435 research outputs found
Edge vulnerability in neural and metabolic networks
Biological networks, such as cellular metabolic pathways or networks of
corticocortical connections in the brain, are intricately organized, yet
remarkably robust toward structural damage. Whereas many studies have
investigated specific aspects of robustness, such as molecular mechanisms of
repair, this article focuses more generally on how local structural features in
networks may give rise to their global stability. In many networks the failure
of single connections may be more likely than the extinction of entire nodes,
yet no analysis of edge importance (edge vulnerability) has been provided so
far for biological networks. We tested several measures for identifying
vulnerable edges and compared their prediction performance in biological and
artificial networks. Among the tested measures, edge frequency in all shortest
paths of a network yielded a particularly high correlation with vulnerability,
and identified inter-cluster connections in biological but not in random and
scale-free benchmark networks. We discuss different local and global network
patterns and the edge vulnerability resulting from them.Comment: 8 pages, 4 figures, to appear in Biological Cybernetic
Perspective: network-guided pattern formation of neural dynamics
The understanding of neural activity patterns is fundamentally linked to an
understanding of how the brain's network architecture shapes dynamical
processes. Established approaches rely mostly on deviations of a given network
from certain classes of random graphs. Hypotheses about the supposed role of
prominent topological features (for instance, the roles of modularity, network
motifs, or hierarchical network organization) are derived from these
deviations. An alternative strategy could be to study deviations of network
architectures from regular graphs (rings, lattices) and consider the
implications of such deviations for self-organized dynamic patterns on the
network. Following this strategy, we draw on the theory of spatiotemporal
pattern formation and propose a novel perspective for analyzing dynamics on
networks, by evaluating how the self-organized dynamics are confined by network
architecture to a small set of permissible collective states. In particular, we
discuss the role of prominent topological features of brain connectivity, such
as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the
notion of network-guided pattern formation with numerical simulations and
outline how it can facilitate the understanding of neural dynamics
Predicting the connectivity of primate cortical networks from topological and spatial node properties
The organization of the connectivity between mammalian cortical areas has
become a major subject of study, because of its important role in scaffolding
the macroscopic aspects of animal behavior and intelligence. In this study we
present a computational reconstruction approach to the problem of network
organization, by considering the topological and spatial features of each area
in the primate cerebral cortex as subsidy for the reconstruction of the global
cortical network connectivity. Starting with all areas being disconnected,
pairs of areas with similar sets of features are linked together, in an attempt
to recover the original network structure. Inferring primate cortical
connectivity from the properties of the nodes, remarkably good reconstructions
of the global network organization could be obtained, with the topological
features allowing slightly superior accuracy to the spatial ones. Analogous
reconstruction attempts for the C. elegans neuronal network resulted in
substantially poorer recovery, indicating that cortical area interconnections
are relatively stronger related to the considered topological and spatial
properties than neuronal projections in the nematode. The close relationship
between area-based features and global connectivity may hint on developmental
rules and constraints for cortical networks. Particularly, differences between
the predictions from topological and spatial properties, together with the
poorer recovery resulting from spatial properties, indicate that the
organization of cortical networks is not entirely determined by spatial
constraints
Mapping the Connectome: Multi-Level Analysis of Brain Connectivity
Background and scope The brain contains vast numbers of interconnected neurons that constitute anatomical and functional networks. Structural descriptions of neuronal network elements and connections make up the “connectome ” of the brain (Hagmann, 2005; Sporns et al., 2005; Sporns, 2011), and are important for understanding normal brain function and disease-related dysfunction. A long-standing ambition of the neuroscience community has been to achieve complete connectome maps for the human brain as well as the brains of non-human primates, rodents, and other species (Bohland et al., 2009; Hagmann et al., 2010; Van Essen and Ugurbil, 2012). A wide repertoire of experimental tools is currently available to map neural connectivity at multiple levels, from the tracing of mesoscopic axonal connections and the delineation of white matter tracts (Saleem et al., 2002; Van der Linden et al., 2002; Sporns et al., 2005; Schmahmann et al., 2007; Hagmann et al., 2010), the mappin
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A predictive model of the cat cortical connectome based on cytoarchitecture and distance
Information processing in the brain is strongly constrained by anatomical connectivity. However, the principles governing the organization of corticocortical connections remain elusive. Here, we tested three models of relationships between the organization of cortical structure and features of connections linking 49 areas of the cat cerebral cortex. Factors taken into account were relative cytoarchitectonic differentiation (‘structural model’), relative spatial position (‘distance model’), or relative hierarchical position (‘hierarchical model’) of the areas. Cytoarchitectonic differentiation and spatial distance (themselves uncorrelated) correlated strongly with the existence of inter-areal connections, whereas no correlation was found with relative hierarchical position. Moreover, a strong correlation was observed between patterns of laminar projection origin or termination and cytoarchitectonic differentiation. Additionally, cytoarchitectonic differentiation correlated with the absolute number of corticocortical connections formed by areas, and varied characteristically between different cortical subnetworks, including a ‘richclub’ module of hub areas. Thus, connections between areas of the cat cerebral cortex can, to a large part, be explained by the two independent factors of relative cytoarchitectonic differentiation and spatial distance of brain regions. As both the structural and distance model were originally formulated in the macaque monkey, their applicability in another mammalian species suggests a general principle of global cortical organization
Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems
It has been suggested that neural systems across several scales of
organization show optimal component placement, in which any spatial
rearrangement of the components would lead to an increase of total wiring.
Using extensive connectivity datasets for diverse neural networks combined with
spatial coordinates for network nodes, we applied an optimization algorithm to
the network layouts, in order to search for wire-saving component
rearrangements. We found that optimized component rearrangements could
substantially reduce total wiring length in all tested neural networks.
Specifically, total wiring among 95 primate (Macaque) cortical areas could be
decreased by 32%, and wiring of neuronal networks in the nematode
Caenorhabditis elegans could be reduced by 48% on the global level, and by 49%
for neurons within frontal ganglia. Wiring length reductions were possible due
to the existence of long-distance projections in neural networks. We explored
the role of these projections by comparing the original networks with minimally
rewired networks of the same size, which possessed only the shortest possible
connections. In the minimally rewired networks, the number of processing steps
along the shortest paths between components was significantly increased
compared to the original networks. Additional benchmark comparisons also
indicated that neural networks are more similar to network layouts that
minimize the length of processing paths, rather than wiring length. These
findings suggest that neural systems are not exclusively optimized for minimal
global wiring, but for a variety of factors including the minimization of
processing steps.Comment: 11 pages, 5 figure
Resolving structural variability in network models and the brain
Large-scale white matter pathways crisscrossing the cortex create a complex
pattern of connectivity that underlies human cognitive function. Generative
mechanisms for this architecture have been difficult to identify in part
because little is known about mechanistic drivers of structured networks. Here
we contrast network properties derived from diffusion spectrum imaging data of
the human brain with 13 synthetic network models chosen to probe the roles of
physical network embedding and temporal network growth. We characterize both
the empirical and synthetic networks using familiar diagnostics presented in
statistical form, as scatter plots and distributions, to reveal the full range
of variability of each measure across scales in the network. We focus on the
degree distribution, degree assortativity, hierarchy, topological Rentian
scaling, and topological fractal scaling---in addition to several summary
statistics, including the mean clustering coefficient, shortest path length,
and network diameter. The models are investigated in a progressive, branching
sequence, aimed at capturing different elements thought to be important in the
brain, and range from simple random and regular networks, to models that
incorporate specific growth rules and constraints. We find that synthetic
models that constrain the network nodes to be embedded in anatomical brain
regions tend to produce distributions that are similar to those extracted from
the brain. We also find that network models hardcoded to display one network
property do not in general also display a second, suggesting that multiple
neurobiological mechanisms might be at play in the development of human brain
network architecture. Together, the network models that we develop and employ
provide a potentially useful starting point for the statistical inference of
brain network structure from neuroimaging data.Comment: 24 pages, 11 figures, 1 table, supplementary material
Role of Mechanical Factors in the Morphology of the Primate Cerebral Cortex
The convoluted cortex of primates is instantly recognizable in its principal morphologic features, yet puzzling in its complex finer structure. Various hypotheses have been proposed about the mechanisms of its formation. Based on the analysis of databases of quantitative architectonic and connection data for primate prefrontal cortices, we offer support for the hypothesis that tension exerted by corticocortical connections is a significant factor in shaping the cerebral cortical landscape. Moreover, forces generated by cortical folding influence laminar morphology, and appear to have a previously unsuspected impact on cellular migration during cortical development. The evidence for a significant role of mechanical factors in cortical morphology opens the possibility of constructing computational models of cortical develoment based on physical principles. Such models are particularly relevant for understanding the relationship of cortical morphology to the connectivity of normal brains, and structurally altered brains in diseases of developmental origin, such as schizophrenia and autism
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