Structure and function of complex brain networks
By Olaf Sporns. Dialogues in Clinical Neuroscience, 2013
876 citations recorded by the source corpus.
An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/251a39598dd4797b142684998901c64a69baa550
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In Cellular and Molecular Neuroscience
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Citations in the corpus
- Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “The human brain is one of the most complex networks in the world, and studies on its static and dynamic properties have undergone explosive growth in recent years (Bullmore and Sporns, 2012; Sporns, 2013b; Kriegeskorte and Douglas, 2018).”
- Metric learning with spectral graph convolutions on brain connectivity networks (2018, background): “The obtained functional connectivity networks incorporate the strength of these connections in their edge labels (Sporns, 2013), yielding a so-called labelled graph representation.”
- Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “Moreover, the inherent complexity and diversity in the formation of a human brain [53] that influence the functional neural networks [54], construct highly volatile neuronal connectivity over time and across subjects [55].”
- Modern temporal network theory: a colloquium (2015, background): “In neuroscience, networks have become a useful tool to understand the organization function of the brain and how different types of conditions alter the coupling between different regions [217,269,15].”