969 research outputs found
The Potential of the Human Connectome as a Biomarker of Brain Disease
The human connectome at the level of fiber tracts between brain regions has
been shown to differ in patients with brain disorders compared to healthy
control groups. Nonetheless, there is a potentially large number of different
network organizations for individual patients that could lead to cognitive
deficits prohibiting correct diagnosis. Therefore changes that can distinguish
groups might not be sufficient to diagnose the disease that an individual
patient suffers from and to indicate the best treatment option for that
patient. We describe the challenges introduced by the large variability of
connectomes within healthy subjects and patients and outline three common
strategies to use connectomes as biomarkers of brain diseases. Finally, we
propose a fourth option in using models of simulated brain activity (the
dynamic connectome) based on structural connectivity rather than the structure
(connectome) itself as a biomarker of disease. Dynamic connectomes, in addition
to currently used structural, functional, or effective connectivity, could be
an important future biomarker for clinical applications.Comment: Perspective Article for special issue on Magnetic Resonance Imaging
of Healthy and Diseased Brain Network
Brain architecture: A design for natural computation
Fifty years ago, John von Neumann compared the architecture of the brain with
that of computers that he invented and which is still in use today. In those
days, the organisation of computers was based on concepts of brain
organisation. Here, we give an update on current results on the global
organisation of neural systems. For neural systems, we outline how the spatial
and topological architecture of neuronal and cortical networks facilitates
robustness against failures, fast processing, and balanced network activation.
Finally, we discuss mechanisms of self-organization for such architectures.
After all, the organization of the brain might again inspire computer
architecture
Evolution and development of Brain Networks: From Caenorhabditis elegans to Homo sapiens
Neural networks show a progressive increase in complexity during the time
course of evolution. From diffuse nerve nets in Cnidaria to modular,
hierarchical systems in macaque and humans, there is a gradual shift from
simple processes involving a limited amount of tasks and modalities to complex
functional and behavioral processing integrating different kinds of information
from highly specialized tissue. However, studies in a range of species suggest
that fundamental similarities, in spatial and topological features as well as
in developmental mechanisms for network formation, are retained across
evolution. 'Small-world' topology and highly connected regions (hubs) are
prevalent across the evolutionary scale, ensuring efficient processing and
resilience to internal (e.g. lesions) and external (e.g. environment) changes.
Furthermore, in most species, even the establishment of hubs, long-range
connections linking distant components, and a modular organization, relies on
similar mechanisms. In conclusion, evolutionary divergence leads to greater
complexity while following essential developmental constraints
Developmental time windows for axon growth influence neuronal network topology
Early brain connectivity development consists of multiple stages: birth of
neurons, their migration and the subsequent growth of axons and dendrites. Each
stage occurs within a certain period of time depending on types of neurons and
cortical layers. Forming synapses between neurons either by growing axons
starting at similar times for all neurons (much-overlapped time windows) or at
different time points (less-overlapped) may affect the topological and spatial
properties of neuronal networks. Here, we explore the extreme cases of axon
formation especially concerning short-distance connectivity during early
development, either starting at the same time for all neurons (parallel, i.e.
maximally-overlapped time windows) or occurring for each neuron separately one
neuron after another (serial, i.e. no overlaps in time windows). For both
cases, the number of potential and established synapses remained comparable.
Topological and spatial properties, however, differed: neurons that started
axon growth early on in serial growth achieved higher out-degrees, higher local
efficiency, and longer axon lengths while neurons demonstrated more homogeneous
connectivity patterns for parallel growth. Second, connection probability
decreased more rapidly with distance between neurons for parallel growth than
for serial growth. Third, bidirectional connections were more numerous for
parallel growth. Finally, we tested our predictions with C. elegans data.
Together, this indicates that time windows for axon growth influence the
topological and spatial properties of neuronal networks opening the possibility
to a posteriori estimate developmental mechanisms based on network properties
of a developed network.Comment: Biol Cybern. 2015 Jan 30. [Epub ahead of print
From Caenorhabditis elegans to the Human Connectome: A Specific Modular Organisation Increases Metabolic, Functional, and Developmental Efficiency
The connectome, or the entire connectivity of a neural system represented by
network, ranges various scales from synaptic connections between individual
neurons to fibre tract connections between brain regions. Although the
modularity they commonly show has been extensively studied, it is unclear
whether connection specificity of such networks can already be fully explained
by the modularity alone. To answer this question, we study two networks, the
neuronal network of C. elegans and the fibre tract network of human brains
yielded through diffusion spectrum imaging (DSI). We compare them to their
respective benchmark networks with varying modularities, which are generated by
link swapping to have desired modularity values but otherwise maximally random.
We find several network properties that are specific to the neural networks and
cannot be fully explained by the modularity alone. First, the clustering
coefficient and the characteristic path length of C. elegans and human
connectomes are both higher than those of the benchmark networks with similar
modularity. High clustering coefficient indicates efficient local information
distribution and high characteristic path length suggests reduced global
integration. Second, the total wiring length is smaller than for the
alternative configurations with similar modularity. This is due to lower
dispersion of connections, which means each neuron in C. elegans connectome or
each region of interest (ROI) in human connectome reaches fewer ganglia or
cortical areas, respectively. Third, both neural networks show lower
algorithmic entropy compared to the alternative arrangements. This implies that
fewer rules are needed to encode for the organisation of neural systems
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
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