946 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
Unifying the essential concepts of biological networks: biological insights and philosophical foundations
Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies
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
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios
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
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