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. 2021 Sep 28;47(1):20–40. doi: 10.1038/s41386-021-01156-6

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — Brain regions or nodes are defined based on anatomical, functional, or multimodal parcellations, thus subdividing the whole brain into p regional nodes (1). Connectivity between nodes can be estimated in many different ways, to define the weight of an edge or connection between each possible pair of nodes. Anatomical connectivity (2, left) can be estimated by dMRI tractography, structural covariance, or morphometric similarity; functional connectivity (2, right) can be estimated by the correlation between nodal mean time series. The resulting (p × p) matrix is the connectome, which can be represented in diverse formats, including (left) an anatomical rendering, where the nodes are located at the centroid of each region and a line is drawn between nodes if their pairwise connectivity exceeds an arbitrary threshold; or (right) a ring diagram, where the regional nodes are arranged around the perimeter of the circle (color-coded according to anatomical criteria or modular affiliation) and edges traverse the interior of the circle denoting suprathreshold connectivity. Finally, the complex topological properties of these networks, including hubs and modules, can be estimated using tools from graph theory, here illustrated for the simplest class of binary undirected graphs (from [236]).