Review Article | Published:

Modern network science of neurological disorders

Nature Reviews Neuroscience volume 15, pages 683695 (2014) | Download Citation

Abstract

Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.

Key points

  • Healthy structural and functional brain networks are characterized by a cost-effective architecture, which has an optimal balance between local and global connectivity, and a hierarchical modular structure.

  • Normal brain-network organization arises during development under genetic control and is correlated with cognitive function.

  • Local brain lesions give rise to widespread changes to networks, whereas global brain disorders preferentially affect highly connected hub regions.

  • In many neurological disorders, the most consistent changes concern a breakdown of the hierarchical modular structure and, in particular, a loss of highly connected hub areas.

  • The pattern of network changes in neurological disorders may be explained by a hypothetical scenario of 'hub overload and failure'.

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Acknowledgements

The author thanks his colleagues, who participated in many of the studies described here and contributed to the ideas expressed in this Review.

Author information

Affiliations

  1. Department of Neurology and Clinical Neurophysiology, MEG Center, VU University Medical Center, De Boelelaan 1118, 1081HV Amsterdam, The Netherlands.

    • Cornelis J. Stam

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C.J.S. is an unpaid advisor for Danone research.

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Correspondence to Cornelis J. Stam.

Glossary

Small-world networks

Networks characterized by a combination of high clustering (which represents local connectedness) and short path lengths (that is, short distances between any two nodes).

Scale-free networks

Networks in which the probability that a randomly chosen node has degree (number of connections) k is inversely proportional to k.

Hierarchical modularity

A type of network organization where each component (for instance, a module or cluster) is composed of smaller components but at the same time is part of a larger component.

Connectedness

A measure of the existence of connections (structural or functional) between network elements.

Degree distributions

The probability distribution (P(k)) of degrees over a network. P(k) is the probability P that a randomly chosen node has degree k.

Centrality

A measure of the relative importance of a node in a network. Various centrality measures exist (including degree, betweenness and eigenvector).

Multiconstraint optimization

Optimal network organization that takes into account multiple, often conflicting, constraints (for instance, wiring cost and path length).

Minimum spanning tree

An acyclic connected subnetwork that minimizes the cost function that is associated with edges.

Synchronizability

A property of a network that indicates whether a dynamical process on this network will reach a stable synchronized state.

Neuromyelitis optica

A demyelinating disorder that affects optic nerves.

Ictal state

Brain state during an epileptic seizure.

Cryptogenic localization-related epilepsy

Focal epilepsy that is putatively due to a local structural abnormality which cannot yet be demonstrated.

Absence epilepsy

A form of generalized epilepsy that is characterized by 3 Hz spike–wave discharges in the electroencephalogram.

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https://doi.org/10.1038/nrn3801

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