Complex network measures of brain connectivity: Uses and interpretations

By Mikail Rubinov, Olaf Sporns. 2010

11,142 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Nine quick tips for analyzing network data (2019, methodology): “, paths in functional networks may not actually correspond to information flow [20]; paths in contact networks may not be relevant when information or disease diffusion is not studied [24]).”
Networks of the Brain: Quantitative Analysis and Modeling (2010, neutral): “More detailed and formal treatments can be found elsewhere (Reijneveld et al., 2007; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010).”
Mapping neurotransmitter systems to the structural and functional organization of the human neocortex (2021, methodology): “Path length was computed using the Python package bctpy and is defined as the shortest contiguous set of edges between two brain regions, which represents a form of routing communication [97].”
Structure and function of complex brain networks (2013, background): “Based on the insights they deliver, they can be classified into measures reporting on aspects of segregation, integration, and influence.(13) Segregation (or specialization) refers to the degree to which a network’s elements form separate cliques or clusters.”
Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis (2010, methodology): “The graph organizational measures were computed as follows (Brain Connectivity Toolbox, http://www.brain-connectivity-toolbox.net) (Rubinov and Sporns, 2010).”
Abnormal intrinsic brain functional network dynamics in Parkinson’s disease (2017, background): “…applied a graph theory approach to examine variability of topological organization of the functional connectivity network, wherein we defined 44 independent components as nodes and the connectivity between each independent component as edges (Rubinov and Sporns, 2010) during resting state scan.”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Generalizations of existing formulae for graph theoretic measures that can account for signed weights, and which are applicable to unthresholded graphs (Rubinov and Sporns, 2010), will be an important goal for the near future.”
Scanning the horizon: towards transparent and reproducible neuroimaging research (2016, neutral): “g.(53)) avoid the multiple testing problem.”
A Brain-Wide Study of Age-Related Changes in Functional Connectivity. (2015, methodology): “Network measures were calculated using functions implemented in the Brain Connectivity Toolbox (Rubinov and Sporns 2010, www. brain-connectivity-toolbox.net).”
A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes (2018, methodology): “To reduce the high dimensionality of FC patterns, a number of methods have been proposed, ranging from graph theory-based features (Bullmore and Sporns, 2009; Rubinov and Sporns, 2010) to recent deep learning approaches (Kuang et al., 2014; Kim et al., 2016; Guo et al., 2017).”