Modular Brain Networks

By Olaf Sporns, Richard F. Betzel. 2015

1,278 citations recorded by the source corpus.

The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Continual Learning via Local Module Composition (2021, background): “the number of tasks; (iii) systematic [8] and out-of-distribution (OOD) generalization [18] through knowledge recombination; and (iv) biological plausibility [91, 90, 96].”
Modular Networks: Learning to Decompose Neural Computation (2018, background): “Furthermore, the modular structure of biological neural connections [22] is hypothesized to optimize energy cost [7], improve adaption to changing environments and mitigate catastrophic forgetting [21].”
The Human Thalamus Is an Integrative Hub for Functional Brain Networks (2016, background): “…nucleus VL. Thalamic lesions have Global and Distal Effects on Cortical Network Organization Modularity is a metric that quantifies the extent to which the brain is differentiated into separable sub-networks, and is an essential property found in many complex systems (Sporns and Betzel, 2016).”
Network-Level Structure-Function Relationships in Human Neocortex (2016, background): “The decompositions of both permuted and bootstrap-resampled datamatrices are not guaranteed to produce a set of LVs comparablewith the ones derived from the original datamatrix, because both types of resampling could induce arbitrary axis rotation (a change in the order of LVs) or axis reflection…”
Cliques and cavities in the human connectome (2016, background): “their members, facilitating local integration of information (Sporns and Betzel 2016).”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Various studies have demonstrated that networks based on modular structure generally reﬂect the properties of small-world networks (Bullmore and Sporns, 2009; Fortunato, 2010; He and Evans, 2010; Meunier et al., 2010; Sporns and Betzel, 2016).”
Mapping neurotransmitter systems to the structural and functional organization of the human neocortex (2021, background): “These wiring patterns display non-trivial architectural features, including specialized network modules that support the segregation of information 2 as well as densely interconnected hub regions that support the integration of information 3 .”
Network neuroscience (2017, methodology): “Important issues currently under development involve improved and domain-appropriate approaches for module (community) detection 56 , model-based inference of networks from observational data 57 , and statistically principled methods for network comparison across different individuals and in…”
Small-World Brain Networks Revisited (2016, methodology): “(And the WS model does not seem like a biologically plausible generative model for brain networks [10–12].)”
Graph theory methods: applications in brain networks (2018, background): “organization into distinct network communities or modules 6,10,23 ( Figure 1 ).”
Statistical physics of vaccination (2016, background): “Along the above definition, may real-world social and engineering systems, such as online social networks [232, 359–365], airport traffic networks [358, 366, 367], biological metabolic networks [368, 369], and scientific collaboration and citation networks [370], can be put into the framework of mul”
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance (2015, positive): “This was also reflected in graph-theoretic measures of network-wide integration: temporal windows associated with segregated states had significantly elevated modularity (QS = 0.55 ± 0.1 versus QI = 0.42 ± 0.2; Cohen’s d = 0.9; p = 10 (11); Sporns and Betzel, 2016), whereas those associated with the”
A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “Similarly, community detection in brain networks [20], [30] reﬂects the functional and anatomical segregation of brain regions.”
Communication dynamics in complex brain networks (2017, background): “On the one hand, nodes with many connections are well positioned to facilitate integration of information both within and between specialized modules 29,40,49,86,99,100 .”