oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2018 ( 1 )

2017 ( 2 )

2016 ( 5 )

2015 ( 58 )

Custom range...

Search Results: 1 - 10 of 1313 matches for " Olaf Sporns "
All listed articles are free for downloading (OA Articles)
Page 1 /1313
Display every page Item
The Non-Random Brain: Efficiency, Economy, and Complex Dynamics
Olaf Sporns
Frontiers in Computational Neuroscience , 2011, DOI: 10.3389/fncom.2011.00005
Abstract: Modern anatomical tracing and imaging techniques are beginning to reveal the structural anatomy of neural circuits at small and large scales in unprecedented detail. When examined with analytic tools from graph theory and network science, neural connectivity exhibits highly non-random features, including high clustering and short path length, as well as modules and highly central hub nodes. These characteristic topological features of neural connections shape non-random dynamic interactions that occur during spontaneous activity or in response to external stimulation. Disturbances of connectivity and thus of neural dynamics are thought to underlie a number of disease states of the brain, and some evidence suggests that degraded functional performance of brain networks may be the outcome of a process of randomization affecting their nodes and edges. This article provides a survey of the non-random structure of neural connectivity, primarily at the large scale of regions and pathways in the mammalian cerebral cortex. In addition, we will discuss how non-random connections can give rise to differentiated and complex patterns of dynamics and information flow. Finally, we will explore the idea that at least some disorders of the nervous system are associated with increased randomness of neural connections.
Mapping Information Flow in Sensorimotor Networks
Max Lungarella ,Olaf Sporns
PLOS Computational Biology , 2006, DOI: 10.1371/journal.pcbi.0020144
Abstract: Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior.
Measuring information integration
Giulio Tononi, Olaf Sporns
BMC Neuroscience , 2003, DOI: 10.1186/1471-2202-4-31
Abstract: The capacity to integrate information, or Φ, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Φ is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration).Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks.A standard concern of communication theory is assessing information transmission between a sender and a receiver through a channel [1,2]. Such an approach has been successfully employed in many areas, including neuroscience [3]. For example, by estimating the probability distribution of sensory inputs, one can show that peripheral sensory pathways are well suited to transmitting information to the central nervous system [4]. When considering the central nervous system itself, however, we face the issue of information integration [5]. In such a distributed network, any combination of neural elements can be viewed as senders or receivers. Moreover, the goal is not just to transmit information, but rather to combine many sources of information within the network to obtain a unified picture of the environment and control behavior in a coherent manner [6,7]. Thus, while a neural system composed of a set of parallel, independent channels or
Weight-conserving characterization of complex functional brain networks
Mikail Rubinov,Olaf Sporns
Quantitative Biology , 2011, DOI: 10.1016/j.neuroimage.2011.03.069
Abstract: Complex functional brain networks are large networks of brain regions and functional brain connections. Statistical characterizations of these networks aim to quantify global and local properties of brain activity with a small number of network measures. Important functional network measures include measures of modularity (measures of the goodness with which a network is optimally partitioned into functional subgroups) and measures of centrality (measures of the functional influence of individual brain regions). Characterizations of functional networks are increasing in popularity, but are associated with several important methodological problems. These problems include the inability to characterize densely connected and weighted functional networks, the neglect of degenerate topologically distinct high-modularity partitions of these networks, and the absence of a network null model for testing hypotheses of association between observed nontrivial network properties and simple weighted connectivity properties. In this study we describe a set of methods to overcome these problems. Specifically, we generalize measures of modularity and centrality to fully connected and weighted complex networks, describe the detection of degenerate high-modularity partitions of these networks, and introduce a weighted-connectivity null model of these networks. We illustrate our methods by demonstrating degenerate high-modularity partitions and strong correlations between two complementary measures of centrality in resting-state functional magnetic resonance imaging (MRI) networks from the 1000 Functional Connectomes Project, an open-access repository of resting-state functional MRI datasets. Our methods may allow more sound and reliable characterizations and comparisons of functional brain networks across conditions and subjects.
Motifs in Brain Networks
Olaf Sporns,Rolf K?tter
PLOS Biology , 2012, DOI: 10.1371/journal.pbio.0020369
Abstract: Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.
Motifs in Brain Networks
Olaf Sporns ,Rolf K?tter
PLOS Biology , 2004, DOI: 10.1371/journal.pbio.0020369
Abstract: Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.
The Human Connectome: A Structural Description of the Human Brain
Olaf Sporns ,Giulio Tononi,Rolf K?tter
PLOS Computational Biology , 2005, DOI: 10.1371/journal.pcbi.0010042
Abstract: The connection matrix of the human brain (the human “connectome”) represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.
The human connectome: a structural description of the human brain.
Sporns Olaf,Tononi Giulio,K?tter Rolf
PLOS Computational Biology , 2005,
Abstract: The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.
Structured thalamocortical connectivity revealed by random walks on complex networks
Luciano da Fontoura Costa,Olaf Sporns
Physics , 2006,
Abstract: The segregated regions of the mammalian cerebral cortex and thalamus form an extensive and complex network, whose structure and function are still only incompletely understood. The present article describes an application of the concepts of complex networks and random walks that allows the identification of non-random, highly structured features of thalamocortical connections, and their potential effects on dynamic interactions between cortical areas in the cat brain. Utilizing large-scale anatomical data sets of this thalamocortical system, we investigate uniform random walks in such a network by considering the steady state eigenvector of the respective stochastic matrix. It is shown that thalamocortical connections are organized in such a way as to guarantee strong correlation between the outdegree and occupancy rate (a stochastic measure potentially related to activation) of each cortical area. Possible organizational principles underlying this effect are identified and discussed.
Diversity of Cortical States at Non-Equilibrium Simulated by the Ferromagnetic Ising Model Under Metropolis Dynamics
Luciano da Fontoura Costa,Olaf Sporns
Physics , 2006,
Abstract: This article investigates the relationship between the interconnectivity and simulated dynamics of the thalamocortical system from the specific perspective of attempting to maximize the diversity of cortical states. This is achieved by designing the dynamics such that they favor opposing activity between adjacent regions, thus promoting dynamic diversity while avoiding widespread activation or de-activation. The anti-ferromagnetic Ising model with Metropolis dynamics is adopted and applied to four variations of the large-scale connectivity of the cat thalamocortical system: (a) considering only cortical regions and connections; (b) considering the entire thalamocortical system; (c) the same as in (b) but with the thalamic connections rewired so as to maintain the statistics of node degree and node degree correlations; and (d) as in (b) but with attenuated weights of the connections between cortical and thalamic nodes. A series of interesting findings are obtained, including the identification of specific substructures revealed by correlations between the activity of adjacent regions in case (a) and a pronounced effect of the thalamic connections in splitting the thalamocortical regions into two large groups of nearly homogenous opposite activation (i.e. cortical regions and thalamic nuclei, respectively) in cases (b) and (c). The latter effect is due to the existence of dense connections between cortical and thalamic regions and the lack of interconnectivity between the latter. Another interesting result regarding case (d) is the fact that the pattern of thalamic correlations tended to mirror that of the cortical regions. The possibility to control the level of correlation between the cortical regions by varying the strength of thalamocortical connections is also identified and discussed.
Page 1 /1313
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.