Graph theory methods: applications in brain networks
By Olaf Sporns. Dialogues in Clinical Neuroscience, 2018
573 citations recorded by the source corpus.
Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, from molecular to behavioral scales, are ever increasing in size and complexity. These developments lead to a strong demand for appropriate tools and methods that model and analyze brain network data, such as those provided by graph theory. This brief review surveys some of the most commonly used and neurobiologically insightful graph measures and techniques. Among these, the detection of network communities or modules, and the identification of central network elements that facilitate communication and signal transfer, are particularly salient. A number of emerging trends are the growing use of generative models, dynamic (time-varying) and multilayer networks, as well as the application of algebraic topology. Overall, graph theory methods are centrally important to understanding the architecture, development, and evolution of brain networks.
Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/73dd81990333e603469fbab07958fbb7e47d302f
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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 advances in graph theory and network neuroscience (i.e., the study of the structure or function of the nervous system) offer an opportunity to understand the details of this complex phenomenon and its modeling (Vecchio et al., 2017; Sporns, 2018).”
- Distributed theta networks support the control of working memory: Evidence from scalp and intracranial EEG (2025, unknown): “According to graph theory, brain networks are collections of nodes (here, Laplacian-transformed channels) and inter-node edges (PLV), summarized as adjacency matrices (Sporns, 2018).”
- Resting-state EEG connectome topology and social norm processing in healthy young adults (2026, unknown): “Within graph theory, the brain is modelled as nodes connected by weighted or binary edges, in which the nodes represent the brain regions and the edges the level of synchronisation between them (Sporns et al. 2005; Sporns 2018 ).”
- Higher integration of the whole-brain resting state theta band network is associated with spatial working memory (2026, unknown): “The participation coefficient reflects the diversity of a brain region’s connections and serves as an index of global integration (Sporns, 2018).”