273,687 research outputs found
Brain networks under attack : robustness properties and the impact of lesions
A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research
The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone
Osteocytes and their cell processes reside in a large, interconnected network
of voids pervading the mineralized bone matrix of most vertebrates. This
osteocyte lacuno-canalicular network (OLCN) is believed to play important roles
in mechanosensing, mineral homeostasis, and for the mechanical properties of
bone. While the extracellular matrix structure of bone is extensively studied
on ultrastructural and macroscopic scales, there is a lack of quantitative
knowledge on how the cellular network is organized. Using a recently introduced
imaging and quantification approach, we analyze the OLCN in different bone
types from mouse and sheep that exhibit different degrees of structural
organization not only of the cell network but also of the fibrous matrix
deposited by the cells. We define a number of robust, quantitative measures
that are derived from the theory of complex networks. These measures enable us
to gain insights into how efficient the network is organized with regard to
intercellular transport and communication. Our analysis shows that the cell
network in regularly organized, slow-growing bone tissue from sheep is less
connected, but more efficiently organized compared to irregular and
fast-growing bone tissue from mice. On the level of statistical topological
properties (edges per node, edge length and degree distribution), both network
types are indistinguishable, highlighting that despite pronounced differences
at the tissue level, the topological architecture of the osteocyte canalicular
network at the subcellular level may be independent of species and bone type.
Our results suggest a universal mechanism underlying the self-organization of
individual cells into a large, interconnected network during bone formation and
mineralization
Master stability functions reveal diffusion-driven pattern formation in networks
We study diffusion-driven pattern-formation in networks of networks, a class
of multilayer systems, where different layers have the same topology, but
different internal dynamics. Agents are assumed to disperse within a layer by
undergoing random walks, while they can be created or destroyed by reactions
between or within a layer. We show that the stability of homogeneous steady
states can be analyzed with a master stability function approach that reveals a
deep analogy between pattern formation in networks and pattern formation in
continuous space.For illustration we consider a generalized model of ecological
meta-foodwebs. This fairly complex model describes the dispersal of many
different species across a region consisting of a network of individual
habitats while subject to realistic, nonlinear predator-prey interactions. In
this example the method reveals the intricate dependence of the dynamics on the
spatial structure. The ability of the proposed approach to deal with this
fairly complex system highlights it as a promising tool for ecology and other
applications.Comment: 20 pages, 5 figures, to appear in Phys. Rev. E (2018
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