The Non-Random Brain: Efficiency, Economy, and Complex Dynamics - Sporns, Olaf (Frontiers in Computational Neuroscience, 2011)
Modern anatomical tracing and imaging techniques are beginning to reveal the structural anatomy of neural circuits at small and large scales in unprecedented detail. When examined with analytic tools from graph theory and network science, neural connectivity exhibits highly nonrandom features, including high clustering and short path length, as well as modules and highly central hub nodes. These characteristic topological features of neural connections shape nonrandom dynamic interactions that occur during spontaneous activity or in response to external stimulation. Disturbances of connectivity and thus of neural dynamics are thought to underlie a number of disease states of the brain, and some evidence suggests that degraded functional performance of brain networks may be the outcome of a process of randomization affecting their nodes and edges. This article provides a survey of the nonrandom structure of neural connectivity, primarily at the large-scale of regions and pathways in the mammalian cerebral cortex. In addition, we will discuss how nonrandom connections can give rise to differentiated and complex patterns of dynamics and information flow. Finally, we will explore the idea that at least some disorders of the nervous system are associated with increased randomness of neural connections.