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 () © 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.


Achard S, Salvador R, Whitcher B, Suckling J and Bullmore E (2006) A resilient, low‐frequency, small‐world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience 26 (1): 63–72.

Achard S et al. (2012) Hubs of brain functional networks are radically reorganized in comatose patients. Proceedings of the National Academy of Sciences of the United States of America 109 (50): 20608–20613.

Alvarado‐Rojas C et al. (2013) Single‐unit activities during epileptic discharges in the human hippocampal formation. Frontiers in Computational Neuroscience 7: 140.

Bassett D and Bullmore E (2006) Small‐world brain networks. The Neuroscientist 12 (6): 512–523.

Bassett DS and Bullmore ET (2009) Human brain networks in health and disease. Current Opinion in Neurology 22 (4): 340.

Bassett DS, Meyer‐Lindenberg A, Achard S, Duke T and Bullmore E (2006) Adaptive reconfiguration of fractal small‐world human brain functional networks. Proceedings of the National Academy of Sciences of the United States of America 103 (51): 19518–19523.

Bassett DS et al. (2011a) Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America 108 (18): 7641–7646.

Bassett DS, Brown JA, Deshpande V, Carlson JM and Grafton ST (2011b) Conserved and variable architecture of human white matter connectivity. NeuroImage 54 (2): 1262–1279.

Bassett DS, Nelson BG, Mueller BA, Camchong J and Lim KO (2012) Altered resting state complexity in schizophrenia. NeuroImage 59 (3): 2196–2207.

Bialonski S and Lehnertz K (2013) Assortative mixing in functional brain networks during epileptic seizures. Chaos 23 (3): 033139.

Buckner RL et al. (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. Journal of Neuroscience 29 (6): 1860–1873.

Bullmore E and Sporns O (2012) The economy of brain network organization. Nature Review Neuroscience 13 (5): 336–349.

Cole MW, Bassett DS, Power JD, Braver TS and Petersen SE (2014) Intrinsic and task‐evoked network architectures of the human brain. Neuron 83 (1): 238–251.

van Dellen E et al. (2014) Epilepsy surgery outcome and functional network alterations in longitudinal MEG: a minimum spanning tree analysis. NeuroImage 86: 354–363.

Fedorenko E and Thompson‐Schill SL (2014) Reworking the language network. Trends in Cognitive Sciences 18 (3): 120–126.

Filippi M et al. (2013) Assessment of system dysfunction in the brain through MRI‐based connectomics. The Lancet Neurology 12 (12): 1189–1199.

Fornito A and Bullmore ET (2014) Connectomics: A new paradigm for understanding brain disease. European Neuropsychopharmacology.

Fornito A, Zalesky A, Pantelis C and Bullmore ET (2012) Schizophrenia, neuroimaging and connectomics. NeuroImage 62: 2296–2314.

Gupta D, Ossenblok P and van Luijtelaar G (2011) Space‐time network connectivity and cortical activations preceding spike wave discharges in human absence epilepsy: a MEG study. Medical & Biological Engineering & Computing 49 (5): 555–565.

Hagmann P et al. (2008) Mapping the structural core of human cerebral cortex. PLoS Biology 6 (7): e159.

He Y, Chen Z and Evans A (2008) Structural insights into aberrant topological patterns of large‐scale cortical networks in Alzheimer's disease. Journal of Neuroscience 28 (18): 4756–4766.

Hermundstad AM et al. (2014) Structurally‐constrained relationships between cognitive states in the human brain. PLoS Computational Biology 10 (5): e1003591.

van den Heuvel MP, Stam CJ, Kahn RS and Hulshoff Pol HE (2009) Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience 29 (23): 7619–7624.

van den Heuvel MP, Kahn RS, Goni J and Sporns O (2012) High‐cost, high‐capacity backbone for global brain communication. Proceedings of the National Academy of Sciences of the United States of America 109 (28): 11372–11377.

van den Heuvel MP et al. (2013) Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70 (8): 783–792.

Humphries MD, Gurney K and Prescott TJ (2006) The brainstem reticular formation is a small‐world, not scale‐free, network. Proceedings of the Biological Sciences/The Royal Society 273 (1585): 503–511.

Kaiser M and Varier S (2011) Evolution and development of brain networks: from Caenorhabditis elegans to Homo sapiens. Network 22 (1–4): 143–147.

Kramer MA and Cash SS (2012) Epilepsy as a disorder of cortical network organization. The Neuroscientist 18 (4): 360–372.

Kramer MA et al. (2010) Coalescence and fragmentation of cortical networks during focal seizures. Journal of Neuroscience 30 (30): 10076–10085.

Lo CY, Wang PN, Chou KH, et al. (2010) Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease. Journal of Neuroscience 30 (50): 16876–16885.

Lynall ME et al. (2010) Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience 30 (28): 9477–9487.

Maldjian JA, Davenport EM and Whitlow CT (2014) Graph theoretical analysis of resting‐state MEG data: Identifying interhemispheric connectivity and the default mode. NeuroImage 96: 88–94.

Power JD et al. (2011) Functional network organization of the human brain. Neuron 72 (4): 665–678.

Raj A, Kuceyeski A and Weiner M (2012) A network diffusion model of disease progression in dementia. Neuron 73 (6): 1204–1215.

Reid AT and Evans AC (2013) Structural networks in Alzheimer's disease. European Neuropsychopharmacology 23 (1): 63–77.

Rubinov M and Bullmore E (2013) Schizophrenia and abnormal brain network hubs. Dialogues in Clinical Neuroscience 15 (3): 339–349.

Rubinov M and Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52 (3): 1059–1069.

Sanz‐Arigita EJ et al. (2010) Loss of ‘small‐world’ networks in Alzheimer's disease: graph analysis of FMRI resting‐state functional connectivity. PLoS One 5 (11): e13788.

Schindler KA, Bialonski S, Horstmann M‐T, Elger CE and Lehnertz K (2008) Evolving functional network properties and synchronizability during human epileptic seizures. Chaos 18 (3): 033119.

Sporns O (2013) Network attributes for segregation and integration in the human brain. Current Opinion in Neurobiology 23 (2): 162–171.

Stead M et al. (2010) Microseizures and the spatiotemporal scales of human partial epilepsy. Brain 133 (9): 2789–2797.

Stephan KE, Friston KJ and Frith CD (2009) Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self‐monitoring. Schizophrenia Bulletin 35 (3): 509–527.

Supekar K, Menon V, Rubin D, Musen M and Greicius MD (2008) Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Computational Biology 4 (6): e1000100.

Tijms BM et al. (2013) Alzheimer's disease: connecting findings from graph theoretical studies of brain networks. Neurobiology of Aging 34 (8): 2023–2036.

Uhlhaas PJ and Singer W (2010) Abnormal neural oscillations and synchrony in schizophrenia. Nature Review Neuroscience 11 (2): 100–113.

Varotto G, Tassi L, Franceschetti S, Spreafico R and Panzica F (2012) Epileptogenic networks of type II focal cortical dysplasia: a stereo‐EEG study. NeuroImage 61 (3): 591–598.

Vourkas M et al. (2014) Simple and difficult mathematics in children: a minimum spanning tree EEG network analysis. Neuroscience Letters 576: 28–33.

Watts DJ and Strogatz SH (1998) Collective dynamics of ‘small‐world’ networks. Nature 393 (6684): 440–442.

Zhang Z et al. (2011) Altered functional‐structural coupling of large‐scale brain networks in idiopathic generalized epilepsy. Brain 134 (Pt 10): 2912–2928.

Zhang J et al. (2012) Pattern classification of large‐scale functional brain networks: identification of informative neuroimaging markers for epilepsy. PLoS One 7 (5): e36733.

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.)

Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite close
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]