Graph theoretical modeling of brain connectivity.

By Yong He, Alan C. Evans. 2010

702 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Olaf Sporns
2. Yong He
3. E. Bullmore
4. Alan C. Evans
5. Jinhui Wang
6. Xi-Nian Zuo
7. D. Bassett
8. Andrew Zalesky

In Experimental and Cognitive Psychology

1. Yong He
2. Jinhui Wang
3. E. Bullmore
4. Xi-Nian Zuo
5. Alex Fornito
6. Alan C. Evans
7. Andrew Zalesky
8. Gaolang Gong

In Molecular Biology

1. Andrew Zalesky
2. Alex Fornito
3. E. Bullmore
4. Yong He
5. Jinhui Wang
6. Adam Gaier
7. David R Ha
8. Xi-Nian Zuo

Citations in the corpus

• Weight Agnostic Neural Networks (2019, methodology): “The connectome can be viewed as a graph [12, 42, 110], and analyzed using rich tools from graph theory, network science and computer simulation.”
• Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “Several recent reviews (Bassett and Bullmore, 2006; Reijneveld et al., 2007; Bullmore and Sporns, 2009; He and Evans, 2010; Rubinov and Sporns, 2010; Wang et al., 2010; Bullmore and Bassett, 2010) have focused on the data analytic methods that can be used to extract complex networks from structural…”
• Clustering and Community Detection in Directed Networks: A Survey (2013, background): “In recent years, there has been several studies concerning graph theoretical analysis of human brain networks for a wide range of mapping techniques, such as MRI, fMRI and EEG/MEG (see the following review articles in the area [136, 137]).”
• Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain (2013, methodology): “Post hoc paired t tests further revealed that rCBF/FCS significantly increased in the left IPL/SPL and MFC, and decreased in mPFC/ACC and PCC/PCu as the task difficulty increased (Fig.”
• Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, background): “These brain networks have consistently demonstrated many non-trivial topological properties, such as small-worldness, modularity and highly connected hubs (for reviews, see [3,4,5,6,7]), and exhibited distinct alterations associated with different neurocognitive disorders (for reviews, see [8,9]).”
• GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, background): “The results are in line with previous graph analysis studies that have consistently shown a small-world architecture in structural brain networks in healthy individuals [12,19,47].”
• Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, background): “2015b), graph theoretic analyses (Salvador et al. 2005; Supekar et al. 2008; Bullmore and Sporns 2009; Meunier et al. 2009; Guye et al. 2010; He and Evans 2010; Wang et al. 2010; Zalesky et al. 2010a; Fornito et al. 2013) or neural mass modeling (Ghosh et al.”
• Graph-Based Network Analysis of Resting-State Functional MRI (2010, positive): “Structural associations can be measured by examining either direct diffusion-based anatomical connectivity or indirect morphology-based statistical interdepend-encies across populations (Bullmore and Sporns, 2009; He and Evans, 2010).”
• Brain Connectivity (2011, background): “Particularly in the past 5 years, graph theory has attracted considerable attention in the neuroimaging community and is being translated to investigate brain networks (He and Evans 2010).”
• Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex (2012, methodology): “The same procedure has been applied to the nodal local efficiency but no significant correlation (R=−0.11, p=0.32) was found between the two networks.”
• Annual Research Review: Enduring neurobiological effects of childhood abuse and neglect (2016, neutral): “One of the earliest and most consistent finding in maltreated children (e.g., (De Bellis, et al., 1999b; De Bellis, et al., 2002; Teicher, et al., 2004)) and adults (e.g., (Andersen, et al., 2008; Teicher, et al., 2010)) is reduced area or integrity of the corpus callosum.”
• On the use of correlation as a measure of network connectivity (2012, methodology): “Contents lists available at SciVerse ScienceDirect NeuroImage j ourna l homepage: www.e lsev ie r .com/ locate /yn img On the use of correlation as a measure of network connectivity Andrew Zalesky a,⁎, Alex Fornito a,b,c, Ed Bullmore d,e a Melbourne Neuropsychiatry Centre, The University of Melbourn”
• Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Graph-based network analysis reveals meaningful information about the topological architecture of human brain networks, such as small-worldness, modular organization, and highly connected or centralized hubs (Bullmore and Sporns, 2009, 2012; He and Evans, 2010; Meunier et al., 2010; Bullmore and Bas”
• Process Overlap Theory: A Unified Account of the General Factor of Intelligence (2016, background): “…emerging paradigm in cognitive neuroscience, based on the recognition that neural computations involved in cognition are not performed by isolated brain areas but rather are the result of networks of interconnected areas (Bressler & Menon, 2010; He & Evans, 2010; Heuvel & Pol, 2010; Sporns, 2002).”
• Diaschisis: past, present, future. (2014, background): “The functional coupling between brain regions can be determined with the study of temporal correlations between slow fluctuations of the neurovascular signal (functional MRI) or EEG and MEG signals (Deuker et al., 2009; He and Evans, 2010; Power et al., 2011).”