Weight-conserving characterization of complex functional brain networks

By Mikail Rubinov, Olaf Sporns. NeuroImage, 2011

893 citations recorded by the source corpus.

Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/07e75f181b9837871ec4b1a48458a9260e17bf2f

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Citations in the corpus

Global connectivity of prefrontal cortex predicts cognitive control and intelligence (2012, methodology): “In the current study we used a recently developed graph theoretic approach to neuroimage data, sometimes referred to as global brain connectivity (GBC) or weighted degree centrality (Cole et al., 2010b, 2011; Rubinov and Sporns, 2011).”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, neutral): “…beyond the prototypical pipeline exposed above, some studies employ complex-graph network modeling approaches – e.g. network modularity or centrality (Rubinov and Sporns, 2011) – (Wolfers et al., 2015; Arbabshirani et al., 2017; Brown and Hamarneh, 2016) These approaches are seldom combined with…”
Functional network organization of the human brain (2011, background): “Recent proposals to incorporate negative weights into analyses of subgraph detection have been made (Rubinov and Sporns, 2011; Traag and Bruggeman, 2009), but here we follow the traditional approach.”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, positive): “Recent progress has been made on several fronts, particularly as related to dealingwith themultiple comparison problem; developing multivariate classifiers with potential clinical relevance; computational modeling of pathophysiological mechanisms causing connectomic disturbances in different brain d”
Network attributes for segregation and integration in the human brain. (2013, methodology): “A measure of weighted degree centrality [53] was used to characterize the resting-state level of global connectivity of brain regions including several that were previously identified as ‘cognitive control regions’ in MRI activation data [51 ].”
Modular Brain Networks (2015, methodology): “…“best” partition but as a meaningful average across multiple near-optimal partitions, and by treating nodes’ community affiliations as fuzzy variables (Bellec et al. 2010, Rubinov & Sporns 2011) or characterizing them by probabilistic clustering (Hinne et al. 2014), rather than as hard assignments.”
The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition (2016, methodology): “We used the Louvain community detection algorithm with weighted edges ( positive only) to produce an estimate of the optimal partitioning of nodes into networks (Rubinov and Sporns, 2011).”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “Functional network communities (RSNs) were derived using a modularity maximization procedure (Newman and Girvan 2004; Rubinov and Sporns 2011; Sporns and Betzel 2016).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “Jones et al. (2012) studied impairments in the dynamics of spontaneous activity by examining time varying changes of a modularity metric (Q, see Rubinov and Sporns, 2011) applied to graphical representations of functional connectivity using a sliding-window approach.”
Schizophrenia, neuroimaging and connectomics (2012, methodology): “An analysis of possible degeneracies in the obtained decomposition is therefore important to understand its stability (Rubinov and Sporns, 2011).”
Decreased segregation of brain systems across the healthy adult lifespan (2014, methodology): “execution of task set, allocation of attention, controlled mnemonic retrieval, and executive control, likely are performed by multiple brain systems and require substantial interaction between areas in these systems (39, 51, 52).”