The Non-Random Brain: Efficiency, Economy, and Complex Dynamics

Olaf Sporns
2011 Frontiers in Computational Neuroscience  
Sporns, 2010). The power of network analysis derives from its applicability to brain connectivity data from different spatial scales, as well as networks derived from anatomical or physiological observations. At the time of writing, virtually all network studies carried out on neural data sets from a variety of species and neural systems have revealed characteristic non-random attributes. The ubiquity of non-random connectivity raises important questions about the origin and functional
more » ... e of non-random patterns. This review focuses mainly on non-random aspects in the organization of mammalian cortex, mapped at the large scale of brain regions and pathways. While most examples are drawn from studies of anatomical connectivity, the relation of anatomical to functional networks will also be addressed. First, after a brief explanation of key terms in graph theory and network analysis, we will turn to a discussion of the meaning of randomness in networks and its manifestation in models and empirical studies of neural circuits. The next two sections are devoted to a survey of various nonrandom network attributes encountered at the large scale of brain regions and pathways, primarily within the mammalian cortex, and to the putative origin of non-random patterns in brain networks. After discussing the role of non-random connectivity in generating complex neural dynamics, we turn to a brief overview of disturbances of connectivity in brain disease and examine the hypothesis that several common brain disorders are associated with increased randomness in structural and functional brain networks. 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 non-random 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 non-random 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 non-random structure of neural connectivity, primarily at the large scale of regions and pathways in the mammalian cerebral cortex. In addition, we will discuss how non-random 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.
doi:10.3389/fncom.2011.00005 pmid:21369354 pmcid:PMC3037776 fatcat:cc4creu7b5au7f4qk5w3xcxjj4