Small-world human brain networks: Perspectives and challenges

• Published in: Neuroscience and biobehavioral reviews Vol. 77; pp. 286 - 300
• Main Authors: Liao, Xuhong, Vasilakos, Athanasios V., He, Yong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-06-2017
• Subjects: Aging; Brain; Brain modelling; Connectome; Distribuerade datorsystem; Graph theory; Humans; Modularity; Neuroimaging; Pervasive Mobile Computing; Selection, Genetic; Small-world; Brain modelling; Small-world; Graph theory; Connectome; Modularity
• ISSN: 0149-7634; 1873-7528; 1873-7528
• DOI: 10.1016/j.neubiorev.2017.03.018

•Human brain structural and functional networks follow small-world configuration.•Small-world model quantifies efficient information segregation and integration.•Small-world model captures individual cognition and exhibits physiological basis.•Small-world brain networks show dramatic changes with development, ageing and disease.•Small-world brain models promote the design of brain-like devices in engineering.
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.