et al. 2012; Hacker et al. 2013), others have focused on finer subdivisions (Cohen et al.”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Returning to the MRI literature, there is convergent evidence that the locations of high-strength and high-betweenness hub regions appear to be stable after 5 years of age (Cao et al., 2014; Grayson et al., 2014; Hwang et al., 2013; Wang et al., 2012; Wu et al., 2013; Zuo et al., 2012).”
Vital nodes identification in complex networks (2016, background): “It has been applied to rank a broad range of objects via their network structure: to rank images [102] and books [103], to rank genes [104] and proteins [105] in Biology and Bioinformatics, to rank molecules [106] in Chemistry, to rank brain regions [107] and neurons [108] in Neuroscience, to rank h”
Tracking whole-brain connectivity dynamics in the resting state. (2014, positive): “2009) or regions of high network centrality (Zuo et al. 2012) in topological descriptions of brain”
Cognitive Network Neuroscience (2015, background): “Average network structures created by taking the mean of individual networks within the group (Zuo et al., 2012; Song et al., 2009; Achard, Salvador, Whitcher, Suckling, & Bullmore, 2006) often fail to adequately reflect representative topological statistics of the networks used to construct them (S”
Network attributes for segregation and integration in the human brain. (2013, background): “A comparison of different measures for characterizing the functional centrality of brain regions demonstrated that each of them tends to highlight different aspects of local and global information flow, with significant differences in the ranking of regions across different measures [50].”
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule (2013, neutral): “In the cortex, a rich club subgraph has been shown by imaging studies to form an extensively connected network core, potentially playing an important role in integrating information (van den Heuvel and Sporns, 2011; Zamora-López et al., 2010; Zuo et al., 2012).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “, 2013a); and 5) network degree centrality (Buckner et al., 2009; Zuo et al., 2012).”
Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain (2013, result): “During rest, we found that functional hubs were located primarily in the DMN and visual areas, which was approximately in accordance with previous functional (7, 8, 24) and structural network studies (5, 6).”
Network hubs in the human brain. (2013, methodology): “Given the wide range of recording empirical approaches and analysis methods, the iden-tiﬁcation of functional hubs depends on the methodology used for estimating network edges as well as the graph metrics used to express ‘functional centrality’ [78].”