On Human Brain Networks in Health and Disease


The brain is a complex system whose function relies on a diverse set of connections or interactions between brain regions. Using the mathematical framework of complex networks, these interaction patterns can be parsimoniously represented as brain graphs: each brain area is represented as a network node and each connection is represented as a network edge. These methods have been used to demonstrate that human brain networks display properties such as a small‐world architecture that may directly facilitate cognitive processes. Moreover, mounting evidence suggests that these properties are altered in disease states, potentially providing important biomarkers for psychiatric and neurological disorders and informing our understanding of the mechanisms of altered cognitive function. Here, the basic concepts in network science are reviewed, and the properties of healthy and diseased brain networks discussed. Relationships between network diagnostics and alterations in behavioural or cognitive variables associated with Alzheimer's disease, schizophrenia and epilepsy are highlighted.

Key Concepts

  • The brain is a complex system that can be represented by a graph.
  • In a graph, nodes represent brain regions and edges represent the links or connections between those areas, forming a complex network.
  • Brain networks display properties such as a small‐world architecture or a hierarchical modular organisation that may directly facilitate cognitive processes.
  • These properties are altered in disease states, potentially providing important biomarkers for psychiatric and neurological disorders.

Keywords: brain network; graph theory; Alzheimer's disease; schizophrenia; epilepsy

Figure 1. Workflow of Human Brain Network Construction: Human brain networks can be constructed from either structural data (grey matter or white matter networks) or functional data, the latter including different modalities such as functional MRI or EEG and MEG. After acquisition, the data is first parcellated into different brain regions that will form the nodes of the network. For EEG and MEG data, this step is often not necessary, as the respective EEG/MEG sensors can be used as nodes of the network. Then, the pairwise association between those nodes is computed to construct the edges of the network. Some researchers choose to perform an additional step to threshold and binarise networks; however, weighted (unthresholded) network analysis is becoming more common in recent years. Reproduced with permission from Danielle S Bassett and Edward T Bullmore (2009) © Wolter Kluwer Health.
Figure 2. Networks and Network Diagnostics: An adjacency matrix (a) and its associated toy binary network (b). Nodes are represented as enumerated circles; edges are depicted as lines. Node number 17 has a degree of 3, as it is connected to 3 other nodes in the network (green lines). The blue lines indicate the shortest path connecting node 7 to node 10, which traverses 5 edges leading to a path length of 5. In red is shown the edges that form triangles. The clustering coefficient is an indicator of how many of a node's nearest neighbours are connected to each other, therefore forming triangles. Here, for example node 11 has a clustering coefficient of 0.33, indicating that 2 of its neighbours have a connection (1 out of 3 possible connections). The yellow area marks a module: nodes 1, 2, 4 and 5 are densely interconnected, but have few connections to other nodes. Reproduced with permission from Danielle S Bassett and Edward T Bullmore (2009) © Wolter Kluwer Health.
Figure 3. Epileptic Functional Networks: ECoG sensors placed on the surface of the brain record local brain dynamics. Functional networks are then created over different periods of time to study the evolution of the epileptic network.


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Further Reading

Albert R and Barabási A‐L (2002) Statistical mechanics of complex networks. Reviews of Modern Physics 74 (1): 47. (This extensive review article offers a detailed introduction to both practical and theoretical concepts in the study of complex networks.)

Boccaletti S, Latora V, Moreno Y, Chavez M and Hwang D‐U (2006) Complex networks: structure and dynamics. Physics Reports 424 (4): 175–308. (A detailed review of concepts in network theory that contains sections specifically devoted to synchronization and applications to brain networks.)

Bullmore E and Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Review Neuroscience 10 (3): 186–198. (This review article by Bullmore and Sporns provides an excellent general introduction to basic concepts in graph analysis as applied to neuroscientific questions.)

Holme P and Saramäki J (2012) Temporal networks. Physics Reports 519 (3): 97–125. (This review article gives a comprehensive overview of key concepts and ideas in the emerging field of temporal networks.)

Newman M (2010) Networks: an introduction. Oxford, UK: Oxford University Press. (This highly acclaimed book by Mark Newman is a brilliant introduction to the field of network science.)

Rubinov M and Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52 (3): 1059–1069. (This review article gives a formal mathematical description and tentative biological interpretations of the most commonly applied graph diagnostics in neuroscience.)

Sporns O (2011) Networks of the Brain. Cambridge, MA: MIT press. (This book by Olaf Sporns provides an extended introduction to and summary of the field of network neuroscience. Of immediate use to practicing neuroscientists, it is also highly accessible to undergraduate readers.)

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Braun, Urs, Muldoon, Sarah F, and Bassett, Danielle S(Feb 2015) On Human Brain Networks in Health and Disease. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0025783]