1,720,972 research outputs found
Brain synchronizability, a false friend
No full textSynchronization plays a fundamental role in healthy cognitive and motor function. However, how synchronization depends on the interplay between local dynamics, coupling and topology and how prone to synchronization a network is, given its topological organization, are still poorly understood issues. To investigate the synchronizability of both anatomical and functional brain networks various studies resorted to the Master Stability Function (MSF) formalism, an elegant tool which allows analysing the stability of synchronous states in a dynamical system consisting of many coupled oscillators. Here, we argue that brain dynamics does not fulfil the formal criteria under which synchronizability is usually quantified and, perhaps more importantly, this measure refers to a global dynamical condition that never holds in the brain (not even in the most pathological conditions), and therefore no neurophysiological conclusions should be drawn based on it. We discuss the meaning of synchronizability and its applicability to neuroscience and propose alternative ways to quantify brain networks synchronization
Can multilayer brain networks be a real step forward?: Comment on “Network science of biological systems at different scales: A review” by M. Gosak et al
No full textVarious aspects of functional brain activity seem to capture genuine aspects of the functional organization of brain networks, making a MN representation more than a convenient representation tool. However, a series of fundamental problems arise with this new approach, which make the interpretation of multilayer brain networks a
terra incognita that will need to be explored in the near future
Reconstructing functional brain networks: Have we got the basics right?
No full textBoth at rest and during the executions of
cognitive tasks, the brain continuously creates
and reshapes complex patterns of correlated
dynamics. Thus, brain functional
activity is naturally described in terms of
networks, i.e., sets of nodes, representing
distinct subsystems, and links connecting
node pairs, representing relationships
between them.
Recently, brain function has started
being investigated using a statistical
physics understanding of graph theory,
an old branch of pure mathematics
(Newman, 2010). Within this framework,
network properties are independent of the
identity of their nodes, as they emerge
in a non-trivial way from their interactions.
Observed topologies are instances
of a network ensemble, falling into one of
few universality classes and are therefore
inherently statistical in nature.
Functional network reconstruction
comprises various steps: first, nodes are
identified; then, links are established
according to a certain metric. This gives
rise to a clique with an all-to-all connectivity.
Deciding which links are significant
is done by choosing which values of these
metrics should be taken into account.
Finally, network properties are computed
and used to characterize the network.
Each of these steps contains an element
of arbitrariness, as graph theory
allows characterizing systems once a network
is reconstructed, but is neutral as
to what should be treated as a system
and to how to isolate its constituent
parts.
Here we discuss some aspects related
to the way nodes, links and networks in
general are defined in system-level studies
using noninvasive techniques, which may
be critical when interpreting the results of
functional brain network analyses
Successful strategies for competing networks
No full textCompetitive interactions represent one of the driving forces behind evolution and natural selection in biological and sociological systems. For example, animals in an ecosystem may vie for food or mates; in a market economy, firms may compete over the same group of customers; sensory stimuli may compete for limited neural resources to enter the focus of attention. Here, we derive rules based on the spectral properties of the network governing the competitive interactions between groups of agents organized in networks. In the scenario studied here the winner of the competition, and the time needed to prevail, essentially depend on the way a given network connects to its competitors and on its internal structure. Our results allow assessment of the extent to which real networks optimize the outcome of their interaction, but also provide strategies through which competing networks can improve on their situation. The proposed approach is applicable to a wide range of systems that can be modelled as networks. Copyright © 2013 Macmillan Publishers Limited. All rights reserved
Editorial: On the relation of dynamics and structure in brain networks
No full textDespite more than a century-long effort, the functioning
of the few-pound lump of white and grey matter that forms
the brain remains at least partially a mystery. Physicists have
made some significant contributions to the understanding of
brain physiology, none perhaps more notable than Hodgkin
and Huxley’s, who discovered the ionic basis of nerve cell
conduction. But could they also help shedding light on how
large numbers of neurons interact to give rise to sophisti-
cated behaviour?
Although complex, a neural system is in fact essentially
a physical device meant to perform specific functions. As
such, brain design must obey general engineering principles,
which shape it at all scales from neuronal sub-components to
the whole system scales.
1
Observable anatomy and physiology
of the brain can be thought of as resulting from selective
evolutionary pressures that managed trade-offs between
energy consumption and adaptiveness, favouring energyefficient
wiring and coding patterns 2,3 and ultimately resulting
in a non-random spatial and temporal structure of brain
anatomy and dynamics. Making sense of this structure is
therefore key to our understanding of the emergence of brain
function
Beware of the small-world neuroscientist!
No full textIn spite of this preliminary evidence, whether or not the brain is indeed a SW network is still
very much an open question (Hilgetag and Goulas, 2015). The question that we address here is of a
pragmatical rather than an ontological nature: independently of whether the brain is a SW network or
not, to what extent can neuroscientists using standard system-level neuroimaging techniques interpret
the SW construct in the context of functional brain networks
Complex network theory and the brain
No full textThe first clear, recognizably scientific representations of the human brain were the
drawings and engravings of the Renaissance anatomists. These prototype anatomical
maps of brain organization demonstrated a physical structure somewhat
walnut-like in appearance: an approximately symmetrical pair of deeply wrinkled
lobes connected to each other by a central bridge of tissue. More extensive
and detailed dissection of the human brain revealed that its convoluted surface
is thinly covered (less than 3 mm) by a layer of so-called grey matter—
the cortex; and that anatomically separated regions of cortical grey matter are
extensively interconnected to each other (and to subcortical grey matter nuclei)
by axonal projections that are bundled together to form macroscopically visible
white matter tracts, including the major white matter tract linking the two
cerebral hemispheres
Role of inter-hemispheric connections in functional brain networks
No full textToday the human brain can be modeled as a graph where nodes represent different regions and links stand for statistical interactions between their activities as recorded by different neuroimaging techniques. Empirical studies have lead to the hypothesis that brain functions rely on the coordination of a scattered mosaic of functionally specialized brain regions (modules or sub-networks), forming a web-like structure of coordinated assemblies (a network of networks. NoN). The study of brain dynamics would therefore benefit from an inspection of how functional sub-networks interact between them. In this paper, we model the brain as an interconnected system composed of two specific sub-networks, the left (L) and right (R) hemispheres, which compete with each other for centrality, a topological measure of importance in a networked system. Specifically, we considered functional scalp EEG networks (SEN) derived from high-density electroencephalographic (EEG) recordings and investigated how node centrality is shaped by interhemispheric connections. Our results show that the distribution of centrality strongly depends on the number of functional connections between hemispheres and the way these connections are distributed. Additionally, we investigated the consequences of node failure on hemispherical centrality, and showed how the abundance of inter-hemispheric links favors the functional balance of centrality distribution between the hemispheres
Functional brain networks: Great expectations, hard times and the big leap forward
No full textMany physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can in..
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