7,540 research outputs found
Navigation of brain networks
Understanding the mechanisms of neural communication in large-scale brain
networks remains a major goal in neuroscience. We investigated whether
navigation is a parsimonious routing model for connectomics. Navigating a
network involves progressing to the next node that is closest in distance to a
desired destination. We developed a measure to quantify navigation efficiency
and found that connectomes in a range of mammalian species (human, mouse and
macaque) can be successfully navigated with near-optimal efficiency (>80% of
optimal efficiency for typical connection densities). Rewiring network topology
or repositioning network nodes resulted in 45%-60% reductions in navigation
performance. Specifically, we found that brain networks cannot be progressively
rewired (randomized or clusterized) to result in topologies with significantly
improved navigation performance. Navigation was also found to: i) promote a
resource-efficient distribution of the information traffic load, potentially
relieving communication bottlenecks; and, ii) explain significant variation in
functional connectivity. Unlike prevalently studied communication strategies in
connectomics, navigation does not mandate biologically unrealistic assumptions
about global knowledge of network topology. We conclude that the wiring and
spatial embedding of brain networks is conducive to effective decentralized
communication. Graph-theoretic studies of the connectome should consider
measures of network efficiency and centrality that are consistent with
decentralized models of neural communication
Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations
For more than a century, cerebral cartography has been driven by
investigations of structural and morphological properties of the brain across
spatial scales and the temporal/functional phenomena that emerge from these
underlying features. The next era of brain mapping will be driven by studies
that consider both of these components of brain organization simultaneously --
elucidating their interactions and dependencies. Using this guiding principle,
we explored the origin of slowly fluctuating patterns of synchronization within
the topological core of brain regions known as the rich club, implicated in the
regulation of mood and introspection. We find that a constellation of densely
interconnected regions that constitute the rich club (including the anterior
insula, amygdala, and precuneus) play a central role in promoting a stable,
dynamical core of spontaneous activity in the primate cortex. The slow time
scales are well matched to the regulation of internal visceral states,
corresponding to the somatic correlates of mood and anxiety. In contrast, the
topology of the surrounding "feeder" cortical regions show unstable, rapidly
fluctuating dynamics likely crucial for fast perceptual processes. We discuss
these findings in relation to psychiatric disorders and the future of
connectomics.Comment: 35 pages, 6 figure
Resting-state connectivity and functional specialization in human medial parieto-occipital cortex
According to recent models of visuo-spatial processing, the medial parieto-occipital cortex is a crucial node of the dorsal visual stream. Evidence from neurophysiological studies in monkeys has indicated that the parieto-occipital sulcus (POS) contains three functionally and cytoarchitectonically distinct areas: the visual area V6 in the fundus of the POS, and the visuo-motor areas V6Av and V6Ad in a progressively dorsal and anterior location with respect to V6. Besides different topographical organization, cytoarchitectonics, and functional properties, these three monkey areas can also be distinguished based on their patterns of cortico-cortical connections. Thanks to wide-field retinotopic mapping, areas V6 and V6Av have been also mapped in the human brain. Here, using a combined approach of resting-state functional connectivity and task-evoked activity by fMRI, we identified a new region in the anterior POS showing a pattern of functional properties and cortical connections that suggests a homology with the monkey area V6Ad. In addition, we observed distinct patterns of cortical connections associated with the human V6 and V6Av which are remarkably consistent with those showed by the anatomical tracing studies in the corresponding monkey areas. Consistent with recent models on visuo-spatial processing, our findings demonstrate a gradient of functional specialization and cortical connections within the human POS, with more posterior regions primarily dedicated to the analysis of visual attributes useful for spatial navigation and more anterior regions primarily dedicated to analyses of spatial information relevant for goal-directed action
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
The specificity and robustness of long-distance connections in weighted, interareal connectomes
Brain areas' functional repertoires are shaped by their incoming and outgoing
structural connections. In empirically measured networks, most connections are
short, reflecting spatial and energetic constraints. Nonetheless, a small
number of connections span long distances, consistent with the notion that the
functionality of these connections must outweigh their cost. While the precise
function of these long-distance connections is not known, the leading
hypothesis is that they act to reduce the topological distance between brain
areas and facilitate efficient interareal communication. However, this
hypothesis implies a non-specificity of long-distance connections that we
contend is unlikely. Instead, we propose that long-distance connections serve
to diversify brain areas' inputs and outputs, thereby promoting complex
dynamics. Through analysis of five interareal network datasets, we show that
long-distance connections play only minor roles in reducing average interareal
topological distance. In contrast, areas' long-distance and short-range
neighbors exhibit marked differences in their connectivity profiles, suggesting
that long-distance connections enhance dissimilarity between regional inputs
and outputs. Next, we show that -- in isolation -- areas' long-distance
connectivity profiles exhibit non-random levels of similarity, suggesting that
the communication pathways formed by long connections exhibit redundancies that
may serve to promote robustness. Finally, we use a linearization of
Wilson-Cowan dynamics to simulate the covariance structure of neural activity
and show that in the absence of long-distance connections, a common measure of
functional diversity decreases. Collectively, our findings suggest that
long-distance connections are necessary for supporting diverse and complex
brain dynamics.Comment: 18 pages, 8 figure
Characterization of the Community Structure of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction
One of the central questions in neuroscience is to understand the way
communication is organized in the brain, trying to comprehend how cognitive
capacities or physiological states of the organism are potentially related to
brain activities involving interactions of several brain areas. One important
characteristic of the functional brain networks is that they are modularly
structured, being this modular architecture regarded to account for a series of
properties and functional dynamics. In the neurobiological context, communities
may indicate brain regions that are involved in one same activity, representing
neural segregated processes. Several studies have demonstrated the modular
character of organization of brain activities. However, empirical evidences
regarding to its dynamics and relation to different levels of consciousness
have not been reported yet. Within this context, this research sought to
characterize the community structure of functional brain networks during an
anesthetic induction process. The experiment was based on intra-cranial
recordings of neural activities of an old world macaque of the species Macaca
fuscata during a Ketamine-Medetomidine anesthetic induction process. Networks
were serially estimated in time intervals of five seconds. Changes were
observed within about one and a half minutes after the administration of the
anesthetics, revealing the occurrence of a transition on the community
structure. The awake state was characterized by the presence of large clusters
involving frontal and parietal regions, while the anesthetized state by the
presence of communities in the primary visual and motor cortices, being the
areas of the secondary associative cortex most affected. The results report the
influence of general anesthesia on the structure of functional clusters,
contributing for understanding some new aspects of neural correlates of
consciousness.Comment: 24 pages, 8 figures. arXiv admin note: text overlap with
arXiv:1604.0000
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