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The non-random brain: efficiency, economy, and complex dynamics.

Sporns O - Front Comput Neurosci (2011)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychological and Brain Sciences, Indiana University Bloomington, IN, USA.

ABSTRACT
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.

No MeSH data available.


Graph evolution for neural complexity. The initial population of graphs in generation 1 consisted of 10 randomized graphs similar to the ones shown in Figure 3B, with 47 nodes and 505 edges. Simple linear dynamics (Galán, 2008) was run on these graphs and the graph generating the highest neural complexity (Tononi et al., 1994) was selected and copied forward to the next generation, as described in Sporns et al. (2000). Then, small random “mutations” were introduced in the graph's “offspring” and the process of selecting for complex dynamics was continued for a total of 50,000 generations. (A) Plots show the increase in complexity and a parallel increase in modularity. (B) Examples of graphs obtained at the end of the simulations exhibit non-random topologies, including higSh modularity and hub nodes.
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Figure 5: Graph evolution for neural complexity. The initial population of graphs in generation 1 consisted of 10 randomized graphs similar to the ones shown in Figure 3B, with 47 nodes and 505 edges. Simple linear dynamics (Galán, 2008) was run on these graphs and the graph generating the highest neural complexity (Tononi et al., 1994) was selected and copied forward to the next generation, as described in Sporns et al. (2000). Then, small random “mutations” were introduced in the graph's “offspring” and the process of selecting for complex dynamics was continued for a total of 50,000 generations. (A) Plots show the increase in complexity and a parallel increase in modularity. (B) Examples of graphs obtained at the end of the simulations exhibit non-random topologies, including higSh modularity and hub nodes.

Mentions: These fluctuations in dynamic couplings greatly increase the dynamic repertoire of neuronal states. A large repertoire of diverse states may be beneficial to an organism as it contributes to its capacity to process signals from an environment that can only be partially predicted. The relationship between structural connectivity and rich and diverse neural dynamics continues to be relatively unexplored. Some computational studies suggest that non-random attributes of structural connectivity discussed earlier, such as modularity and small-world architecture, give rise to complex patterns of neural dynamics (Tononi et al., 1994). For example, the combination of high clustering and short path length encountered in small-world networks promotes the coexistence of segregation and integration of neural information, a key ingredient of neural complexity (Tononi et al., 1994; Sporns et al., 2000; Figure 5). Segregation and integration jointly capture different aspects of the way neural information is distributed and integrated in a network. High segregation involves the existence of many specialized communities that maintain coherence amongst their members, but relative independence between groups. High integration involves the flow if information between all elements of the network, both within and between communities. Viewed from a network perspective, the small-world attributes of clustering and efficiency, embodied in the brain's module/hub architecture, jointly enable information specialization and integration. Non-random attributes of the structural connectivity of the human brain may thus have significant impact on the distribution and flow of information in neural circuits.


The non-random brain: efficiency, economy, and complex dynamics.

Sporns O - Front Comput Neurosci (2011)

Graph evolution for neural complexity. The initial population of graphs in generation 1 consisted of 10 randomized graphs similar to the ones shown in Figure 3B, with 47 nodes and 505 edges. Simple linear dynamics (Galán, 2008) was run on these graphs and the graph generating the highest neural complexity (Tononi et al., 1994) was selected and copied forward to the next generation, as described in Sporns et al. (2000). Then, small random “mutations” were introduced in the graph's “offspring” and the process of selecting for complex dynamics was continued for a total of 50,000 generations. (A) Plots show the increase in complexity and a parallel increase in modularity. (B) Examples of graphs obtained at the end of the simulations exhibit non-random topologies, including higSh modularity and hub nodes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3037776&req=5

Figure 5: Graph evolution for neural complexity. The initial population of graphs in generation 1 consisted of 10 randomized graphs similar to the ones shown in Figure 3B, with 47 nodes and 505 edges. Simple linear dynamics (Galán, 2008) was run on these graphs and the graph generating the highest neural complexity (Tononi et al., 1994) was selected and copied forward to the next generation, as described in Sporns et al. (2000). Then, small random “mutations” were introduced in the graph's “offspring” and the process of selecting for complex dynamics was continued for a total of 50,000 generations. (A) Plots show the increase in complexity and a parallel increase in modularity. (B) Examples of graphs obtained at the end of the simulations exhibit non-random topologies, including higSh modularity and hub nodes.
Mentions: These fluctuations in dynamic couplings greatly increase the dynamic repertoire of neuronal states. A large repertoire of diverse states may be beneficial to an organism as it contributes to its capacity to process signals from an environment that can only be partially predicted. The relationship between structural connectivity and rich and diverse neural dynamics continues to be relatively unexplored. Some computational studies suggest that non-random attributes of structural connectivity discussed earlier, such as modularity and small-world architecture, give rise to complex patterns of neural dynamics (Tononi et al., 1994). For example, the combination of high clustering and short path length encountered in small-world networks promotes the coexistence of segregation and integration of neural information, a key ingredient of neural complexity (Tononi et al., 1994; Sporns et al., 2000; Figure 5). Segregation and integration jointly capture different aspects of the way neural information is distributed and integrated in a network. High segregation involves the existence of many specialized communities that maintain coherence amongst their members, but relative independence between groups. High integration involves the flow if information between all elements of the network, both within and between communities. Viewed from a network perspective, the small-world attributes of clustering and efficiency, embodied in the brain's module/hub architecture, jointly enable information specialization and integration. Non-random attributes of the structural connectivity of the human brain may thus have significant impact on the distribution and flow of information in neural circuits.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychological and Brain Sciences, Indiana University Bloomington, IN, USA.

ABSTRACT
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.

No MeSH data available.