A scholarly review of the early literature on the physics of complex networks, with an emphasis on various types of scale-free and small-world connectivity.īoccaletti, S., Latora, V., Moreno, Y., Chavez, M. Statistical mechanics of complex networks. Beyond phrenology: what can neuroimaging tell us about distributed circuitry? Annu. Large-scale cortical networks and cognition. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Neuronal synchrony: a versatile code for the definition of relations? Neuron 24, 49–65 (1999).įries, P. Histology of the Nervous System of Man and Vertebrates (Oxford Univ. Key issues for future work include clarifying the relationship between the brain's network properties and its emergent cognitive behaviours in health and disease.Ĭajal, S. The network organization of the brain, as it is beginning to be revealed by graph theory, is compatible with the hypothesis that the brain, perhaps in common with other complex networks, has evolved both to maximize the efficiency of information transfer and to minimize connection cost, at all scales of space and time. Graph theory can help us to understand the vulnerability of brain networks to lesions and could in future be used to provide markers of genetic risk for disorders or to measure therapeutic effects of drug treatments on functional networks. Neuropsychiatric disorders can be thought of as dysconnectivity syndromes, and graph theory has already been used to quantify abnormality of structural and functional network properties in schizophrenia, Alzheimer's disease and other disorders. Conversely, over a slower timescale the dynamics can modulate structural network topology. The topology, synchronizability and other dynamic properties of functional networks are strongly affected by small-world and other metrics of structural connectivity. Additionally, complex network properties including small-worldness and the existence of hubs are conserved over different frequency scales in functional MRI and electrophysiological data.Ĭonvergent experimental and computational data suggest that there is interdependence in the organization of structural and functional networks. Human brain anatomical networks, derived from MRI or diffusion tensor imaging data, have high-degree cortical 'hubs' and modular and hierarchical properties.įunctional networks also demonstrate small-world properties at whole-brain and cellular spatial scales. In the past decade, developments in graph theory have provided many new methods for topologically analysing complex networks, some of which have already been translated to the characterization of anatomical and functional brain networks.Īnatomical networks at whole-brain and cellular scales in several species consistently demonstrate conservation of wiring costs and small-world topology (high clustering and short path length). Understanding the network organization of the brain has been a long-standing challenge for neuroscience.
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