On the use of correlation as a measure of network connectivity

By Andrew Zalesky, Alex Fornito, E. Bullmore. NeuroImage, 2012

448 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/eb4fc973bab4ffbfc571da6b7cce85e5696c85ff

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

In Statistical and Nonlinear Physics

Citations in the corpus

Time-resolved resting-state brain networks (2014, methodology): “A criticism of full correlation is its sensitivity to indirect functional relations between pairs of regions that are mediated by a third region (38).”
Intrinsic and task-evoked network architectures of the human brain. (2014, unknown): “FC was estimated as temporal correlations (Zalesky et al., 2012) among a set of 264 putative functional regions throughout the brain (defined independently to reduce potential statistical biases) (Power et al., 2011).”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, methodology): “It should be noted that how to generate random networks is an ongoing topic for brain network studies Zalesky et al. (2012); Hosseini and Kesler (2013).”
Cognitive Network Neuroscience (2015, background): “, 2011;David, Cosmelli, & Friston, 2004), a common choice in fMRI networks is the Pearson correlation (Zalesky et al., 2012), although other measures of signal relatedness can be used, such as covariance, mutual information, and coherence.”
Network-Level Structure-Function Relationships in Human Neocortex (2016, background): “definite and are unlikely to show properties that are inherent to networks mapped with pairwise correlations, such as increased transitivity (Zalesky et al. 2012), closed triangle motifs (Sporns & Kotter 2004), and local clustering (Sporns&Zwi2004).”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, methodology): “However, recent evidence suggests that correlation networks are inherently more clustered and partial correlation networks are inherently less clustered than random networks of the same size and degree [53].”
Contributions and challenges for network models in cognitive neuroscience (2014, background): “In functional magnetic resonance imaging (fMRI), crosscorrelation remains a useful measure expressing the similarity of time courses, despite a known propensity to link struc turally unconnected nodes 20 .”
Multi-scale brain networks (2016, unknown): “Usually this statistical relationship is operationalized as a Fisher-transformed correlation coefficient (Zalesky et al., 2012) or a coherence measure (Zhang et al.”
Cognitive relevance of the community structure of the human brain functional coactivation network (2013, methodology): “Measures on the coactivation network were compared with the same measures estimated on random graphs matched for number of nodes, connection density, degree and weight distributions (47), and on graphs constructed from random permutations of the BrainMap database (48) (SI Materials and Methods and F”