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Noise during rest enables the exploration of the brain's dynamic repertoire.

Ghosh A, Rho Y, McIntosh AR, Kötter R, Jirsa VK - PLoS Comput. Biol. (2008)

Bottom Line: Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network.The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging.The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Neuroscience Group, Institut des Sciences du Mouvement, Marseille, France. Anandamohan.GHOSH@univmed.fr

ABSTRACT
Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

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Anatomical connectivity.(A) The primate cortical connectivity map is overlaid over the human cortical locations. See Table 1 for abbreviations. (B) The connectivity matrix of primate cortical graph (white pixel implies no connection; grey scale indicates the weight of the connection). The columns are sources and rows are targets. (C) Statistical characterization of the primate connectivity matrix: node wise degree distributions, clustering index and betweeness centrality (14). The color codes for areas of interest: green, prefrontal; magenta, parietal; yellow, cingulate; black, all others. The connectivity matrix has characteristic path length = 1.633; clustering index = 0.568.
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pcbi-1000196-g001: Anatomical connectivity.(A) The primate cortical connectivity map is overlaid over the human cortical locations. See Table 1 for abbreviations. (B) The connectivity matrix of primate cortical graph (white pixel implies no connection; grey scale indicates the weight of the connection). The columns are sources and rows are targets. (C) Statistical characterization of the primate connectivity matrix: node wise degree distributions, clustering index and betweeness centrality (14). The color codes for areas of interest: green, prefrontal; magenta, parietal; yellow, cingulate; black, all others. The connectivity matrix has characteristic path length = 1.633; clustering index = 0.568.

Mentions: We first performed a graph theoretical analysis of the anatomical connectivity matrix of a single hemisphere obtained from the CoCoMac database [21]. Thus, we initially consider only the spatial aspect of the couplings. The connectivity matrix collated from macaque tracing studies comprises 38 nodes with weights ranging from 0 to 3 (see Figure 1; see also Table 1 for abbreviations). The corresponding “Regional map” gives the translation between macaque and human neuroanatomy [22]. It is to be noted that some connections between some areas are not known. For the subsequent simulations we assign random weights to these unknowns within the range of 0 and 1, but omit these in the graph theoretical analyses. The connectivity matrix is shown in Figure 1B, where the columns are targets and the rows are sources. To explore the connectivity characteristics quantitatively, we compute a set of network connectivity measures [20],[23] for all nodes including the in- and out-degree of connectivity, the clustering coefficient and betweeness centrality (see Methods) are computed on the binarized graph, and are shown in Figure 1C. When computing these measures for the weighted graphs, no significant differences are found. If the putative components of the resting state networks (see Table 2) differentiated themselves from other network nodes on the pure basis of anatomical connectivity, we would anticipate finding a clustering of these components in some of the graph theoretical measures, likely at the largest values. For better visualization, we use a color coding for the various components in Figure 1C. Anatomically, the prefrontal cortex is characterized by a large degree of afferent and efferent connectivity, whereas the temporal and medial parietal areas display only an intermediate degree of connectedness. Clustering index and betweeness centrality are commonly used to identify hubs in a network, but do not clearly differentiate the default network either, though the betweeness centrality measure shows a cluster of six components comprising prefrontal, parietal and cingulate cortices (PFCCL, PFCVL, CCA, CCP, PCIP, PCI) for midrange values. Based on this graph theoretical analysis, we find that pure anatomical connectivity of the large scale network does not suffice to reliably identify the network constituents during rest.


Noise during rest enables the exploration of the brain's dynamic repertoire.

Ghosh A, Rho Y, McIntosh AR, Kötter R, Jirsa VK - PLoS Comput. Biol. (2008)

Anatomical connectivity.(A) The primate cortical connectivity map is overlaid over the human cortical locations. See Table 1 for abbreviations. (B) The connectivity matrix of primate cortical graph (white pixel implies no connection; grey scale indicates the weight of the connection). The columns are sources and rows are targets. (C) Statistical characterization of the primate connectivity matrix: node wise degree distributions, clustering index and betweeness centrality (14). The color codes for areas of interest: green, prefrontal; magenta, parietal; yellow, cingulate; black, all others. The connectivity matrix has characteristic path length = 1.633; clustering index = 0.568.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2551736&req=5

pcbi-1000196-g001: Anatomical connectivity.(A) The primate cortical connectivity map is overlaid over the human cortical locations. See Table 1 for abbreviations. (B) The connectivity matrix of primate cortical graph (white pixel implies no connection; grey scale indicates the weight of the connection). The columns are sources and rows are targets. (C) Statistical characterization of the primate connectivity matrix: node wise degree distributions, clustering index and betweeness centrality (14). The color codes for areas of interest: green, prefrontal; magenta, parietal; yellow, cingulate; black, all others. The connectivity matrix has characteristic path length = 1.633; clustering index = 0.568.
Mentions: We first performed a graph theoretical analysis of the anatomical connectivity matrix of a single hemisphere obtained from the CoCoMac database [21]. Thus, we initially consider only the spatial aspect of the couplings. The connectivity matrix collated from macaque tracing studies comprises 38 nodes with weights ranging from 0 to 3 (see Figure 1; see also Table 1 for abbreviations). The corresponding “Regional map” gives the translation between macaque and human neuroanatomy [22]. It is to be noted that some connections between some areas are not known. For the subsequent simulations we assign random weights to these unknowns within the range of 0 and 1, but omit these in the graph theoretical analyses. The connectivity matrix is shown in Figure 1B, where the columns are targets and the rows are sources. To explore the connectivity characteristics quantitatively, we compute a set of network connectivity measures [20],[23] for all nodes including the in- and out-degree of connectivity, the clustering coefficient and betweeness centrality (see Methods) are computed on the binarized graph, and are shown in Figure 1C. When computing these measures for the weighted graphs, no significant differences are found. If the putative components of the resting state networks (see Table 2) differentiated themselves from other network nodes on the pure basis of anatomical connectivity, we would anticipate finding a clustering of these components in some of the graph theoretical measures, likely at the largest values. For better visualization, we use a color coding for the various components in Figure 1C. Anatomically, the prefrontal cortex is characterized by a large degree of afferent and efferent connectivity, whereas the temporal and medial parietal areas display only an intermediate degree of connectedness. Clustering index and betweeness centrality are commonly used to identify hubs in a network, but do not clearly differentiate the default network either, though the betweeness centrality measure shows a cluster of six components comprising prefrontal, parietal and cingulate cortices (PFCCL, PFCVL, CCA, CCP, PCIP, PCI) for midrange values. Based on this graph theoretical analysis, we find that pure anatomical connectivity of the large scale network does not suffice to reliably identify the network constituents during rest.

Bottom Line: Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network.The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging.The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Neuroscience Group, Institut des Sciences du Mouvement, Marseille, France. Anandamohan.GHOSH@univmed.fr

ABSTRACT
Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

Show MeSH
Related in: MedlinePlus