Modular and Hierarchically Modular Organization of Brain Networks

By David Meunier, Renaud Lambiotte, E. Bullmore. 2010

1,197 citations recorded by the source corpus.

Brain networks are increasingly understood as one of a large class of information processing systems that share important organizational principles in common, including the property of a modular community structure. A module is topologically defined as a subset of highly inter-connected nodes which are relatively sparsely connected to nodes in other modules. In brain networks, topological modules are often made up of anatomically neighboring and/or functionally related cortical regions, and inter-modular connections tend to be relatively long distance. Moreover, brain networks and many other complex systems demonstrate the property of hierarchical modularity, or modularity on several topological scales: within each module there will be a set of sub-modules, and within each sub-module a set of sub-sub-modules, etc. There are several general advantages to modular and hierarchically modular network organization, including greater robustness, adaptivity, and evolvability of network function. In this context, we review some of the mathematical concepts available for quantitative analysis of (hierarchical) modularity in brain networks and we summarize some of the recent work investigating modularity of structural and functional brain networks derived from analysis of human neuroimaging data.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Embracing Change: Continual Learning in Deep Neural Networks. (2020, neutral): “Evidence of modularity extends beyond anatomic features to functional separation in terms of sparse activations and hierarchical organization [6].”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “Moreover, correlations are sensitive to the shape of the hemodynamic response function (HRF), which causes variations across diﬀerent individuals and diﬀerent brain areas (Miezin et al., 2000; Lee et al., 2001).”
Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing (2014, background): “Small-world characteristics are also observed in anatomical networks, ranging from the simplest animal nervous system (C. Elegans), to mammalian cortex, which has a consistently modular and hierarchical organization throughout [69].”
Weight-conserving characterization of complex functional brain networks (2011, background): “Simple functional networks form highly connected modules – components or subnetworks – within complex functional networks (Meunier et al., 2010).”
KAN: Kolmogorov-Arnold Networks (2024, positive): “When a new task is learned, structure re-organization only occurs in local regions responsible for relevant skills [25, 26], leaving other regions intact.”
Focal Brain Lesions to Critical Locations Cause Widespread Disruption of the Modular Organization of the Brain (2012, neutral): “Modularity quantifies the ability of the brain to differentiate into separable subnetworks and is an essential property found in many complex systems that allows the system to easily evolve, develop, and engage in flexible, dynamic behaviors (Meunier et al., 2010).”
Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, methodology): “This function was maximized by a computationally expedient greedy algorithm (Meunier et al., 2010).”
Changes in structural and functional connectivity among resting-state networks across the human lifespan (2014, background): “…2006; Gong et al., 2009), hubs and cores (Achard et al., 2006; Hagmann et al., 2008; Zuo et al., 2012), a structural rich club (van den Heuvel and Sporns, 2011), modular architecture (Meunier et al., 2009, 2010), and economicwiring (Bassett et al., 2010; Bullmore and Sporns, 2012), among others.”
On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks (2013, background): “Despite the large amount of work in this direction, striking differences still separate most artificiallyevolved networks from biological ones: biological nervous systems are much larger [5], much more organized [6], much more plastic [7] and, overall, much more complex [8].”
The Hierarchical Organisation of Cortical and Basal-Ganglia Systems: A Computationally-Informed Review and Integrated Hypothesis (2013, positive): “top ones exert control over the lower ones but at the same time are influenced by them in their functioning (Meunier et al., 2010).”
Simple models of human brain functional networks (2012, background): “For example, human brain networks have the smallworld property of high clustering and high efficiency (or short path length) (4); they are also modular (5) and comprise a number of highly connected hub nodes in a fat-tailed degree distribution (6).”
Communication dynamics in complex brain networks (2017, background): “A high within-module connection density promotes high within-module communication efficiency 87 ; sparser connections between modules enable efficient inter-module integration of information 28,88,89 .”
Growing Homophilic Networks Are Natural Navigable Small Worlds (2015, positive): “Studies have shown that the functional brain networks form a hierarchically modular community structure[53, 57] consisting of highly interconnected specialized modules, only loosely connected to each other.”