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Sample records for cortical network model

  1. Modelling Human Cortical Network in Real Brain Space

    Institute of Scientific and Technical Information of China (English)

    ZHAO Qing-Bai; FENG Hong-Bo; TANG Yi-Yuan

    2007-01-01

    Highly specific structural organization is of great significance in the topology of cortical networks.We introduce a human cortical network model.taking the specific cortical structure into account,in which nodes are brain sites placed in the actual positions of cerebral cortex and the establishment of edges depends on the spatial path length rather than the linear distance.The resulting network exhibits the essential features of cortical connectivity,properties of small-world networks and multiple clusters structure.Additionally.assortative mixing is also found in this roodel.All of these findings may be attributed to the spedtic cortical architecture.

  2. Modeling of cortical signals using echo state networks

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    Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

    2009-10-01

    Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.

  3. Columnar architecture improves noise robustness in a model cortical network.

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    Paul C Bush

    Full Text Available Cortical columnar architecture was discovered decades ago yet there is no agreed upon explanation for its function. Indeed, some have suggested that it has no function, it is simply an epiphenomenon of developmental processes. To investigate this problem we have constructed a computer model of one square millimeter of layer 2/3 of the primary visual cortex (V1 of the cat. Model cells are connected according to data from recent paired cell studies, in particular the connection probability between pyramidal cells is inversely proportional both to the distance separating the cells and to the distance between the preferred parameters (features of the cells. We find that these constraints, together with a columnar architecture, produce more tightly clustered populations of cells when compared to the random architecture seen in, for example, rodents. This causes the columnar network to converge more quickly and accurately on the pattern representing a particular stimulus in the presence of noise, suggesting that columnar connectivity functions to improve pattern recognition in cortical circuits. The model also suggests that synaptic failure, a phenomenon exhibited by weak synapses, may conserve metabolic resources by reducing transmitter release at these connections that do not contribute to network function.

  4. High-conductance states in a mean-field cortical network model

    DEFF Research Database (Denmark)

    Lerchner, Alexander; Ahmadi, Mandana; Hertz, John

    2004-01-01

    cortical network model with random connectivity and conductance-based synapses. We employ mean-field theory with correctly colored noise to describe temporal correlations in the neuronal activity. Our results illuminate the connection between two independent experimental findings: high-conductance states......Measured responses from visual cortical neurons show that spike times tend to be correlated rather than exactly Poisson distributed. Fano factors vary and are usually greater than 1, indicating a tendency toward spikes being clustered. We show that this behavior emerges naturally in a balanced...... of cortical neurons in their natural environment, and variable non-Poissonian spike statistics with Fano factors greater than 1. (C) 2004 Elsevier B.V. All rights reserved....

  5. Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses.

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    Maxwell R Bennett

    Full Text Available Measurements of blood oxygenation level dependent (BOLD signals have produced some surprising observations. One is that their amplitude is proportional to the entire activity in a region of interest and not just the fluctuations in this activity. Another is that during sleep and anesthesia the average BOLD correlations between regions of interest decline as the activity declines. Mechanistic explanations of these phenomena are described here using a cortical network model consisting of modules with excitatory and inhibitory neurons, taken as regions of cortical interest, each receiving excitatory inputs from outside the network, taken as subcortical driving inputs in addition to extrinsic (intermodular connections, such as provided by associational fibers. The model shows that the standard deviation of the firing rate is proportional to the mean frequency of the firing when the extrinsic connections are decreased, so that the mean BOLD signal is proportional to both as is observed experimentally. The model also shows that if these extrinsic connections are decreased or the frequency of firing reaching the network from the subcortical driving inputs is decreased, or both decline, there is a decrease in the mean firing rate in the modules accompanied by decreases in the mean BOLD correlations between the modules, consistent with the observed changes during NREM sleep and under anesthesia. Finally, the model explains why a transient increase in the BOLD signal in a cortical area, due to a transient subcortical input, gives rises to responses throughout the cortex as observed, with these responses mediated by the extrinsic (intermodular connections.

  6. Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses.

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    Bennett, Maxwell R; Farnell, Les; Gibson, William G; Lagopoulos, Jim

    2015-01-01

    Measurements of blood oxygenation level dependent (BOLD) signals have produced some surprising observations. One is that their amplitude is proportional to the entire activity in a region of interest and not just the fluctuations in this activity. Another is that during sleep and anesthesia the average BOLD correlations between regions of interest decline as the activity declines. Mechanistic explanations of these phenomena are described here using a cortical network model consisting of modules with excitatory and inhibitory neurons, taken as regions of cortical interest, each receiving excitatory inputs from outside the network, taken as subcortical driving inputs in addition to extrinsic (intermodular) connections, such as provided by associational fibers. The model shows that the standard deviation of the firing rate is proportional to the mean frequency of the firing when the extrinsic connections are decreased, so that the mean BOLD signal is proportional to both as is observed experimentally. The model also shows that if these extrinsic connections are decreased or the frequency of firing reaching the network from the subcortical driving inputs is decreased, or both decline, there is a decrease in the mean firing rate in the modules accompanied by decreases in the mean BOLD correlations between the modules, consistent with the observed changes during NREM sleep and under anesthesia. Finally, the model explains why a transient increase in the BOLD signal in a cortical area, due to a transient subcortical input, gives rises to responses throughout the cortex as observed, with these responses mediated by the extrinsic (intermodular) connections.

  7. Cortical information flow in Parkinson's disease: a composite network/field model

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    Cliff C. Kerr

    2013-04-01

    Full Text Available The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD. Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power towards lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality in the beta and low gamma bands between cortical layers, but this was largely absent in the PD model. In particular, the reduction in Granger causality from the main "input" layer of the cortex (layer 4 to the main "output" layer (layer 5 was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than

  8. Cross-correlations in high-conductance states of a model cortical network

    DEFF Research Database (Denmark)

    Hertz, John

    2010-01-01

    (dansk abstrakt findes ikke) Neuronal firing correlations are studied using simulations of a simple network model for a cortical column in a high-conductance state with dynamically balanced excitation and inhibition.  Although correlations between individual pairs of neurons exhibit considerable...... heterogeneity, population averages show systematic behavior. When the network is in a stationary state, the average correlations are generically small: correlation coefficients are of order 1/N, where N is the number of neurons in the network. However, when the input to the network varies strongly in time, much...... larger values are found. In this situation, the network is out of balance, and the synaptic conductance is low, at times when the strongest firing occurs.  However, examination of the correlation functions of synaptic currents reveals that after these bursts, balance is restored within a few ms...

  9. A GPU-accelerated cortical neural network model for visually guided robot navigation.

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    Beyeler, Michael; Oros, Nicolas; Dutt, Nikil; Krichmar, Jeffrey L

    2015-12-01

    Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls.

  10. Neural network models for spatial data mining, map production, and cortical direction selectivity

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    Parsons, Olga

    A family of ARTMAP neural networks for incremental supervised learning has been developed over the last decade. The Sensor Exploitation Group of MIT Lincoln Laboratory (LL) has incorporated an early version of this network as the recognition engine of a hierarchical system for fusion and data mining of multiple registered geospatial images. The LL system has been successfully fielded, but it is limited to target vs. non-target identifications and does not produce whole maps. This dissertation expands the capabilities of the LL system so that it learns to identify arbitrarily many target classes at once and can thus produce a whole map. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of a consistent procedure and a benchmark testbed has permitted the evaluation of candidate recognition networks as well as pre- and post-processing and feature extraction options. The resulting default ARTMAP network and mapping methodology set a standard for a variety of related mapping problems and application domains. The second part of the dissertation investigates the development of cortical direction selectivity. The possible role of visual experience and oculomotor behavior in the maturation of cells in the primary visual cortex is studied. The responses of neurons in the thalamus and cortex of the cat are modeled when natural scenes are scanned by several types of eye movements. Inspired by the Hebbian-like synaptic plasticity, which is based upon correlations between cell activations, the second-order statistical structure of thalamo-cortical activity is examined. In the simulations, patterns of neural activity that lead to a correct refinement of cell responses are observed during visual fixation, when small ocular movements occur, but are not observed in the presence of large saccades. Simulations also replicate experiments in which kittens are reared under stroboscopic

  11. Spatial learning and action planning in a prefrontal cortical network model.

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    Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

    2011-05-01

    The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive "insight" capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates.

  12. Spatial learning and action planning in a prefrontal cortical network model.

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    Louis-Emmanuel Martinet

    2011-05-01

    Full Text Available The interplay between hippocampus and prefrontal cortex (PFC is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive "insight" capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates.

  13. Mean field methods for cortical network dynamics

    DEFF Research Database (Denmark)

    Hertz, J.; Lerchner, Alexander; Ahmadi, M.

    2004-01-01

    We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases with the stren...... cortex. Finally, an extension of the model to describe an orientation hypercolumn provides understanding of how cortical interactions sharpen orientation tuning, in a way that is consistent with observed firing statistics...

  14. Face activated neurodynamic cortical networks.

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    Susac, Ana; Ilmoniemi, Risto J; Ranken, Doug; Supek, Selma

    2011-05-01

    Previous neuroimaging studies have shown that complex visual stimuli, such as faces, activate multiple brain regions, yet little is known on the dynamics and complexity of the activated cortical networks during the entire measurable evoked response. In this study, we used simulated and face-evoked empirical MEG data from an oddball study to investigate the feasibility of accurate, efficient, and reliable spatio-temporal tracking of cortical pathways over prolonged time intervals. We applied a data-driven, semiautomated approach to spatio-temporal source localization with no prior assumptions on active cortical regions to explore non-invasively face-processing dynamics and their modulation by task. Simulations demonstrated that the use of multi-start downhill simplex and data-driven selections of time intervals submitted to the Calibrated Start Spatio-Temporal (CSST) algorithm resulted in improved accuracy of the source localization and the estimation of the onset of their activity. Locations and dynamics of the identified sources indicated a distributed cortical network involved in face processing whose complexity was task dependent. This MEG study provided the first non-invasive demonstration, agreeing with intracranial recordings, of an early onset of the activity in the fusiform face gyrus (FFG), and that frontal activation preceded parietal for responses elicited by target faces.

  15. Analysis of Cortical Flow Models In Vivo

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    Benink, Hélène A.; Mandato, Craig A.; Bement, William M.

    2000-01-01

    Cortical flow, the directed movement of cortical F-actin and cortical organelles, is a basic cellular motility process. Microtubules are thought to somehow direct cortical flow, but whether they do so by stimulating or inhibiting contraction of the cortical actin cytoskeleton is the subject of debate. Treatment of Xenopus oocytes with phorbol 12-myristate 13-acetate (PMA) triggers cortical flow toward the animal pole of the oocyte; this flow is suppressed by microtubules. To determine how this suppression occurs and whether it can control the direction of cortical flow, oocytes were subjected to localized manipulation of either the contractile stimulus (PMA) or microtubules. Localized PMA application resulted in redirection of cortical flow toward the site of application, as judged by movement of cortical pigment granules, cortical F-actin, and cortical myosin-2A. Such redirected flow was accelerated by microtubule depolymerization, showing that the suppression of cortical flow by microtubules is independent of the direction of flow. Direct observation of cortical F-actin by time-lapse confocal analysis in combination with photobleaching showed that cortical flow is driven by contraction of the cortical F-actin network and that microtubules suppress this contraction. The oocyte germinal vesicle serves as a microtubule organizing center in Xenopus oocytes; experimental displacement of the germinal vesicle toward the animal pole resulted in localized flow away from the animal pole. The results show that 1) cortical flow is directed toward areas of localized contraction of the cortical F-actin cytoskeleton; 2) microtubules suppress cortical flow by inhibiting contraction of the cortical F-actin cytoskeleton; and 3) localized, microtubule-dependent suppression of actomyosin-based contraction can control the direction of cortical flow. We discuss these findings in light of current models of cortical flow. PMID:10930453

  16. The influence of ion concentrations on the dynamic behavior of the Hodgkin-Huxley model-based cortical network.

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    Tagluk, M Emin; Tekin, Ramazan

    2014-08-01

    Action potentials (APs) in the form of very short pulses arise when the cell is excited by any internal or external stimulus exceeding the critical threshold of the membrane. During AP generation, the membrane potential completes its natural cycle through typical phases that can be formatted by ion channels, gates and ion concentrations, as well as the synaptic excitation rate. On the basis of the Hodgkin-Huxley cell model, a cortical network consistent with the real anatomic structure is realized with randomly interrelated small population of neurons to simulate a cerebral cortex segment. Using this model, we investigated the effects of Na(+) and K(+) ion concentrations on the outcome of this network in terms of regularity, phase locking, and synchronization. The results suggested that Na(+) concentration does slightly affect the amplitude but not considerably affects the other parameters specified by depolarization and repolarization. K(+) concentration significantly influences the form, regularity, and synchrony of the network-generated APs. No previous study dealing directly with the effects of both Na(+) and K(+) ion concentrations on regularity and synchronization of the simulated cortical network-generated APs, allowing for the comparison of results obtained using our methods, was encountered in the literature. The results, however, were consistent with those obtained through studies concerning resonance and synchronization from another perspective and with the information revealed through physiological and pharmacological experiments concerning changing ion concentrations or blocking ion channels. Our results demonstrated that the regularity and reliability of brain functions have a strong relationship with cellular ion concentrations, and suggested the management of the dynamic behavior of the cellular network with ion concentrations.

  17. Mean field methods for cortical network dynamics

    DEFF Research Database (Denmark)

    Hertz, J.; Lerchner, Alexander; Ahmadi, M.

    2004-01-01

    We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...

  18. The statistics of repeating patterns of cortical activity can be reproduced by a model network of stochastic binary neurons.

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    Roxin, Alex; Hakim, Vincent; Brunel, Nicolas

    2008-10-15

    Calcium imaging of the spontaneous activity in cortical slices has revealed repeating spatiotemporal patterns of transitions between so-called down states and up states (Ikegaya et al., 2004). Here we fit a model network of stochastic binary neurons to data from these experiments, and in doing so reproduce the distributions of such patterns. We use two versions of this model: (1) an unconnected network in which neurons are activated as independent Poisson processes; and (2) a network with an interaction matrix, estimated from the data, representing effective interactions between the neurons. The unconnected model (model 1) is sufficient to account for the statistics of repeating patterns in 11 of the 15 datasets studied. Model 2, with interactions between neurons, is required to account for pattern statistics of the remaining four. Three of these four datasets are the ones that contain the largest number of transitions, suggesting that long datasets are in general necessary to render interactions statistically visible. We then study the topology of the matrix of interactions estimated for these four datasets. For three of the four datasets, we find sparse matrices with long-tailed degree distributions and an overrepresentation of certain network motifs. The remaining dataset exhibits a strongly interconnected, spatially localized subgroup of neurons. In all cases, we find that interactions between neurons facilitate the generation of long patterns that do not repeat exactly.

  19. Improved dimensionally-reduced visual cortical network using stochastic noise modeling.

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    Tao, Louis; Praissman, Jeremy; Sornborger, Andrew T

    2012-04-01

    In this paper, we extend our framework for constructing low-dimensional dynamical system models of large-scale neuronal networks of mammalian primary visual cortex. Our dimensional reduction procedure consists of performing a suitable linear change of variables and then systematically truncating the new set of equations. The extended framework includes modeling the effect of neglected modes as a stochastic process. By parametrizing and including stochasticity in one of two ways we show that we can improve the systems-level characterization of our dimensionally reduced neuronal network model. We examined orientation selectivity maps calculated from the firing rate distribution of large-scale simulations and stochastic dimensionally reduced models and found that by using stochastic processes to model the neglected modes, we were able to better reproduce the mean and variance of firing rates in the original large-scale simulations while still accurately predicting the orientation preference distribution.

  20. Relating Cortical Atrophy in Temporal Lobe Epilepsy with Graph Diffusion-Based Network Models

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    Abdelnour, Farras; Mueller, Susanne; Raj, Ashish

    2015-01-01

    Mesial temporal lobe epilepsy (TLE) is characterized by stereotyped origination and spread pattern of epileptogenic activity, which is reflected in stereotyped topographic distribution of neuronal atrophy on magnetic resonance imaging (MRI). Both epileptogenic activity and atrophy spread appear to follow white matter connections. We model the networked spread of activity and atrophy in TLE from first principles via two simple first order network diffusion models. Atrophy distribution is modeled as a simple consequence of the propagation of epileptogenic activity in one model, and as a progressive degenerative process in the other. We show that the network models closely reproduce the regional volumetric gray matter atrophy distribution of two epilepsy cohorts: 29 TLE subjects with medial temporal sclerosis (TLE-MTS), and 50 TLE subjects with normal appearance on MRI (TLE-no). Statistical validation at the group level suggests high correlation with measured atrophy (R = 0.586 for TLE-MTS, R = 0.283 for TLE-no). We conclude that atrophy spread model out-performs the hyperactivity spread model. These results pave the way for future clinical application of the proposed model on individual patients, including estimating future spread of atrophy, identification of seizure onset zones and surgical planning. PMID:26513579

  1. Relating Cortical Atrophy in Temporal Lobe Epilepsy with Graph Diffusion-Based Network Models.

    Science.gov (United States)

    Abdelnour, Farras; Mueller, Susanne; Raj, Ashish

    2015-10-01

    Mesial temporal lobe epilepsy (TLE) is characterized by stereotyped origination and spread pattern of epileptogenic activity, which is reflected in stereotyped topographic distribution of neuronal atrophy on magnetic resonance imaging (MRI). Both epileptogenic activity and atrophy spread appear to follow white matter connections. We model the networked spread of activity and atrophy in TLE from first principles via two simple first order network diffusion models. Atrophy distribution is modeled as a simple consequence of the propagation of epileptogenic activity in one model, and as a progressive degenerative process in the other. We show that the network models closely reproduce the regional volumetric gray matter atrophy distribution of two epilepsy cohorts: 29 TLE subjects with medial temporal sclerosis (TLE-MTS), and 50 TLE subjects with normal appearance on MRI (TLE-no). Statistical validation at the group level suggests high correlation with measured atrophy (R = 0.586 for TLE-MTS, R = 0.283 for TLE-no). We conclude that atrophy spread model out-performs the hyperactivity spread model. These results pave the way for future clinical application of the proposed model on individual patients, including estimating future spread of atrophy, identification of seizure onset zones and surgical planning.

  2. A cortical network model of cognitive and emotional influences in human decision making.

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    Nazir, Azadeh Hassannejad; Liljenström, Hans

    2015-10-01

    Decision making (DM)(2) is a complex process that appears to involve several brain structures. In particular, amygdala, orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) seem to be essential in human decision making, where both emotional and cognitive aspects are taken into account. In this paper, we present a computational network model representing the neural information processing of DM, from perception to behavior. We model the population dynamics of the three neural structures (amygdala, OFC and LPFC), as well as their interaction. In our model, the neurodynamic activity of amygdala and OFC represents the neural correlates of secondary emotion, while the activity of certain neural populations in OFC alone represents the outcome expectancy of different options. The cognitive/rational aspect of DM is associated with LPFC. Our model is intended to give insights on the emotional and cognitive processes involved in DM under various internal and external contexts. Different options for actions are represented by the oscillatory activity of cell assemblies, which may change due to experience and learning. Knowledge and experience of the outcome of our decisions and actions can eventually result in changes in our neural structures, attitudes and behaviors. Simulation results may have implications for how we make decisions for our individual actions, as well as for societal choices, where we take examples from transport and its impact on CO2 emissions and climate change.

  3. Modeling cortical circuits.

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    Rohrer, Brandon Robinson; Rothganger, Fredrick H.; Verzi, Stephen J.; Xavier, Patrick Gordon

    2010-09-01

    The neocortex is perhaps the highest region of the human brain, where audio and visual perception takes place along with many important cognitive functions. An important research goal is to describe the mechanisms implemented by the neocortex. There is an apparent regularity in the structure of the neocortex [Brodmann 1909, Mountcastle 1957] which may help simplify this task. The work reported here addresses the problem of how to describe the putative repeated units ('cortical circuits') in a manner that is easily understood and manipulated, with the long-term goal of developing a mathematical and algorithmic description of their function. The approach is to reduce each algorithm to an enhanced perceptron-like structure and describe its computation using difference equations. We organize this algorithmic processing into larger structures based on physiological observations, and implement key modeling concepts in software which runs on parallel computing hardware.

  4. Computational model of thalamo-cortical networks: dynamical control of alpha rhythms in relation to focal attention.

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    Suffczynski, P; Kalitzin, S; Pfurtscheller, G; Lopes da Silva, F H

    2001-12-01

    EEG/MEG rhythmic activities such as alpha rhythms, of the visual or of the somato-sensory cortex, are commonly modulated as subjects perform certain tasks or react to specific stimuli. In general, these activities change depending on extrinsic or intrinsic events. A decrease of the amplitude of alpha rhythmic activity occurring after a given event, which manifests as a decrease of a spectral peak, is called event-related desynchronization (ERD), whereas the inverse is called event-related synchronization (ERS), since it is assumed that the power of a spectral peak is related to the degree of synchrony of the underlying oscillating neuronal populations. An intriguing observation in this respect [Pfurtscheller and Neuper, Neurosci. Lett. 174 (1994) 93-96] was that ERD of alpha rhythms recorded over the central areas was accompanied by ERS, within the same frequency band, recorded over neighboring areas. In case the event was a hand movement, ERD was recorded over the scalp overlying the hand cortical area, whereas ERS was concomitantly recorded over the midline, whereas if the movement was of the foot the opposite was found. We called this phenomenon 'focal ERD/surround ERS'. The question of how this phenomenon may be generated was approached by means of a computational model of thalamo-cortical networks, that incorporates basic properties of neurons and synaptic interactions. These simulation studies revealed that this antagonistic ERD/ERS phenomenon depends on the functional interaction between the populations of thalamo-cortical cells (TCR) and reticular nucleus cells (RE) and on how this interaction is modulated by cholinergic inputs. An essential feature of this interaction is the existence of cross-talk between different sectors of RE that correspond to distinct sensory modules (e.g. hand, foot). These observations led us to formulate the hypothesis that this basic neurophysiological mechanism can account for the general observation that enhanced attention

  5. The brain's router: a cortical network model of serial processing in the primate brain.

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    Zylberberg, Ariel; Fernández Slezak, Diego; Roelfsema, Pieter R; Dehaene, Stanislas; Sigman, Mariano

    2010-04-29

    The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100-500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a "router" network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates.

  6. Influence of Slow Oscillation on Hippocampal Activity and Ripples Through Cortico-Hippocampal Synaptic Interactions, Analyzed by a Cortical-CA3-CA1 Network Model

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    Jiannis eTaxidis

    2013-02-01

    Full Text Available Hippocampal sharp wave-ripple complexes (SWRs involve the synchronous discharge of thousands of cells throughout the CA3-CA1-subiculum-entorhinal cortex axis. Their strong transient output affects cortical targets, rendering SWRs a possible means for memory transfer from the hippocampus to the neocortex for long-term storage. Neurophysiological observations of hippocampal activity modulation by the cortical slow oscillation (SO during deep sleep and anesthesia, and correlations between ripples and UP states, support the role of SWRs in memory consolidation through a cortico-hippocampal feedback loop. We couple a cortical network exhibiting SO with a hippocampal CA3-CA1 computational network model exhibiting SWRs, in order to model such cortico-hippocampal correlations and uncover important parameters and coupling mechanisms controlling them. The cortical oscillatory output entrains the CA3 network via connections representing the mossy fiber input, and the CA1 network via the temporoammonic pathway. The spiking activity in CA3 and CA1 is shown to depend on the excitation-to-inhibition ratio, induced by combining the two hippocampal inputs, with mossy fiber input controlling the UP-state correlation of CA3 population bursts and corresponding SWRs, whereas the temporoammonic input affects the overall CA1 spiking activity. Ripple characteristics and pyramidal spiking participation to SWRs are shaped by the strength of the Schaffer collateral drive. A set of in vivo recordings from the rat hippocampus confirms a model-predicted segregation of pyramidal cells into subgroups according to the SO state where they preferentially fire and their response to SWRs. These groups can potentially play distinct functional roles in the replay of spike sequences.

  7. The ontogeny of the cortical language network.

    Science.gov (United States)

    Skeide, Michael A; Friederici, Angela D

    2016-05-01

    Language-processing functions follow heterogeneous developmental trajectories. The human embryo can already distinguish vowels in utero, but grammatical complexity is usually not fully mastered until at least 7 years of age. Examining the current literature, we propose that the ontogeny of the cortical language network can be roughly subdivided into two main developmental stages. In the first stage extending over the first 3 years of life, the infant rapidly acquires bottom-up processing capacities, which are primarily implemented bilaterally in the temporal cortices. In the second stage continuing into adolescence, top-down processes emerge gradually with the increasing functional selectivity and structural connectivity of the left inferior frontal cortex.

  8. Self-organized two-state membrane potential transitions in a network of realistically modeled cortical neurons.

    Science.gov (United States)

    Kang, Siu; Kitano, Katsunori; Fukai, Tomoki

    2004-04-01

    Recent studies have revealed that in vivo cortical neurons show spontaneous transitions between two subthreshold levels of the membrane potentials, 'up' and 'down' states. The neural mechanism of generating those spontaneous states transitions, however, remains unclear. Recent electrophysiological studies have suggested that those state transitions may occur through activation of a hyperpolarization-activated cation current (H-current), possibly by inhibitory synaptic inputs. Here, we demonstrate that two-state membrane potential fluctuations similar to those exhibited by in vivo neurons can be generated through a spike-timing-dependent self-organizing process in a network of inhibitory neurons and excitatory neurons expressing the H-current.

  9. Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation.

    Directory of Open Access Journals (Sweden)

    Salvador Dura-Bernal

    Full Text Available Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance. Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom

  10. Network bursts in cortical neuronal cultures: 'noise - versus pacemaker'- driven neural network simulations

    NARCIS (Netherlands)

    Gritsun, T.; Stegenga, J.; le Feber, Jakob; Rutten, Wim

    2009-01-01

    In this paper we address the issue of spontaneous bursting activity in cortical neuronal cultures and explain what might cause this collective behavior using computer simulations of two different neural network models. While the common approach to acivate a passive network is done by introducing

  11. Serotonin modulation of cortical neurons and networks

    Directory of Open Access Journals (Sweden)

    Pau eCelada

    2013-04-01

    Full Text Available The serotonergic pathways originating in the dorsal and median raphe nuclei (DR and MnR, respectively are critically involved in cortical function. Serotonin (5-HT, acting on postsynaptic and presynaptic receptors, is involved in cognition, mood, impulse control and motor functions by 1 modulating the activity of different neuronal types, and 2 varying the release of other neurotransmitters, such as glutamate, GABA, acetylcholine and dopamine. Also, 5-HT seems to play an important role in cortical development. Of all cortical regions, the frontal lobe is the area most enriched in serotonergic axons and 5-HT receptors. 5-HT and selective receptor agonists modulate the excitability of cortical neurons and their discharge rate through the activation of several receptor subtypes, of which the 5-HT1A, 5-HT1B, 5-HT2A and 5-HT3 subtypes play a major role. Little is known, however, on the role of other excitatory receptors moderately expressed in cortical areas, such as 5-HT2C, 5-HT4, 5-HT6 and 5-HT7. In vitro and in vivo studies suggest that 5-HT1A and 5-HT2A receptors are key players and exert opposite effects on the activity of pyramidal neurons in the medial prefrontal cortex (mPFC. The activation of 5-HT1A receptors in mPFC hyperpolarizes pyramidal neurons whereas that of 5-HT2A receptors results in neuronal depolarization, reduction of the afterhyperpolarization and increase of excitatory postsynaptic currents (EPSCs and of discharge rate. 5-HT can also stimulate excitatory (5-HT2A and 5-HT3 and inhibitory (5-HT1A receptors in GABA interneurons to modulate synaptic GABA inputs onto pyramidal neurons. Likewise, the pharmacological manipulation of various 5-HT receptors alters oscillatory activity in PFC, suggesting that 5-HT is also involved in the control of cortical network activity. A better understanding of the actions of 5-HT in PFC may help to develop treatments for mood and cognitive disorders associated with an abnormal function of the

  12. Characterization of Early Cortical Neural Network ...

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to

  13. The cortical and sub-cortical network of sensory evoked response in healthy subjects.

    Science.gov (United States)

    Muthuraman, M; Hellriegel, H; Groppa, S; Deuschl, G; Raethjen, J

    2013-01-01

    The aim of this study was to find the cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects during electrical stimulation of right median nerve at wrist. The multitaper method was used to estimate the power and coherence spectrum followed by the source analysis method dynamic imaging of coherent sources (DICS) to find the highest coherent source for the basic frequency 3 Hz and the complete cortical and sub-cortical network responsible for the sensory evoked coherence in healthy subjects. The highest coherent source for the basic frequency was in the posterior parietal cortex for all the subjects. The cortical and sub-cortical network comprised of the primary sensory motor cortex (SI), secondary sensory motor cortex (SII), frontal cortex and medial pulvinar nucleus in the thalamus. The cortical and sub-cortical network responsible for the sensory evoked coherence was found successfully with a 64-channel EEG system. The sensory evoked coherence is involved with a thalamo-cortical network in healthy subjects.

  14. Stable propagation of synchronous spiking in cortical neural networks

    Science.gov (United States)

    Diesmann, Markus; Gewaltig, Marc-Oliver; Aertsen, Ad

    1999-12-01

    The classical view of neural coding has emphasized the importance of information carried by the rate at which neurons discharge action potentials. More recent proposals that information may be carried by precise spike timing have been challenged by the assumption that these neurons operate in a noisy fashion-presumably reflecting fluctuations in synaptic input-and, thus, incapable of transmitting signals with millisecond fidelity. Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.

  15. Glutamate-bound NMDARs arising from in vivo-like network activity extend spatio-temporal integration in a L5 cortical pyramidal cell model.

    Directory of Open Access Journals (Sweden)

    Matteo Farinella

    2014-04-01

    Full Text Available In vivo, cortical pyramidal cells are bombarded by asynchronous synaptic input arising from ongoing network activity. However, little is known about how such 'background' synaptic input interacts with nonlinear dendritic mechanisms. We have modified an existing model of a layer 5 (L5 pyramidal cell to explore how dendritic integration in the apical dendritic tuft could be altered by the levels of network activity observed in vivo. Here we show that asynchronous background excitatory input increases neuronal gain and extends both temporal and spatial integration of stimulus-evoked synaptic input onto the dendritic tuft. Addition of fast and slow inhibitory synaptic conductances, with properties similar to those from dendritic targeting interneurons, that provided a 'balanced' background configuration, partially counteracted these effects, suggesting that inhibition can tune spatio-temporal integration in the tuft. Excitatory background input lowered the threshold for NMDA receptor-mediated dendritic spikes, extended their duration and increased the probability of additional regenerative events occurring in neighbouring branches. These effects were also observed in a passive model where all the non-synaptic voltage-gated conductances were removed. Our results show that glutamate-bound NMDA receptors arising from ongoing network activity can provide a powerful spatially distributed nonlinear dendritic conductance. This may enable L5 pyramidal cells to change their integrative properties as a function of local network activity, potentially allowing both clustered and spatially distributed synaptic inputs to be integrated over extended timescales.

  16. Cortical Network for Reading Linear Words in an Alphasyllabary

    Science.gov (United States)

    Das, Tanusree; Bapi, Raju S.; Padakannaya, Prakash; Singh, Nandini C.

    2011-01-01

    Functional imaging studies have established cortical networks for reading alphabetic, syllabic and logographic scripts. There is little information about the different cortical areas that participate in reading an alphasyllabary. We use functional brain imaging to study the reading network for Devanagari, an alphasyllabary. Similar to syllabic…

  17. Lateral thinking, from the Hopfield model to cortical dynamics.

    Science.gov (United States)

    Akrami, Athena; Russo, Eleonora; Treves, Alessandro

    2012-01-24

    Self-organizing attractor networks may comprise the building blocks for cortical dynamics, providing the basic operations of categorization, including analog-to-digital conversion, association and auto-association, which are then expressed as components of distinct cognitive functions depending on the contents of the neural codes in each region. To assess the viability of this scenario, we first review how a local cortical patch may be modeled as an attractor network, in which memory representations are not artificially stored as prescribed binary patterns of activity as in the Hopfield model, but self-organize as continuously graded patterns induced by afferent input. Recordings in macaques indicate that such cortical attractor networks may express retrieval dynamics over cognitively plausible rapid time scales, shorter than those dominated by neuronal fatigue. A cortical network encompassing many local attractor networks, and incorporating a realistic description of adaptation dynamics, may be captured by a Potts model. This network model has the capacity to engage long-range associations into sustained iterative attractor dynamics at a cortical scale, in what may be regarded as a mathematical model of spontaneous lateral thought. This article is part of a Special Issue entitled: Neural Coding.

  18. Magnetoencephalography from signals to dynamic cortical networks

    CERN Document Server

    Aine, Cheryl

    2014-01-01

    "Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...

  19. Effective Suppression of Pathological Synchronization in Cortical Networks by Highly Heterogeneous Distribution of Inhibitory Connections

    Science.gov (United States)

    Kada, Hisashi; Teramae, Jun-Nosuke; Tokuda, Isao T.

    2016-01-01

    Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons. The previous studies, however, have not considered highly non-random features of the synaptic connectivity, namely, bidirectional connections between cortical neurons are more common than expected by chance and strengths of synapses are positively correlated between pre- and postsynaptic neurons. The positive correlation of synaptic connections may destabilize asynchronous activity of networks with the long-tailed synaptic distribution and induce pathological synchronized firing among neurons. It remains unclear how the cortical network avoids such pathological synchronization. Here, we demonstrate that introduction of the correlated connections indeed gives rise to synchronized firings in a cortical network model with the long-tailed distribution. By using a simplified feed-forward network model of spiking neurons, we clarify the underlying mechanism of the synchronization. We then show that the synchronization can be efficiently suppressed by highly heterogeneous distribution, typically a lognormal distribution, of inhibitory-to-excitatory connection strengths in a recurrent network model of cortical neurons. PMID:27803659

  20. In vivo models of cortical acquired epilepsy

    Science.gov (United States)

    Chauvette, Sylvain; Soltani, Sara; Seigneur, Josée; Timofeev, Igor

    2015-01-01

    The neocortex is the site of origin of several forms of acquired epilepsy. Here we provide a brief review of experimental models that were recently developed to study neocortical epileptogenesis as well as some major results obtained with these methods. Most of neocortical seizures appear to be nocturnal and it is known that neuronal activities reveal high levels of synchrony during slow-wave sleep. Therefore, we start the review with a description of mechanisms of neuronal synchronization and major forms of synchronized normal and pathological activities. Then, we describe three experimental models of seizures and epileptogenesis: ketamine-xylazine anesthesia as feline seizure triggered factor, cortical undercut as cortical penetrating wound model and neocortical kindling. Besides specific technical details describing these models we also provide major features of pathological brain activities recorded during epileptogenesis and seizures. The most common feature of all models of neocortical epileptogenesis is the increased duration of network silent states that up-regulates neuronal excitability and eventually leads to epilepsy. PMID:26343530

  1. In vivo models of cortical acquired epilepsy.

    Science.gov (United States)

    Chauvette, Sylvain; Soltani, Sara; Seigneur, Josée; Timofeev, Igor

    2016-02-15

    The neocortex is the site of origin of several forms of acquired epilepsy. Here we provide a brief review of experimental models that were recently developed to study neocortical epileptogenesis as well as some major results obtained with these methods. Most of neocortical seizures appear to be nocturnal and it is known that neuronal activities reveal high levels of synchrony during slow-wave sleep. Therefore, we start the review with a description of mechanisms of neuronal synchronization and major forms of synchronized normal and pathological activities. Then, we describe three experimental models of seizures and epileptogenesis: ketamine-xylazine anesthesia as feline seizure triggered factor, cortical undercut as cortical penetrating wound model and neocortical kindling. Besides specific technical details describing these models we also provide major features of pathological brain activities recorded during epileptogenesis and seizures. The most common feature of all models of neocortical epileptogenesis is the increased duration of network silent states that up-regulates neuronal excitability and eventually leads to epilepsy.

  2. Negative Correlations in Visual Cortical Networks

    National Research Council Canada - National Science Library

    Chelaru, Mircea I; Dragoi, Valentin

    2016-01-01

    .... Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive...

  3. Cortical network reorganization guided by sensory input features.

    Science.gov (United States)

    Kilgard, Michael P; Pandya, Pritesh K; Engineer, Navzer D; Moucha, Raluca

    2002-12-01

    Sensory experience alters the functional organization of cortical networks. Previous studies using behavioral training motivated by aversive or rewarding stimuli have demonstrated that cortical plasticity is specific to salient inputs in the sensory environment. Sensory experience associated with electrical activation of the basal forebrain (BasF) generates similar input specific plasticity. By directly engaging plasticity mechanisms and avoiding extensive behavioral training, BasF stimulation makes it possible to efficiently explore how specific sensory features contribute to cortical plasticity. This review summarizes our observations that cortical networks employ a variety of strategies to improve the representation of the sensory environment. Different combinations of receptive-field, temporal, and spectrotemporal plasticity were generated in primary auditory cortex neurons depending on the pitch, modulation rate, and order of sounds paired with BasF stimulation. Simple tones led to map expansion, while modulated tones altered the maximum cortical following rate. Exposure to complex acoustic sequences led to the development of combination-sensitive responses. This remodeling of cortical response characteristics may reflect changes in intrinsic cellular mechanisms, synaptic efficacy, and local neuronal connectivity. The intricate relationship between the pattern of sensory activation and cortical plasticity suggests that network-level rules alter the functional organization of the cortex to generate the most behaviorally useful representation of the sensory environment.

  4. Slow dynamics and high variability in balanced cortical networks with clustered connections.

    Science.gov (United States)

    Litwin-Kumar, Ashok; Doiron, Brent

    2012-11-01

    Anatomical studies demonstrate that excitatory connections in cortex are not uniformly distributed across a network but instead exhibit clustering into groups of highly connected neurons. The implications of clustering for cortical activity are unclear. We studied the effect of clustered excitatory connections on the dynamics of neuronal networks that exhibited high spike time variability owing to a balance between excitation and inhibition. Even modest clustering substantially changed the behavior of these networks, introducing slow dynamics during which clusters of neurons transiently increased or decreased their firing rate. Consequently, neurons exhibited both fast spiking variability and slow firing rate fluctuations. A simplified model shows how stimuli bias networks toward particular activity states, thereby reducing firing rate variability as observed experimentally in many cortical areas. Our model thus relates cortical architecture to the reported variability in spontaneous and evoked spiking activity.

  5. Processing of Feature Selectivity in Cortical Networks with Specific Connectivity.

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    Full Text Available Although non-specific at the onset of eye opening, networks in rodent visual cortex attain a non-random structure after eye opening, with a specific bias for connections between neurons of similar preferred orientations. As orientation selectivity is already present at eye opening, it remains unclear how this specificity in network wiring contributes to feature selectivity. Using large-scale inhibition-dominated spiking networks as a model, we show that feature-specific connectivity leads to a linear amplification of feedforward tuning, consistent with recent electrophysiological single-neuron recordings in rodent neocortex. Our results show that optimal amplification is achieved at an intermediate regime of specific connectivity. In this configuration a moderate increase of pairwise correlations is observed, consistent with recent experimental findings. Furthermore, we observed that feature-specific connectivity leads to the emergence of orientation-selective reverberating activity, and entails pattern completion in network responses. Our theoretical analysis provides a mechanistic understanding of subnetworks' responses to visual stimuli, and casts light on the regime of operation of sensory cortices in the presence of specific connectivity.

  6. Assessing cortical network properties using TMS-EEG.

    Science.gov (United States)

    Rogasch, Nigel C; Fitzgerald, Paul B

    2013-07-01

    The past decade has seen significant developments in the concurrent use of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to directly assess cortical network properties such as excitability and connectivity in humans. New hardware solutions, improved EEG amplifier technology, and advanced data processing techniques have allowed substantial reduction of the TMS-induced artifact, which had previously rendered concurrent TMS-EEG impossible. Various physiological artifacts resulting from TMS have also been identified, and methods are being developed to either minimize or remove these sources of artifact. With these developments, TMS-EEG has unlocked regions of the cortex to researchers that were previously inaccessible to TMS. By recording the TMS-evoked response directly from the cortex, TMS-EEG provides information on the excitability, effective connectivity, and oscillatory tuning of a given cortical area, removing the need to infer such measurements from indirect measures. In the following review, we investigate the different online and offline methods for reducing artifacts in TMS-EEG recordings and the physiological information contained within the TMS-evoked cortical response. We then address the use of TMS-EEG to assess different cortical mechanisms such as cortical inhibition and neural plasticity, before briefly reviewing studies that have utilized TMS-EEG to explore cortical network properties at rest and during different functional brain states.

  7. Determinants of Multiple Semantic Priming: A Meta-Analysis and Spike Frequency Adaptive Model of a Cortical Network

    Science.gov (United States)

    Lavigne, Frederic; Dumercy, Laurent; Darmon, Nelly

    2011-01-01

    Recall and language comprehension while processing sequences of words involves multiple semantic priming between several related and/or unrelated words. Accounting for multiple and interacting priming effects in terms of underlying neuronal structure and dynamics is a challenge for current models of semantic priming. Further elaboration of current…

  8. Determinants of Multiple Semantic Priming: A Meta-Analysis and Spike Frequency Adaptive Model of a Cortical Network

    Science.gov (United States)

    Lavigne, Frederic; Dumercy, Laurent; Darmon, Nelly

    2011-01-01

    Recall and language comprehension while processing sequences of words involves multiple semantic priming between several related and/or unrelated words. Accounting for multiple and interacting priming effects in terms of underlying neuronal structure and dynamics is a challenge for current models of semantic priming. Further elaboration of current…

  9. Locus coeruleus stimulation recruits a broad cortical neuronal network and increases cortical perfusion.

    Science.gov (United States)

    Toussay, Xavier; Basu, Kaustuv; Lacoste, Baptiste; Hamel, Edith

    2013-02-20

    The locus coeruleus (LC), the main source of brain noradrenalin (NA), modulates cortical activity, cerebral blood flow (CBF), glucose metabolism, and blood-brain barrier permeability. However, the role of the LC-NA system in the regulation of cortical CBF has remained elusive. This rat study shows that similar proportions (∼20%) of cortical pyramidal cells and GABA interneurons are contacted by LC-NA afferents on their cell soma or proximal dendrites. LC stimulation induced ipsilateral activation (c-Fos upregulation) of pyramidal cells and of a larger proportion (>36%) of interneurons that colocalize parvalbumin, somatostatin, or nitric oxide synthase compared with pyramidal cells expressing cyclooxygenase-2 (22%, p interneurons (16%, p BK, -52%, p < 0.05), and inward-rectifier (Kir, -40%, p < 0.05) K+ channels primarily impaired the hyperemic response. The data demonstrate that LC stimulation recruits a broad network of cortical excitatory and inhibitory neurons resulting in increased cortical activity and that K+ fluxes and EET signaling mediate a large part of the hemodynamic response.

  10. Simulation of Robustness against Lesions of Cortical Networks

    CERN Document Server

    Kaiser, Marcus; Andras, Peter; Young, Malcolm P

    2008-01-01

    Structure entails function and thus a structural description of the brain will help to understand its function and may provide insights into many properties of brain systems, from their robustness and recovery from damage, to their dynamics and even their evolution. Advances in the analysis of complex networks provide useful new approaches to understanding structural and functional properties of brain networks. Structural properties of networks recently described allow their characterization as small-world, random (exponential) and scale-free. They complement the set of other properties that have been explored in the context of brain connectivity, such as topology, hodology, clustering, and hierarchical organization. Here we apply new network analysis methods to cortical inter-areal connectivity networks for the cat and macaque brains. We compare these corticocortical fibre networks to benchmark rewired, small-world, scale-free and random networks, using two analysis strategies, in which we measure the effect...

  11. Graph properties of synchronized cortical networks during visual working memory maintenance.

    Science.gov (United States)

    Palva, Satu; Monto, Simo; Palva, J Matias

    2010-02-15

    Oscillatory synchronization facilitates communication in neuronal networks and is intimately associated with human cognition. Neuronal activity in the human brain can be non-invasively imaged with magneto- (MEG) and electroencephalography (EEG), but the large-scale structure of synchronized cortical networks supporting cognitive processing has remained uncharacterized. We combined simultaneous MEG and EEG (MEEG) recordings with minimum-norm-estimate-based inverse modeling to investigate the structure of oscillatory phase synchronized networks that were active during visual working memory (VWM) maintenance. Inter-areal phase-synchrony was quantified as a function of time and frequency by single-trial phase-difference estimates of cortical patches covering the entire cortical surfaces. The resulting networks were characterized with a number of network metrics that were then compared between delta/theta- (3-6 Hz), alpha- (7-13 Hz), beta- (16-25 Hz), and gamma- (30-80 Hz) frequency bands. We found several salient differences between frequency bands. Alpha- and beta-band networks were more clustered and small-world like but had smaller global efficiency than the networks in the delta/theta and gamma bands. Alpha- and beta-band networks also had truncated-power-law degree distributions and high k-core numbers. The data converge on showing that during the VWM-retention period, human cortical alpha- and beta-band networks have a memory-load dependent, scale-free small-world structure with densely connected core-like structures. These data further show that synchronized dynamic networks underlying a specific cognitive state can exhibit distinct frequency-dependent network structures that could support distinct functional roles.

  12. Qualia could arise from information processing in local cortical networks

    Directory of Open Access Journals (Sweden)

    Roger D Orpwood

    2013-03-01

    Full Text Available Re-entrant feedback, either within sensory cortex or arising from prefrontal areas, has been strongly linked to the emergence of consciousness, both in theoretical and experimental work. This idea, together with evidence for local micro-consciousness, suggests the generation of qualia could in some way result from local network activity under re-entrant activation. This paper explores the possibility by examining the processing of information by local cortical networks. It highlights the difference between the information structure (how the information is physically embodied, and the information message (what the information is about. It focuses on the network’s ability to recognise information structures amongst its inputs under conditions of extensive local feedback, and to then assign information messages to those structures. It is shown that if the re-entrant feedback enables the network to achieve an attractor state, then the message assigned in any given pass of information through the network is a representation of the message assigned in the previous pass-through of information. Based on this ability the paper argues that as information is repeatedly cycled through the network, the information message that is assigned evolves from a recognition of what the input structure is, to what it is like, to how it appears, to how it seems. It could enable individual networks to be the site of qualia generation. The paper goes on to show networks in cortical layers 2/3 and 5a have the connectivity required for the behaviour proposed, and reviews some evidence for a link between such local cortical cyclic activity and conscious percepts. It concludes with some predictions based on the theory discussed.

  13. Spike phase synchronization in multiplex cortical neural networks

    Science.gov (United States)

    Jalili, Mahdi

    2017-01-01

    In this paper we study synchronizability of two multiplex cortical networks: whole-cortex of hermaphrodite C. elegans and posterior cortex in male C. elegans. These networks are composed of two connection layers: network of chemical synapses and the one formed by gap junctions. This work studies the contribution of each layer on the phase synchronization of non-identical spiking Hindmarsh-Rose neurons. The network of male C. elegans shows higher phase synchronization than its randomized version, while it is not the case for hermaphrodite type. The random networks in each layer are constructed such that the nodes have the same degree as the original network, thus providing an unbiased comparison. In male C. elegans, although the gap junction network is sparser than the chemical network, it shows higher contribution in the synchronization phenomenon. This is not the case in hermaphrodite type, which is mainly due to significant less density of gap junction layer (0.013) as compared to chemical layer (0.028). Also, the gap junction network in this type has stronger community structure than the chemical network, and this is another driving factor for its weaker synchronizability.

  14. Relations between the geometry of cortical gyrification and white-matter network architecture.

    Science.gov (United States)

    Henderson, James A; Robinson, Peter A

    2014-03-01

    A geometrically based network model of cortico-cortical white-matter connectivity is used in combination with diffusion spectrum MRI (DSI) data to show that white-matter cortical network architecture is founded on a homogeneous, isotropic geometric connection principle. No other special information about single connections or groups of connections is required to generate networks very similar to experimental ones. This model provides excellent agreement with experimental DSI frequency distributions of network measures-degree, clustering coefficient, path length, and betweenness centrality. In the model, these distributions are a result of geometrically induced spatial variations in the values of these measures with deep nodes having more hublike properties than superficial nodes. This leads to experimentally testable predictions of corresponding variations in real cortexes. The convoluted geometry of the cortex is also found to introduce weak modularity, similar to the lobe structure of the cortex, with the boundaries between modules having hublike properties. These findings mean that some putative discoveries regarding the structure of white-matter cortical networks are simply artifacts and/or consequences of geometry. This model may help provide insight into diseases associated with differences in gyrification as well as evolutionary development of the cortex.

  15. Response variability in balanced cortical networks

    DEFF Research Database (Denmark)

    Lerchner, Alexander; Ursta, C.; Hertz, J.

    2006-01-01

    population. The high connectivity permits a mean field description in which synaptic currents can be treated as gaussian noise, the mean and autocorrelation function of which are calculated self-consistently from the firing statistics of single model neurons. Within this description, a wide range of Fano...

  16. Cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm

    Directory of Open Access Journals (Sweden)

    Salvador eDura-Bernal

    2015-11-01

    Full Text Available Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm.This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuro-prosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility

  17. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.

    Science.gov (United States)

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A; Przekwas, Andrzej; Francis, Joseph T; Lytton, William W

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  18. Functional Cortical Network in Alpha Band Correlates with Social Bargaining

    Science.gov (United States)

    Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

    2014-01-01

    Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240

  19. Cortical factor feedback model for cellular locomotion and cytofission.

    Directory of Open Access Journals (Sweden)

    Shin I Nishimura

    2009-03-01

    Full Text Available Eukaryotic cells can move spontaneously without being guided by external cues. For such spontaneous movements, a variety of different modes have been observed, including the amoeboid-like locomotion with protrusion of multiple pseudopods, the keratocyte-like locomotion with a widely spread lamellipodium, cell division with two daughter cells crawling in opposite directions, and fragmentations of a cell to multiple pieces. Mutagenesis studies have revealed that cells exhibit these modes depending on which genes are deficient, suggesting that seemingly different modes are the manifestation of a common mechanism to regulate cell motion. In this paper, we propose a hypothesis that the positive feedback mechanism working through the inhomogeneous distribution of regulatory proteins underlies this variety of cell locomotion and cytofission. In this hypothesis, a set of regulatory proteins, which we call cortical factors, suppress actin polymerization. These suppressing factors are diluted at the extending front and accumulated at the retracting rear of cell, which establishes a cellular polarity and enhances the cell motility, leading to the further accumulation of cortical factors at the rear. Stochastic simulation of cell movement shows that the positive feedback mechanism of cortical factors stabilizes or destabilizes modes of movement and determines the cell migration pattern. The model predicts that the pattern is selected by changing the rate of formation of the actin-filament network or the threshold to initiate the network formation.

  20. The Impact of Structural Heterogeneity on Excitation-Inhibition Balance in Cortical Networks.

    Science.gov (United States)

    Landau, Itamar D; Egger, Robert; Dercksen, Vincent J; Oberlaender, Marcel; Sompolinsky, Haim

    2016-12-07

    Models of cortical dynamics often assume a homogeneous connectivity structure. However, we show that heterogeneous input connectivity can prevent the dynamic balance between excitation and inhibition, a hallmark of cortical dynamics, and yield unrealistically sparse and temporally regular firing. Anatomically based estimates of the connectivity of layer 4 (L4) rat barrel cortex and numerical simulations of this circuit indicate that the local network possesses substantial heterogeneity in input connectivity, sufficient to disrupt excitation-inhibition balance. We show that homeostatic plasticity in inhibitory synapses can align the functional connectivity to compensate for structural heterogeneity. Alternatively, spike-frequency adaptation can give rise to a novel state in which local firing rates adjust dynamically so that adaptation currents and synaptic inputs are balanced. This theory is supported by simulations of L4 barrel cortex during spontaneous and stimulus-evoked conditions. Our study shows how synaptic and cellular mechanisms yield fluctuation-driven dynamics despite structural heterogeneity in cortical circuits.

  1. Self-organized criticality in cortical assemblies occurs in concurrent scale-free and small-world networks.

    Science.gov (United States)

    Massobrio, Paolo; Pasquale, Valentina; Martinoia, Sergio

    2015-06-01

    The spontaneous activity of cortical networks is characterized by the emergence of different dynamic states. Although several attempts were accomplished to understand the origin of these dynamics, the underlying factors continue to be elusive. In this work, we specifically investigated the interplay between network topology and spontaneous dynamics within the framework of self-organized criticality (SOC). The obtained results support the hypothesis that the emergence of critical states occurs in specific complex network topologies. By combining multi-electrode recordings of spontaneous activity of in vitro cortical assemblies with theoretical models, we demonstrate that different 'connectivity rules' drive the network towards different dynamic states. In particular, scale-free architectures with different degree of small-worldness account better for the variability observed in experimental data, giving rise to different dynamic states. Moreover, in relationship with the balance between excitation and inhibition and percentage of inhibitory hubs, the simulated cortical networks fall in a critical regime.

  2. Euchromatin histone methyltransferase 1 regulates cortical neuronal network development

    Science.gov (United States)

    Bart Martens, Marijn; Frega, Monica; Classen, Jessica; Epping, Lisa; Bijvank, Elske; Benevento, Marco; van Bokhoven, Hans; Tiesinga, Paul; Schubert, Dirk; Nadif Kasri, Nael

    2016-01-01

    Heterozygous mutations or deletions in the human Euchromatin histone methyltransferase 1 (EHMT1) gene cause Kleefstra syndrome, a neurodevelopmental disorder that is characterized by autistic-like features and severe intellectual disability (ID). Neurodevelopmental disorders including ID and autism may be related to deficits in activity-dependent wiring of brain circuits during development. Although Kleefstra syndrome has been associated with dendritic and synaptic defects in mice and Drosophila, little is known about the role of EHMT1 in the development of cortical neuronal networks. Here we used micro-electrode arrays and whole-cell patch-clamp recordings to investigate the impact of EHMT1 deficiency at the network and single cell level. We show that EHMT1 deficiency impaired neural network activity during the transition from uncorrelated background action potential firing to synchronized network bursting. Spontaneous bursting and excitatory synaptic currents were transiently reduced, whereas miniature excitatory postsynaptic currents were not affected. Finally, we show that loss of function of EHMT1 ultimately resulted in less regular network bursting patterns later in development. These data suggest that the developmental impairments observed in EHMT1-deficient networks may result in a temporal misalignment between activity-dependent developmental processes thereby contributing to the pathophysiology of Kleefstra syndrome. PMID:27767173

  3. Cortical EEG oscillations and network connectivity as efficacy indices for assessing drugs with cognition enhancing potential.

    Science.gov (United States)

    Ahnaou, A; Huysmans, H; Jacobs, T; Drinkenburg, W H I M

    2014-11-01

    Synchronization of electroencephalographic (EEG) oscillations represents a core mechanism for cortical and subcortical networks, and disturbance in neural synchrony underlies cognitive processing deficits in neurological and neuropsychiatric disorders. Here, we investigated the effects of cognition enhancers (donepezil, rivastigmine, tacrine, galantamine and memantine), which are approved for symptomatic treatment of dementia, on EEG oscillations and network connectivity in conscious rats chronically instrumented with epidural electrodes in different cortical areas. Next, EEG network indices of cognitive impairments with the muscarinic receptor antagonist scopolamine were modeled. Lastly, we examined the efficacy of cognition enhancers to normalize those aberrant oscillations. Cognition enhancers elicited systematic ("fingerprint") enhancement of cortical slow theta (4.5-6 Hz) and gamma (30.5-50 Hz) oscillations correlated with lower activity levels. Principal component analysis (PCA) revealed a compact cluster that corresponds to shared underlying mechanisms as compared to different drug classes. Functional network connectivity revealed consistent elevated coherent slow theta activity in parieto-occipital and between interhemispheric cortical areas. In rats instrumented with depth hippocampal CA1-CA3 electrodes, donepezil elicited similar oscillatory and coherent activities in cortico-hippocampal networks. When combined with scopolamine, the cognition enhancers attenuated the leftward shift in coherent slow delta activity. Such a consistent shift in EEG coherence into slow oscillations associated with altered slow theta and gamma oscillations may underlie cognitive deficits in scopolamine-treated animals, whereas enhanced coherent slow theta and gamma activity may be a relevant mechanism by which cognition enhancers exert their beneficial effect on plasticity and cognitive processes. The findings underscore that PCA and network connectivity are valuable tools to

  4. Taming desynchronized bursting with delays in the Macaque cortical network

    Institute of Scientific and Technical Information of China (English)

    Wang Qing-Yun; Murks Aleksandra; Perc Matja(z); Lu Qi-Shao

    2011-01-01

    Inhibitory coupled bursting Hindmarsh-Rose neurons are considered as constitutive units of the Macaque cortical network. In the absence of information transmission delay the bursting activity is desynchronized, giving rise to spatiotemporally disordered dynamics. This paper shows that the introduction of finite delays can lead to the synchroization of bursting and thus to the emergence of coherent propagating fronts of excitation in the space-time domain.Moreover, it shows that the type of synchronous bursting is uniquely determined by the delay length, with the transitions from one type to the other occurring in a step-like manner depending on the delay. Interestingly, as the delay is tuned close to the transition points, the synchronization deteriorates, which implies the coexistence of different bursting attractors. These phenomena can be observed be different but fixed coupling strengths, thus indicating a new role for information transmission delays in realistic neuronal networks.

  5. Population spikes in cortical networks during different functional states.

    Directory of Open Access Journals (Sweden)

    Shirley eMark

    2012-07-01

    Full Text Available Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing (Population Spikes can be generated in homogenous recurrent neural networks with short-term synaptic depression. Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.

  6. Cultured networks of excitatory projection neurons and inhibitory interneurons for studying human cortical neurotoxicity.

    Science.gov (United States)

    Xu, Jin-Chong; Fan, Jing; Wang, Xueqing; Eacker, Stephen M; Kam, Tae-In; Chen, Li; Yin, Xiling; Zhu, Juehua; Chi, Zhikai; Jiang, Haisong; Chen, Rong; Dawson, Ted M; Dawson, Valina L

    2016-04-06

    Translating neuroprotective treatments from discovery in cell and animal models to the clinic has proven challenging. To reduce the gap between basic studies of neurotoxicity and neuroprotection and clinically relevant therapies, we developed a human cortical neuron culture system from human embryonic stem cells or human inducible pluripotent stem cells that generated both excitatory and inhibitory neuronal networks resembling the composition of the human cortex. This methodology used timed administration of retinoic acid to FOXG1(+) neural precursor cells leading to differentiation of neuronal populations representative of the six cortical layers with both excitatory and inhibitory neuronal networks that were functional and homeostatically stable. In human cortical neuronal cultures, excitotoxicity or ischemia due to oxygen and glucose deprivation led to cell death that was dependent on N-methyl-D-aspartate (NMDA) receptors, nitric oxide (NO), and poly(ADP-ribose) polymerase (PARP) (a cell death pathway called parthanatos that is distinct from apoptosis, necroptosis, and other forms of cell death). Neuronal cell death was attenuated by PARP inhibitors that are currently in clinical trials for cancer treatment. This culture system provides a new platform for the study of human cortical neurotoxicity and suggests that PARP inhibitors may be useful for ameliorating excitotoxic and ischemic cell death in human neurons.

  7. Visual cortical areas of the mouse: comparison of parcellation and network structure with primates.

    Science.gov (United States)

    Laramée, Marie-Eve; Boire, Denis

    2014-01-01

    Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals.

  8. Math anxiety: Brain cortical network changes in anticipation of doing mathematics.

    Science.gov (United States)

    Klados, Manousos A; Pandria, Niki; Micheloyannis, Sifis; Margulies, Daniel; Bamidis, Panagiotis D

    2017-05-05

    Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Order-based representation in random networks of cortical neurons.

    Directory of Open Access Journals (Sweden)

    Goded Shahaf

    2008-11-01

    Full Text Available The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.

  10. Electrophysiological Data and the Biophysical Modelling of Local Cortical Circuits

    Directory of Open Access Journals (Sweden)

    Dimitris Pinotsis

    2014-03-01

    Full Text Available This paper shows how recordings of gamma oscillations – under different experimental conditions or from different subjects – can be combined with a class of population models called neural fields and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical structure and function. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. It draws on the computational power of Bayesian model inversion, when applied to neural field models of cortical dynamics. Bayesian model comparison allows one to adjudicate among different mechanistic hypotheses about cortical excitability, synaptic kinetics and the cardinal topographic features of local cortical circuits. It also provides optimal parameter estimates that quantify neuromodulation and the spatial dispersion of axonal connections or summation of receptive fields in the visual cortex. This paper provides an overview of a family of neural field models that have been recently implemented using the DCM toolbox of the academic freeware Statistical Parametric Mapping (SPM. The SPM software is a popular platform for analyzing neuroimaging data, used by several neuroscience communities worldwide. DCM allows for a formal (Bayesian statistical analysis of cortical network connectivity, based upon realistic biophysical models of brain responses. It is this particular feature of DCM – the unique combination of generative models with optimization techniques based upon (variational Bayesian principles – that furnishes a novel way to characterize functional brain architectures. In particular, it provides answers to questions about how the brain is wired and how it responds to different experimental manipulations. For a review of the general role of neural fields in SPM the reader can consult e.g. see [1]. Neural fields have a long and illustrious history in mathematical

  11. Increased cortical thickness in a frontoparietal network in social anxiety disorder.

    Science.gov (United States)

    Brühl, Annette Beatrix; Hänggi, Jürgen; Baur, Volker; Rufer, Michael; Delsignore, Aba; Weidt, Steffi; Jäncke, Lutz; Herwig, Uwe

    2014-07-01

    Social anxiety disorder (SAD) is the second leading anxiety disorder. On the functional neurobiological level, specific brain regions involved in the processing of anxiety-laden stimuli and in emotion regulation have been shown to be hyperactive and hyper-responsive in SAD such as amygdala, insula and orbito- and prefrontal cortex. On the level of brain structure, prior studies on anatomical differences in SAD resulted in mixed and partially contradictory findings. Based on previous functional and anatomical models of SAD, this study examined cortical thickness in structural magnetic resonance imaging data of 46 patients with SAD without comorbidities (except for depressed episode in one patient) compared with 46 matched healthy controls in a region of interest-analysis and in whole-brain. In a theory-driven ROI-analysis, cortical thickness was increased in SAD in left insula, right anterior cingulate and right temporal pole. Furthermore, the whole-brain analysis revealed increased thickness in right dorsolateral prefrontal and right parietal cortex. This study detected no regions of decreased cortical thickness or brain volume in SAD. From the perspective of brain networks, these findings are in line with prior functional differences in salience networks and frontoparietal networks associated with executive-controlling and attentional functions.

  12. Short-term memory in networks of dissociated cortical neurons.

    Science.gov (United States)

    Dranias, Mark R; Ju, Han; Rajaram, Ezhilarasan; VanDongen, Antonius M J

    2013-01-30

    Short-term memory refers to the ability to store small amounts of stimulus-specific information for a short period of time. It is supported by both fading and hidden memory processes. Fading memory relies on recurrent activity patterns in a neuronal network, whereas hidden memory is encoded using synaptic mechanisms, such as facilitation, which persist even when neurons fall silent. We have used a novel computational and optogenetic approach to investigate whether these same memory processes hypothesized to support pattern recognition and short-term memory in vivo, exist in vitro. Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays. Cultures were transfected with ChannelRhodopsin-2 and optically stimulated using random dot stimuli. The pattern of neuronal activity resulting from this stimulation was analyzed using classification algorithms that enabled the identification of stimulus-specific memories. Fading memories for different stimuli, encoded in ongoing neural activity, persisted and could be distinguished from each other for as long as 1 s after stimulation was terminated. Hidden memories were detected by altered responses of neurons to additional stimulation, and this effect persisted longer than 1 s. Interestingly, network bursts seem to eliminate hidden memories. These results are similar to those that have been reported from similar experiments in vivo and demonstrate that mechanisms of information processing and short-term memory can be studied using cultured neuronal networks, thereby setting the stage for therapeutic applications using this platform.

  13. Complex Networks in Psychological Models

    Science.gov (United States)

    Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

    We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

  14. The Age of Cortical Neural Networks Affects Their Interactions with Magnetic Nanoparticles.

    Science.gov (United States)

    Tay, Andy; Kunze, Anja; Jun, Dukwoo; Hoek, Eric; Di Carlo, Dino

    2016-07-01

    Despite increasing use of nanotechnology in neuroscience, the characterization of interactions between magnetic nanoparticles (MNPs) and primary cortical neural networks remains underdeveloped. In particular, how the age of primary neural networks affects MNP uptake and endocytosis is critical when considering MNP-based therapies for age-related diseases. Here, primary cortical neural networks are cultured up to 4 weeks and with CCL11/eotaxin, an age-inducing chemokine, to create aged neural networks. As the neural networks are aged, their association with membrane-bound starch-coated ferromagnetic nanoparticles (fMNPs) increases while their endocytic mechanisms are impaired, resulting in reduced internalization of chitosan-coated fMNPs. The age of the neurons also negates the neuroprotective effects of chitosan coatings on fMNPs, attributing to decreased intracellular trafficking and increased colocalization of MNPs with lysosomes. These findings demonstrate the importance of age and developmental stage of primary neural cells when developing in vitro models for fMNP therapeutics targeting age-related diseases.

  15. Low and High-Frequency Field Potentials of Cortical Networks ...

    Science.gov (United States)

    Neural networks grown on microelectrode arrays (MEAs) have become an important, high content in vitro assay for assessing neuronal function. MEA experiments typically examine high- frequency (HF) (>200 Hz) spikes, and bursts which can be used to discriminate between different pharmacological agents/chemicals. However, normal brain activity is additionally composed of integrated low-frequency (0.5-100 Hz) field potentials (LFPs) which are filtered out of MEA recordings. The objective of this study was to characterize the relationship between HF and LFP neural network signals, and to assess the relative sensitivity of LFPs to selected neurotoxicants. Rat primary cortical cultures were grown on glass, single-well MEA chips. Spontaneous activity was sampled at 25 kHz and recorded (5 min) (Multi-Channel Systems) from mature networks (14 days in vitro). HF (spike, mean firing rate, MFR) and LF (power spectrum, amplitude) components were extracted from each network and served as its baseline (BL). Next, each chip was treated with either 1) a positive control, bicuculline (BIC, 25μM) or domoic acid (DA, 0.3μM), 2) or a negative control, acetaminophen (ACE, 100μM) or glyphosate (GLY, 100μM), 3) a solvent control (H2O or DMSO:EtOH), or 4) a neurotoxicant, (carbaryl, CAR 5, 30μM ; lindane, LIN 1, 10μM; permethrin, PERM 25, 50μM; triadimefon, TRI 5, 65μM). Post treatment, 5 mins of spontaneous activity was recorded and analyzed. As expected posit

  16. LFP spectral peaks in V1 cortex: network resonance and cortico-cortical feedback.

    Science.gov (United States)

    Kang, Kukjin; Shelley, Michael; Henrie, James Andrew; Shapley, Robert

    2010-12-01

    This paper is about how cortical recurrent interactions in primary visual cortex (V1) together with feedback from extrastriate cortex can account for spectral peaks in the V1 local field potential (LFP). Recent studies showed that visual stimulation enhances the γ-band (25-90 Hz) of the LFP power spectrum in macaque V1. The height and location of the γ-band peak in the LFP spectrum were correlated with visual stimulus size. Extensive spatial summation, possibly mediated by feedback connections from extrastriate cortex and long-range horizontal connections in V1, must play a crucial role in the size dependence of the LFP. To analyze stimulus-effects on the LFP of V1 cortex, we propose a network model for the visual cortex that includes two populations of V1 neurons, excitatory and inhibitory, and also includes feedback to V1 from extrastriate cortex. The neural network model for V1 was a resonant system. The model's resonance frequency (ResF) was in the γ-band and varied up or down in frequency depending on cortical feedback. The model's ResF shifted downward with stimulus size, as in the real cortex, because increased size recruited more activity in extrastriate cortex and V1 thereby causing stronger feedback. The model needed to have strong local recurrent inhibition within V1 to obtain ResFs that agree with cortical data. Network resonance as a consequence of recurrent excitation and inhibition appears to be a likely explanation for γ-band peaks in the LFP power spectrum of the primary visual cortex.

  17. Predictors of coupling between structural and functional cortical networks in normal aging.

    Science.gov (United States)

    Romero-Garcia, Rafael; Atienza, Mercedes; Cantero, Jose L

    2014-06-01

    Understanding how the mammalian neocortex creates cognition largely depends on knowledge about large-scale cortical organization. Accumulated evidence has illuminated cortical substrates of cognition across the lifespan, but how topological properties of cortical networks support structure-function relationships in normal aging remains an open question. Here we investigate the role of connections (i.e., short/long and direct/indirect) and node properties (i.e., centrality and modularity) in predicting functional-structural connectivity coupling in healthy elderly subjects. Connectivity networks were derived from correlations of cortical thickness and cortical glucose consumption in resting state. Local-direct connections (i.e., nodes separated by less than 30 mm) and node modularity (i.e., a set of nodes highly interconnected within a topological community and sparsely interconnected with nodes from other modules) in the functional network were identified as the main determinants of coupling between cortical networks, suggesting that the structural network in aging is mainly constrained by functional topological properties involved in the segregation of information, likely due to aging-related deficits in functional integration. This hypothesis is supported by an enhanced connectivity between cortical regions of different resting-state networks involved in sensorimotor and memory functions in detrimental to associations between fronto-parietal regions supporting executive processes. Taken collectively, these findings open new avenues to identify aging-related failures in the anatomo-functional organization of the neocortical mantle, and might contribute to early detection of prevalent neurodegenerative conditions occurring in the late life.

  18. Cortical oscillatory dynamics in a social interaction model.

    Science.gov (United States)

    Knyazev, Gennady G; Slobodskoj-Plusnin, Jaroslav Y; Bocharov, Andrey V; Pylkova, Liudmila V

    2013-03-15

    In this study we sought to investigate cortical oscillatory dynamics accompanying three major kinds of social behavior: aggressive, friendly, and avoidant. Behavioral and EEG data were collected in 48 participants during a computer game modeling social interactions with virtual 'persons'. 3D source reconstruction and independent component analysis were applied to EEG data. Results showed that social behavior was partly reactive and partly proactive with subject's personality playing an important role in shaping this behavior. Most salient differences were found between avoidance and approach behaviors, whereas the two kinds of approach behavior (i.e., aggression and friendship) did not differ from each other. Comparative to avoidance, approach behaviors were associated with higher induced responses in most frequency bands which were mostly observed in cortical areas overlapping with the default mode network. The difference between approach- and avoidance-related oscillatory dynamics was more salient in subjects predisposed to approach behaviors (i.e., in aggressive or sociable subjects) and was less pronounced in subjects predisposed to avoidance behavior (i.e., in high trait anxiety scorers). There was a trend to higher low frequency phase-locking in motor area in approach than in avoid condition. Results are discussed in light of the concept linking induced responses with top-down and evoked responses with bottom-up processes. Copyright © 2012 Elsevier B.V. All rights reserved.

  19. Cortical network dynamics with time delays reveals functional connectivity in the resting brain.

    NARCIS (Netherlands)

    Ghosh, A.; Rho, Y.; McIntosh, A.R.; Kotter, R.; Jirsa, V.K.

    2008-01-01

    In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present pa

  20. Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity

    Directory of Open Access Journals (Sweden)

    Juan Carlos Vasquez

    2013-11-01

    Full Text Available Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that report quantifiable behavioral responses in response to short spike trains elicited in single neurons. Hence, these networks are stable against internally generated fluctuations in firing rate but at the same time remain sensitive to similarly-sized externally induced perturbations. We investigated stability and sensitivity in a simple recurrent network of stochastic binary neurons and determined numerically the effects of correlation between the number of afferent (‘in-degree’ and efferent (‘out-degree’ connections in neurons. The key advance reported in this work is that anti-correlation between in-/out-degree distributions increased the stability of the network in comparison to networks with no correlation or positive correlations, while being able to achieve the same level of sensitivity. The experimental characterization of degree distributions is difficult because all presynaptic and postsynaptic neurons have to be identified and counted. We explored whether the statistics of network motifs, which requires the characterization of connections between small subsets of neurons, could be used to detect evidence for degree anti-correlations. We find that the sample frequency of the 3-neuron ‘ring’ motif (1→2→3→1, can be used to detect degree anti-correlation for sub-networks of size 30 using about 50 samples, which is of significance because the necessary measurements are achievable experimentally in the near future.Taken together, we hypothesize that barrel cortex networks exhibit degree anti-correlations and specific network motif statistics.

  1. Neural synchrony in cortical networks: history, concept and current status

    Directory of Open Access Journals (Sweden)

    Peter Uhlhaas

    2009-07-01

    Full Text Available Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies.

  2. Cortical network dysfunction caused by a subtle defect of myelination

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    Poggi, Giulia; Boretius, Susann; Möbius, Wiebke; Moschny, Nicole; Baudewig, Jürgen; Ruhwedel, Torben; Hassouna, Imam; Wieser, Georg L.; Werner, Hauke B.; Goebbels, Sandra

    2016-01-01

    Subtle white matter abnormalities have emerged as a hallmark of brain alterations in magnetic resonance imaging or upon autopsy of mentally ill subjects. However, it is unknown whether such reduction of white matter and myelin contributes to any disease‐relevant phenotype or simply constitutes an epiphenomenon, possibly even treatment‐related. Here, we have re‐analyzed Mbp heterozygous mice, the unaffected parental strain of shiverer, a classical neurological mutant. Between 2 and 20 months of age, Mbp+/‐ versus Mbp+/+ littermates were deeply phenotyped by combining extensive behavioral/cognitive testing with MRI, 1H‐MR spectroscopy, electron microscopy, and molecular techniques. Surprisingly, Mbp‐dependent myelination was significantly reduced in the prefrontal cortex. We also noticed a mild but progressive hypomyelination of the prefrontal corpus callosum and low‐grade inflammation. While most behavioral functions were preserved, Mbp+/‐ mice exhibited defects of sensorimotor gating, as evidenced by reduced prepulse‐inhibition, and a late‐onset catatonia phenotype. Thus, subtle but primary abnormalities of CNS myelin can be the cause of a persistent cortical network dysfunction including catatonia, features typical of neuropsychiatric conditions. GLIA 2016;64:2025–2040 PMID:27470661

  3. Predicting the connectivity of primate cortical networks from topological and spatial node properties

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    Kaiser Marcus

    2007-03-01

    Full Text Available Abstract Background The organization of the connectivity between mammalian cortical areas has become a major subject of study, because of its important role in scaffolding the macroscopic aspects of animal behavior and intelligence. In this study we present a computational reconstruction approach to the problem of network organization, by considering the topological and spatial features of each area in the primate cerebral cortex as subsidy for the reconstruction of the global cortical network connectivity. Starting with all areas being disconnected, pairs of areas with similar sets of features are linked together, in an attempt to recover the original network structure. Results Inferring primate cortical connectivity from the properties of the nodes, remarkably good reconstructions of the global network organization could be obtained, with the topological features allowing slightly superior accuracy to the spatial ones. Analogous reconstruction attempts for the C. elegans neuronal network resulted in substantially poorer recovery, indicating that cortical area interconnections are relatively stronger related to the considered topological and spatial properties than neuronal projections in the nematode. Conclusion The close relationship between area-based features and global connectivity may hint on developmental rules and constraints for cortical networks. Particularly, differences between the predictions from topological and spatial properties, together with the poorer recovery resulting from spatial properties, indicate that the organization of cortical networks is not entirely determined by spatial constraints.

  4. Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas.

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    Négyessy, László; Nepusz, Tamás; Zalányi, László; Bazsó, Fülöp

    2008-10-22

    Cognition is based on the integrated functioning of hierarchically organized cortical processing streams in a manner yet to be clarified. Because integration fundamentally depends on convergence and the complementary notion of divergence of the neuronal connections, we analysed integration by measuring the degree of convergence/divergence through the connections in the network of cortical areas. By introducing a new index, we explored the complementary convergent and divergent nature of connectional reciprocity and delineated the backward and forward cortical sub-networks for the first time. Integrative properties of the areas defined by the degree of convergence/divergence through their afferents and efferents exhibited distinctive characteristics at different levels of the cortical hierarchy. Areas previously identified as hubs exhibit information bottleneck properties. Cortical networks largely deviate from random graphs where convergence and divergence are balanced at low reciprocity level. In the cortex, which is dominated by reciprocal connections, balance appears only by further increasing the number of reciprocal connections. The results point to the decisive role of the optimal number and placement of reciprocal connections in large-scale cortical integration. Our findings also facilitate understanding of the functional interactions between the cortical areas and the information flow or its equivalents in highly recurrent natural and artificial networks.

  5. Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method

    CERN Document Server

    Barkaoui, Abdelwahed; Tarek, Merzouki; Hambli, Ridha; Ali, Mkaddem

    2014-01-01

    The complexity and heterogeneity of bone tissue require a multiscale modelling to understand its mechanical behaviour and its remodelling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network computation and homogenisation equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained neural network simulation. Finite element (FE) calculation is performed at nanoscopic levels to provide a database to train an in-house neural network program; (iii) in steps 2 to 10 from fibril to continuum cortical bone tissue, homogenisation equations are used to perform the computation at the higher s...

  6. The thalamo-cortical complex network correlates of chronic pain

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    Zippo, Antonio G.; Valente, Maurizio; Caramenti, Gian Carlo; Biella, Gabriele E. M.

    2016-01-01

    Chronic pain (CP) is a condition with a large repertory of clinical signs and symptoms with diverse expressions. Though widely analyzed, an appraisal at the level of single neuron and neuronal networks in CP is however missing. The present research proposes an empirical and theoretic framework which identifies a complex network correlate nested in the somatosensory thalamocortical (TC) circuit in diverse CP models. In vivo simultaneous extracellular neuronal electrophysiological high-density recordings have been performed from the TC circuit in rats. Wide functional network statistics neatly discriminated CP from control animals identifying collective dynamical traits. In particular, a collapsed functional connectivity and an altered modular architecture of the thalamocortical circuit have been evidenced. These results envisage CP as a functional connectivity disorder and give the clue for unveiling innovative therapeutic strategies. PMID:27734895

  7. Effects of Small-World Rewiring Probability and Noisy Synaptic Conductivity on Slow Waves: Cortical Network.

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    Tekin, Ramazan; Tagluk, Mehmet Emin

    2017-03-01

    Physiological rhythms play a critical role in the functional development of living beings. Many biological functions are executed with an interaction of rhythms produced by internal characteristics of scores of cells. While synchronized oscillations may be associated with normal brain functions, anomalies in these oscillations may cause or relate the emergence of some neurological or neuropsychological pathologies. This study was designed to investigate the effects of topological structure and synaptic conductivity noise on the spatial synchronization and temporal rhythmicity of the waves generated by cells in the network. Because of holding the ability of clustering and randomizing with change of parameters, small-world (SW) network topology was chosen. The oscillatory activity of network was tried out by manipulating an insulated SW, cortical network model whose morphology is very close to real world. According to the obtained results, it was observed that at the optimal probabilistic rates of conductivity noise and rewiring of SW, powerful synchronized oscillatory small waves are generated in relation to the internal dynamics of cells, which are in line with the network's input. These two parameters were observed to be quite effective on the excitation-inhibition balance of the network. Accordingly, it may be suggested that the topological dynamics of SW and noisy synaptic conductivity may be associated with the normal and abnormal development of neurobiological structure.

  8. Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

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    Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

    2017-01-18

    This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

  9. Statistical Analysis of Tract-Tracing Experiments Demonstrates a Dense, Complex Cortical Network in the Mouse.

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    Ypma, Rolf J F; Bullmore, Edward T

    2016-09-01

    Anatomical tract tracing methods are the gold standard for estimating the weight of axonal connectivity between a pair of pre-defined brain regions. Large studies, comprising hundreds of experiments, have become feasible by automated methods. However, this comes at the cost of positive-mean noise making it difficult to detect weak connections, which are of particular interest as recent high resolution tract-tracing studies of the macaque have identified many more weak connections, adding up to greater connection density of cortical networks, than previously recognized. We propose a statistical framework that estimates connectivity weights and credibility intervals from multiple tract-tracing experiments. We model the observed signal as a log-normal distribution generated by a combination of tracer fluorescence and positive-mean noise, also accounting for injections into multiple regions. Using anterograde viral tract-tracing data provided by the Allen Institute for Brain Sciences, we estimate the connection density of the mouse intra-hemispheric cortical network to be 73% (95% credibility interval (CI): 71%, 75%); higher than previous estimates (40%). Inter-hemispheric density was estimated to be 59% (95% CI: 54%, 62%). The weakest estimable connections (about 6 orders of magnitude weaker than the strongest connections) are likely to represent only one or a few axons. These extremely weak connections are topologically more random and longer distance than the strongest connections, which are topologically more clustered and shorter distance (spatially clustered). Weak links do not substantially contribute to the global topology of a weighted brain graph, but incrementally increased topological integration of a binary graph. The topology of weak anatomical connections in the mouse brain, rigorously estimable down to the biological limit of a single axon between cortical areas in these data, suggests that they might confer functional advantages for integrative

  10. Cortical Network Dynamics of Perceptual Decision-Making in the Human Brain

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    Markus eSiegel

    2011-02-01

    Full Text Available Goal-directed behavior requires the flexible transformation of sensory evidence about our environment into motor actions. Studies of perceptual decision-making have shown that this transformation is distributed across several widely separated brain regions. Yet, little is known about how decision-making emerges from the dynamic interactions among these regions. Here, we review a series of studies, in which we characterized the cortical network interactions underlying a perceptual decision process in the human brain. We used magnetoencephalography (MEG to measure the large-scale cortical population dynamics underlying each of the sub-processes involved in this decision: the encoding of sensory evidence and action plan, the mapping between the two, and the attentional selection of task-relevant evidence. We found that these sub-processes are mediated by neuronal oscillations within specific frequency ranges. Localized gamma-band oscillations in sensory and motor cortices reflect the encoding of the sensory evidence and motor plan. Large-scale oscillations across widespread cortical networks mediate the integrative processes connecting these local networks: Gamma- and beta-band oscillations across frontal, parietal and sensory cortices serve the selection of relevant sensory evidence and its flexible mapping onto action plans. In sum, our results suggest that perceptual decisions are mediated by oscillatory interactions within overlapping local and large-scale cortical networks.

  11. Early-life exposure to caffeine affects the construction and activity of cortical networks in mice.

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    Fazeli, Walid; Zappettini, Stefania; Marguet, Stephan Lawrence; Grendel, Jasper; Esclapez, Monique; Bernard, Christophe; Isbrandt, Dirk

    2017-09-01

    The consumption of psychoactive drugs during pregnancy can have deleterious effects on newborns. It remains unclear whether early-life exposure to caffeine, the most widely consumed psychoactive substance, alters brain development. We hypothesized that maternal caffeine ingestion during pregnancy and the early postnatal period in mice affects the construction and activity of cortical networks in offspring. To test this hypothesis, we focused on primary visual cortex (V1) as a model neocortical region. In a study design mimicking the daily consumption of approximately three cups of coffee during pregnancy in humans, caffeine was added to the drinking water of female mice and their offspring were compared to control offspring. Caffeine altered the construction of GABAergic neuronal networks in V1, as reflected by a reduced number of somatostatin-containing GABA neurons at postnatal days 6-7, with the remaining ones showing poorly developed dendritic arbors. These findings were accompanied by increased synaptic activity in vitro and elevated network activity in vivo in V1. Similarly, in vivo hippocampal network activity was altered from the neonatal period until adulthood. Finally, caffeine-exposed offspring showed increased seizure susceptibility in a hyperthermia-induced seizure model. In summary, our results indicate detrimental effects of developmental caffeine exposure on mouse brain development. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. A cortical attractor network with Martinotti cells driven by facilitating synapses.

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    Pradeep Krishnamurthy

    Full Text Available The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

  13. The maturation of cortical sleep rhythms and networks over early development.

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    Chu, C J; Leahy, J; Pathmanathan, J; Kramer, M A; Cash, S S

    2014-07-01

    Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  14. Models of cortical malformation--Chemical and physical.

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    Luhmann, Heiko J

    2016-02-15

    Pharmaco-resistant epilepsies, and also some neuropsychiatric disorders, are often associated with malformations in hippocampal and neocortical structures. The mechanisms leading to these cortical malformations causing an imbalance between the excitatory and inhibitory system are largely unknown. Animal models using chemical or physical manipulations reproduce different human pathologies by interfering with cell generation and neuronal migration. The model of in utero injection of methylazoxymethanol (MAM) acetate mimics periventricular nodular heterotopia. The freeze lesion model reproduces (poly)microgyria, focal heterotopia and schizencephaly. The in utero irradiation model causes microgyria and heterotopia. Intraperitoneal injections of carmustine 1-3-bis-chloroethyl-nitrosurea (BCNU) to pregnant rats produces laminar disorganization, heterotopias and cytomegalic neurons. The ibotenic acid model induces focal cortical malformations, which resemble human microgyria and ulegyria. Cortical dysplasia can be also observed following prenatal exposure to ethanol, cocaine or antiepileptic drugs. All these models of cortical malformations are characterized by a pronounced hyperexcitability, few of them also produce spontaneous epileptic seizures. This dysfunction results from an impairment in GABAergic inhibition and/or an increase in glutamatergic synaptic transmission. The cortical region initiating or contributing to this hyperexcitability may not necessarily correspond to the site of the focal malformation. In some models wide-spread molecular and functional changes can be observed in remote regions of the brain, where they cause pathophysiological activities. This paper gives an overview on different animal models of cortical malformations, which are mostly used in rodents and which mimic the pathology and to some extent the pathophysiology of neuronal migration disorders associated with epilepsy in humans.

  15. Abnormal dopaminergic modulation of striato-cortical networks underlies levodopa-induced dyskinesias in humans.

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    Herz, Damian M; Haagensen, Brian N; Christensen, Mark S; Madsen, Kristoffer H; Rowe, James B; Løkkegaard, Annemette; Siebner, Hartwig R

    2015-06-01

    Dopaminergic signalling in the striatum contributes to reinforcement of actions and motivational enhancement of motor vigour. Parkinson's disease leads to progressive dopaminergic denervation of the striatum, impairing the function of cortico-basal ganglia networks. While levodopa therapy alleviates basal ganglia dysfunction in Parkinson's disease, it often elicits involuntary movements, referred to as levodopa-induced peak-of-dose dyskinesias. Here, we used a novel pharmacodynamic neuroimaging approach to identify the changes in cortico-basal ganglia connectivity that herald the emergence of levodopa-induced dyskinesias. Twenty-six patients with Parkinson's disease (age range: 51-84 years; 11 females) received a single dose of levodopa and then performed a task in which they had to produce or suppress a movement in response to visual cues. Task-related activity was continuously mapped with functional magnetic resonance imaging. Dynamic causal modelling was applied to assess levodopa-induced modulation of effective connectivity between the pre-supplementary motor area, primary motor cortex and putamen when patients suppressed a motor response. Bayesian model selection revealed that patients who later developed levodopa-induced dyskinesias, but not patients without dyskinesias, showed a linear increase in connectivity between the putamen and primary motor cortex after levodopa intake during movement suppression. Individual dyskinesia severity was predicted by levodopa-induced modulation of striato-cortical feedback connections from putamen to the pre-supplementary motor area (Pcorrected = 0.020) and primary motor cortex (Pcorrected = 0.044), but not feed-forward connections from the cortex to the putamen. Our results identify for the first time, aberrant dopaminergic modulation of striatal-cortical connectivity as a neural signature of levodopa-induced dyskinesias in humans. We argue that excessive striato-cortical connectivity in response to levodopa produces an

  16. Targeted enhancement of cortical-hippocampal brain networks and associative memory.

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    Wang, Jane X; Rogers, Lynn M; Gross, Evan Z; Ryals, Anthony J; Dokucu, Mehmet E; Brandstatt, Kelly L; Hermiller, Molly S; Voss, Joel L

    2014-08-29

    The influential notion that the hippocampus supports associative memory by interacting with functionally distinct and distributed brain regions has not been directly tested in humans. We therefore used targeted noninvasive electromagnetic stimulation to modulate human cortical-hippocampal networks and tested effects of this manipulation on memory. Multiple-session stimulation increased functional connectivity among distributed cortical-hippocampal network regions and concomitantly improved associative memory performance. These alterations involved localized long-term plasticity because increases were highly selective to the targeted brain regions, and enhancements of connectivity and associative memory persisted for ~24 hours after stimulation. Targeted cortical-hippocampal networks can thus be enhanced noninvasively, demonstrating their role in associative memory.

  17. Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates.

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    Horvát, Szabolcs; Gămănuț, Răzvan; Ercsey-Ravasz, Mária; Magrou, Loïc; Gămănuț, Bianca; Van Essen, David C; Burkhalter, Andreas; Knoblauch, Kenneth; Toroczkai, Zoltán; Kennedy, Henry

    2016-07-01

    Mammals show a wide range of brain sizes, reflecting adaptation to diverse habitats. Comparing interareal cortical networks across brains of different sizes and mammalian orders provides robust information on evolutionarily preserved features and species-specific processing modalities. However, these networks are spatially embedded, directed, and weighted, making comparisons challenging. Using tract tracing data from macaque and mouse, we show the existence of a general organizational principle based on an exponential distance rule (EDR) and cortical geometry, enabling network comparisons within the same model framework. These comparisons reveal the existence of network invariants between mouse and macaque, exemplified in graph motif profiles and connection similarity indices, but also significant differences, such as fractionally smaller and much weaker long-distance connections in the macaque than in mouse. The latter lends credence to the prediction that long-distance cortico-cortical connections could be very weak in the much-expanded human cortex, implying an increased susceptibility to disconnection syndromes such as Alzheimer disease and schizophrenia. Finally, our data from tracer experiments involving only gray matter connections in the primary visual areas of both species show that an EDR holds at local scales as well (within 1.5 mm), supporting the hypothesis that it is a universally valid property across all scales and, possibly, across the mammalian class.

  18. Altered resting-state functional connectivity in cortical networks in psychopathy.

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    Philippi, Carissa L; Pujara, Maia S; Motzkin, Julian C; Newman, Joseph; Kiehl, Kent A; Koenigs, Michael

    2015-04-15

    Psychopathy is a personality disorder characterized by callous antisocial behavior and criminal recidivism. Here we examine whether psychopathy is associated with alterations in functional connectivity in three large-scale cortical networks. Using fMRI in 142 adult male prison inmates, we computed resting-state functional connectivity using seeds from the default mode network, frontoparietal network, and cingulo-opercular network. To determine the specificity of our findings to these cortical networks, we also calculated functional connectivity using seeds from two comparison primary sensory networks: visual and auditory networks. Regression analyses related network connectivity to overall psychopathy scores and to subscores for the "factors" and "facets" of psychopathy: Factor 1, interpersonal/affective traits; Factor 2, lifestyle/antisocial traits; Facet 1, interpersonal; Facet 2, affective; Facet 3, lifestyle; Facet 4, antisocial. Overall psychopathy severity was associated with reduced functional connectivity between lateral parietal cortex and dorsal anterior cingulate cortex. The two factor scores exhibited contrasting relationships with functional connectivity: Factor 1 scores were associated with reduced functional connectivity in the three cortical networks, whereas Factor 2 scores were associated with heightened connectivity in the same networks. This dissociation was evident particularly in the functional connectivity between anterior insula and dorsal anterior cingulate cortex. The facet scores also demonstrated distinct patterns of connectivity. We found no associations between psychopathy scores and functional connectivity within visual or auditory networks. These findings provide novel evidence on the neural correlates of psychopathy and suggest that connectivity between cortical association hubs, such as the dorsal anterior cingulate cortex, may be a neurobiological marker of the disorder.

  19. Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

    Institute of Scientific and Technical Information of China (English)

    Qin Ying-Mei; Wang Jiang; Men Cong; Zhao Jia; Wei Xi-Le; Deng Bin

    2012-01-01

    Both external and endogenous electrical fields widely exist in the environment of cortical neurons.The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied.It is found that self-sustained rhythmic firing patterns,which are closely correlated with the cognitive functions,are significantly modified due to the self-organizing of the network in the weak AC field.The activities of the neural networks are affected by the synaptic connection strength,the external stimuli,and so on.In the presence of learning rules,the synaptic connections can be modulated by the external stimuli,which will further enhance the sensitivity of the network to the external signal.The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network.

  20. Tetanic stimulation of cortical networks induces parallel memory

    NARCIS (Netherlands)

    Veenendaal, van Tamar; Witteveen, Tim; Feber, le Joost; Akay, M

    2013-01-01

    The mechanisms behind memory have been studied mainly in artificial neural networks. Several mechanisms have been proposed, but it remains unclear yet if and how these findings can be translated to biological networks. Here we unravel part of the mechanism by showing that cultured neuronal networks

  1. Modulation of Cortical-subcortical Networks in Parkinson’s Disease by Applied Field Effects

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    Christopher William Hess

    2013-09-01

    Full Text Available Studies suggest that endogenous field effects may play a role in neuronal oscillations and communication. Non-invasive transcranial electrical stimulation with low-intensity currents can also have direct effects on the underlying cortex as well as distant network effects. While Parkinson's disease (PD is amenable to invasive neuromodulation in the basal ganglia by deep brain stimulation, techniques of non-invasive neuromodulation like transcranial direct current stimulation (tDCS and transcranial alternating current stimulation (tACS are being investigated as possible therapies. tDCS and tACS have the potential to influence the abnormal cortical-subcortical network activity that occurs in PD through sub-threshold changes in cortical excitability or through entrainment or disruption of ongoing rhythmic cortical activity. This may allow for the targeting of specific features of the disease involving abnormal oscillatory activity, as well as the enhancement of potential cortical compensation for basal ganglia dysfunction and modulation of cortical plasticity in neurorehabilitation. However, little is currently known about how cortical stimulation will affect subcortical structures, the size of any effect, and the factors of stimulation that will influence these effects.

  2. A thalamo-cortical neural mass model for the simulation of brain rhythms during sleep.

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    Cona, F; Lacanna, M; Ursino, M

    2014-08-01

    Cortico-thalamic interactions are known to play a pivotal role in many brain phenomena, including sleep, attention, memory consolidation and rhythm generation. Hence, simple mathematical models that can simulate the dialogue between the cortex and the thalamus, at a mesoscopic level, have a great cognitive value. In the present work we describe a neural mass model of a cortico-thalamic module, based on neurophysiological mechanisms. The model includes two thalamic populations (a thalamo-cortical relay cell population, TCR, and its related thalamic reticular nucleus, TRN), and a cortical column consisting of four connected populations (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow and fast kinetics). Moreover, thalamic neurons exhibit two firing modes: bursting and tonic. Finally, cortical synapses among pyramidal neurons incorporate a disfacilitation mechanism following prolonged activity. Simulations show that the model is able to mimic the different patterns of rhythmic activity in cortical and thalamic neurons (beta and alpha waves, spindles, delta waves, K-complexes, slow sleep waves) and their progressive changes from wakefulness to deep sleep, by just acting on modulatory inputs. Moreover, simulations performed by providing short sensory inputs to the TCR show that brain rhythms during sleep preserve the cortex from external perturbations, still allowing a high cortical activity necessary to drive synaptic plasticity and memory consolidation. In perspective, the present model may be used within larger cortico-thalamic networks, to gain a deeper understanding of mechanisms beneath synaptic changes during sleep, to investigate the specific role of brain rhythms, and to explore cortical synchronization achieved via thalamic influences.

  3. The unimodal distribution of sub-threshold, ongoing activity in cortical networks

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    Anat eYaron-Jakoubovitch

    2013-07-01

    Full Text Available The characterization of the subthreshold, ongoing-activity in cortical neurons has been the focus of numerous studies. This activity, described as spontaneous slow waves in membrane potential, has been observed in a span of species in diverse cortical and subcortical areas. We here characterized membrane potential fluctuations in motor and the frontal association cortices cortical neurons of ketamine-xylazine anesthetized rats. We recorded from 95 neurons from a range of cortical depths to unravel the network and cellular mechanisms that shape the subthreshold ongoing spontaneous activity of these neurons. We define a unitary event that generates the sub-threshold ongoing activity: Giant Synaptic Potentials (GSPs. These events have a duration of 87 ± 50 ms and an amplitude of 19 ± 6.4 mV. They occur at a frequency of 3.7 ± 0.8 Hz and involve an increase in conductance change of 22 ± 21%. GSPs are mainly due to excitatory activity that occurs throughout all cortical layers, unaffected by the intrinsic properties of the cells. Indeed, blocking the GABAA receptors, a procedure that had a profound effect on cortical activity, did not alter these unitary events. We propose that this unitary event is composed of individual, excitatory synaptic potentials that appear at different levels of synchrony and that the level of synchrony determines the shape of the subthreshold activity.

  4. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI.

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    He, Yong; Chen, Zhang J; Evans, Alan C

    2007-10-01

    An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.

  5. Molecular Correlates of Cortical Network Modulation by Long-Term Sensory Experience in the Adult Rat Barrel Cortex

    Science.gov (United States)

    Vallès, Astrid; Granic, Ivica; De Weerd, Peter; Martens, Gerard J. M.

    2014-01-01

    Modulation of cortical network connectivity is crucial for an adaptive response to experience. In the rat barrel cortex, long-term sensory stimulation induces cortical network modifications and neuronal response changes of which the molecular basis is unknown. Here, we show that long-term somatosensory stimulation by enriched environment…

  6. Reconstitution of cortical actin networks within water-in-oil emulsions.

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    Abu Shah, Enas; Malik-Garbi, Maya; Keren, Kinneret

    2015-01-01

    We describe the reconstitution of dynamic cortical actin networks within cell-like compartments. The approach is based on encapsulation of Xenopus egg extract within water-in-oil emulsions. The growth of cortical actin networks is catalyzed by an amphiphilic actin nucleation-promoting factor that localizes to the water/oil interface. We first describe the preparation of cell-free Xenopus egg extract that provides all the soluble components of the actin machinery. We then describe the preparation of the amphiphilic, fluorescent actin nucleation-promoting factor that directs actin polymerization to the interface. Finally, we describe the steps required for assembly of dynamic actin cortices within water-in-oil emulsions, including the emulsification process and the sample preparation procedures. We provide recommendations for handling sensitive system components and discuss potential uses of this reconstitution approach for cytoskeletal research. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function.

    Science.gov (United States)

    Lv, Jinglei; Jiang, Xi; Li, Xiang; Zhu, Dajiang; Zhang, Shu; Zhao, Shijie; Chen, Hanbo; Zhang, Tuo; Hu, Xintao; Han, Junwei; Ye, Jieping; Guo, Lei; Liu, Tianming

    2015-04-01

    For decades, it has been largely unknown to what extent multiple functional networks spatially overlap/interact with each other and jointly realize the total cortical function. Here, by developing novel sparse representation of whole-brain fMRI signals and by using the recently publicly released large-scale Human Connectome Project high-quality fMRI data, we show that a number of reproducible and robust functional networks, including both task-evoked and resting state networks, are simultaneously distributed in distant neuroanatomic areas and substantially spatially overlapping with each other, thus forming an initial collection of holistic atlases of functional networks and interactions (HAFNIs). More interestingly, the HAFNIs revealed two distinct patterns of highly overlapped regions and highly specialized regions and exhibited that these two patterns of areas are reciprocally localized, revealing a novel organizational principle of cortical function.

  8. Cortical Thinning in Network-Associated Regions in Cognitively Normal and Below-Normal Range Schizophrenia

    Science.gov (United States)

    Pinnock, Farena; Parlar, Melissa; Hawco, Colin; Hanford, Lindsay; Hall, Geoffrey B.

    2017-01-01

    This study assessed whether cortical thickness across the brain and regionally in terms of the default mode, salience, and central executive networks differentiates schizophrenia patients and healthy controls with normal range or below-normal range cognitive performance. Cognitive normality was defined using the MATRICS Consensus Cognitive Battery (MCCB) composite score (T = 50 ± 10) and structural magnetic resonance imaging was used to generate cortical thickness data. Whole brain analysis revealed that cognitively normal range controls (n = 39) had greater cortical thickness than both cognitively normal (n = 17) and below-normal range (n = 49) patients. Cognitively normal controls also demonstrated greater thickness than patients in regions associated with the default mode and salience, but not central executive networks. No differences on any thickness measure were found between cognitively normal range and below-normal range controls (n = 24) or between cognitively normal and below-normal range patients. In addition, structural covariance between network regions was high and similar across subgroups. Positive and negative symptom severity did not correlate with thickness values. Cortical thinning across the brain and regionally in relation to the default and salience networks may index shared aspects of the psychotic psychopathology that defines schizophrenia with no relation to cognitive impairment.

  9. Age- and gender-related differences in the cortical anatomical network.

    Science.gov (United States)

    Gong, Gaolang; Rosa-Neto, Pedro; Carbonell, Felix; Chen, Zhang J; He, Yong; Evans, Alan C

    2009-12-16

    Neuroanatomical differences attributable to aging and gender have been well documented, and these differences may be associated with differences in behaviors and cognitive performance. However, little is known about the dynamic organization of anatomical connectivity within the cerebral cortex, which may underlie population differences in brain function. In this study, we investigated age and sex effects on the anatomical connectivity patterns of 95 normal subjects ranging in age from 19 to 85 years. Using the connectivity probability derived from diffusion magnetic resonance imaging tractography, we characterized the cerebral cortex as a weighted network of connected regions. This approach captures the underlying organization of anatomical connectivity for each subject at a regional level. Advanced graph theoretical analysis revealed that the resulting cortical networks exhibited "small-world" character (i.e., efficient information transfer both at local and global scale). In particular, the precuneus and posterior cingulate gyrus were consistently observed as centrally connected regions, independent of age and sex. Additional analysis revealed a reduction in overall cortical connectivity with age. There were also changes in the underlying network organization that resulted in decreased local efficiency, and also a shift of regional efficiency from the parietal and occipital to frontal and temporal neocortex in older brains. In addition, women showed greater overall cortical connectivity and the underlying organization of their cortical networks was more efficient, both locally and globally. There were also distributed regional differences in efficiency between sexes. Our results provide new insights into the substrates that underlie behavioral and cognitive differences in aging and sex.

  10. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentiall...

  11. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentiall...

  12. Cortical Thinning in Network-Associated Regions in Cognitively Normal and Below-Normal Range Schizophrenia

    Directory of Open Access Journals (Sweden)

    R. Walter Heinrichs

    2017-01-01

    Full Text Available This study assessed whether cortical thickness across the brain and regionally in terms of the default mode, salience, and central executive networks differentiates schizophrenia patients and healthy controls with normal range or below-normal range cognitive performance. Cognitive normality was defined using the MATRICS Consensus Cognitive Battery (MCCB composite score (T=50 ± 10 and structural magnetic resonance imaging was used to generate cortical thickness data. Whole brain analysis revealed that cognitively normal range controls (n=39 had greater cortical thickness than both cognitively normal (n=17 and below-normal range (n=49 patients. Cognitively normal controls also demonstrated greater thickness than patients in regions associated with the default mode and salience, but not central executive networks. No differences on any thickness measure were found between cognitively normal range and below-normal range controls (n=24 or between cognitively normal and below-normal range patients. In addition, structural covariance between network regions was high and similar across subgroups. Positive and negative symptom severity did not correlate with thickness values. Cortical thinning across the brain and regionally in relation to the default and salience networks may index shared aspects of the psychotic psychopathology that defines schizophrenia with no relation to cognitive impairment.

  13. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Science.gov (United States)

    El Boustani, Sami; Marre, Olivier; Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

    2009-09-01

    Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured

  14. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Directory of Open Access Journals (Sweden)

    Sami El Boustani

    2009-09-01

    Full Text Available Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population

  15. Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity

    Directory of Open Access Journals (Sweden)

    Bryan M Krause

    2014-09-01

    Full Text Available The state of the sensory cortical network can have a profound impact on neural responses and perception. In rodent auditory cortex, sensory responses are reported to occur in the context of network events, similar to brief UP states, that produce 'packets' of spikes and are associated with synchronized synaptic input (Bathellier et al., 2012; Hromadka et al., 2013; Luczak et al., 2013. However, traditional models based on data from visual and somatosensory cortex predict that ascending sensory thalamocortical (TC pathways sequentially activate cells in layers 4 (L4, L2/3 and L5. The relationship between these two spatio-temporal activity patterns is unclear. Here, we used calcium imaging and electrophysiological recordings in murine auditory TC brain slices to investigate the laminar response pattern to stimulation of TC afferents. We show that although monosynaptically driven spiking in response to TC afferents occurs, the vast majority of spikes fired following TC stimulation occurs during brief UP states and outside the context of the L4>L2/3>L5 activation sequence. Specifically, monosynaptic subthreshold TC responses with similar latencies were observed throughout layers 2 - 6, presumably via synapses onto dendritic processes located in L3 & L4. However, monosynaptic spiking was rare, and occurred primarily in L4 and L5 non-pyramidal cells. By contrast, during brief, TC-induced UP states, spiking was dense and occurred primarily in pyramidal cells. These network events always involved infragranular layers, whereas involvement of supragranular layers was variable. During UP states, spike latencies were comparable between infragranular and supragranular cells. These data are consistent with a model in which activation of auditory cortex, especially supragranular layers, depends on internally generated network events that represent a nonlinear amplification process, are initiated by infragranular cells and tightly regulated by feed

  16. Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks.

    Science.gov (United States)

    Schwiedrzik, Caspar M; Ruff, Christian C; Lazar, Andreea; Leitner, Frauke C; Singer, Wolf; Melloni, Lucia

    2014-05-01

    Perception is an active inferential process in which prior knowledge is combined with sensory input, the result of which determines the contents of awareness. Accordingly, previous experience is known to help the brain "decide" what to perceive. However, a critical aspect that has not been addressed is that previous experience can exert 2 opposing effects on perception: An attractive effect, sensitizing the brain to perceive the same again (hysteresis), or a repulsive effect, making it more likely to perceive something else (adaptation). We used functional magnetic resonance imaging and modeling to elucidate how the brain entertains these 2 opposing processes, and what determines the direction of such experience-dependent perceptual effects. We found that although affecting our perception concurrently, hysteresis and adaptation map into distinct cortical networks: a widespread network of higher-order visual and fronto-parietal areas was involved in perceptual stabilization, while adaptation was confined to early visual areas. This areal and hierarchical segregation may explain how the brain maintains the balance between exploiting redundancies and staying sensitive to new information. We provide a Bayesian model that accounts for the coexistence of hysteresis and adaptation by separating their causes into 2 distinct terms: Hysteresis alters the prior, whereas adaptation changes the sensory evidence (the likelihood function).

  17. Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing

    Science.gov (United States)

    Clawson, Wesley Patrick

    Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.

  18. Visual cortical mechanisms of perceptual grouping: interacting layers, networks, columns, and maps.

    Science.gov (United States)

    Ross, W D; Grossberg, S; Mingolla, E

    2000-07-01

    The visual cortex has a laminar organization whose circuits form functional columns in cortical maps. How this laminar architecture supports visual percepts is not well understood. A neural model proposes how the laminar circuits of V1 and V2 generate perceptual groupings that maintain sensitivity to the contrasts and spatial organization of scenic cues. The model can decisively choose which groupings cohere and survive, even while balanced excitatory and inhibitory interactions preserve contrast-sensitive measures of local boundary likelihood or strength. In the model, excitatory inputs from lateral geniculate nucleus (LGN) activate layers 4 and 6 of V1. Layer 6 activates an on-center off-surround network of inputs to layer 4. Together these layer 4 inputs preserve analog sensitivity to LGN input contrasts. Layer 4 cells excite pyramidal cells in layer 2/3, which activate monosynaptic long-range horizontal excitatory connections between layer 2/3 pyramidal cells, and short-range disynaptic inhibitory connections mediated by smooth stellate cells. These interactions support inward perceptual grouping between two or more boundary inducers, but not outward grouping from a single inducer. These boundary signals feed back to layer 4 via the layer 6-to-4 on-center off-surround network. This folded feedback joins cells in different layers into functional columns while selecting winning groupings. Layer 6 in V1 also sends top-down signals to LGN using an on-center off-surround network, which suppresses LGN cells that do not receive feedback, while selecting, enhancing, and synchronizing activity of those that do. The model is used to simulate psychophysical and neurophysiological data about perceptual grouping, including various Gestalt grouping laws.

  19. Frontotemporal dementia and language networks: cortical thickness reduction is driven by dyslexia susceptibility genes

    Science.gov (United States)

    Paternicó, Donata; Manes, Marta; Premi, Enrico; Cosseddu, Maura; Gazzina, Stefano; Alberici, Antonella; Archetti, Silvana; Bonomi, Elisa; Cotelli, Maria Sofia; Cotelli, Maria; Turla, Marinella; Micheli, Anna; Gasparotti, Roberto; Padovani, Alessandro; Borroni, Barbara

    2016-01-01

    Variations within genes associated with dyslexia result in a language network vulnerability, and in patients with Frontotemporal Dementia (FTD), language disturbances represent a disease core feature. Here we explored whether variations within three related-dyslexia genes, namely KIAA0319, DCDC2, and CNTNAP, might affect cortical thickness measures in FTD patients. 112 FTD patients underwent clinical and neuropsychological examination, genetic analyses and brain Magnetic Resonance Imaging (MRI). KIAA0319 rs17243157 G/A, DCDC2 rs793842 A/G and CNTNAP2 rs17236239 A/G genetic variations were assessed. Cortical thickness was analysed by Freesurfer. Patients carrying KIAA0319 A*(AG or AA) carriers showed greater cortical thickness atrophy in the left fusiform and inferior temporal gyri, compared to KIAA0319 GG (p ≤ 0.001). Patients carrying CNTNAP2 G*(GA or GG) showed reduced cortical thickness in the left insula thenCNTNAP2 AA carriers (p≤0.001). When patients with both at-risk polymorphisms were considered (KIAA0319 A* and CNTNAP2 G*), greater and addictive cortical thickness atrophy of the left insula and the inferior temporal gyrus was demonstrated (p ≤ 0.001). No significant effect of DCDC2 was found. In FTD, variations of KIAA0319 and CNTNAP2 genes were related to cortical thickness abnormalities in those brain areas involved in language abilities. These findings shed light on genetic predisposition in defining phenotypic variability in FTD. PMID:27484312

  20. Self-organizing model of motor cortical activities during drawing

    Science.gov (United States)

    Lin, Siming H.; Si, Jennie; Schwartz, Andrew B.

    1996-05-01

    The population vector algorithm has been developed to combine the simultaneous direction- related activities of a population of motor cortical neurons to predict the trajectory of the arm movement. In our study, we consider a self-organizing model of a neural representation of the arm trajectory based on neuronal discharge rates. Self-organizing feature mapping (SOFM) is used to select the optimal set of weights in the model to determine the contribution of individual neuron to the overall movement. The correspondence between the movement directions and the discharge patterns of the motor cortical neurons is established in the output map. The topology preserving property of the SOFM is used to analyze real recorded data of a behavior monkey. The data used in this analysis were taken while the monkey was drawing spirals and doing the center out movement. Using such a statistical model, the monkey's arm moving directions could be well predicted based on the motor cortex neuronal firing information.

  1. Cortical morphometry in frontoparietal and default mode networks in math-gifted adolescents.

    Science.gov (United States)

    Navas-Sánchez, Francisco J; Carmona, Susana; Alemán-Gómez, Yasser; Sánchez-González, Javier; Guzmán-de-Villoria, Juan; Franco, Carolina; Robles, Olalla; Arango, Celso; Desco, Manuel

    2016-05-01

    Math-gifted subjects are characterized by above-age performance in intelligence tests, exceptional creativity, and high task commitment. Neuroimaging studies reveal enhanced functional brain organization and white matter microstructure in the frontoparietal executive network of math-gifted individuals. However, the cortical morphometry of these subjects remains largely unknown. The main goal of this study was to compare the cortical morphometry of math-gifted adolescents with that of an age- and IQ-matched control group. We used surface-based methods to perform a vertex-wise analysis of cortical thickness and surface area. Our results show that math-gifted adolescents present a thinner cortex and a larger surface area in key regions of the frontoparietal and default mode networks, which are involved in executive processing and creative thinking, respectively. The combination of reduced cortical thickness and larger surface area suggests above-age neural maturation of these networks in math-gifted individuals. Hum Brain Mapp 37:1893-1902, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network.

    Science.gov (United States)

    Amiri, Masoud; Amiri, Mahmood; Nazari, Soheila; Faez, Karim

    2016-12-07

    Hyper-synchronous neural oscillations are the character of several neurological diseases such as epilepsy. On the other hand, glial cells and particularly astrocytes can influence neural synchronization. Therefore, based on the recent researches, a new bio-inspired stimulator is proposed which basically is a dynamical model of the astrocyte biophysical model. The performance of the new stimulator is investigated on a large-scale, cortical network. Both excitatory and inhibitory synapses are also considered in the simulated spiking neural network. The simulation results show that the new stimulator has a good performance and is able to reduce recurrent abnormal excitability which in turn avoids the hyper-synchronous neural firing in the spiking neural network. In this way, the proposed stimulator has a demand controlled characteristic and is a good candidate for deep brain stimulation (DBS) technique to successfully suppress the neural hyper-synchronization. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters.

    Science.gov (United States)

    Denève, Sophie; Duhamel, Jean-René; Pouget, Alexandre

    2007-05-23

    Several behavioral experiments suggest that the nervous system uses an internal model of the dynamics of the body to implement a close approximation to a Kalman filter. This filter can be used to perform a variety of tasks nearly optimally, such as predicting the sensory consequence of motor action, integrating sensory and body posture signals, and computing motor commands. We propose that the neural implementation of this Kalman filter involves recurrent basis function networks with attractor dynamics, a kind of architecture that can be readily mapped onto cortical circuits. In such networks, the tuning curves to variables such as arm velocity are remarkably noninvariant in the sense that the amplitude and width of the tuning curves of a given neuron can vary greatly depending on other variables such as the position of the arm or the reliability of the sensory feedback. This property could explain some puzzling properties of tuning curves in the motor and premotor cortex, and it leads to several new predictions.

  4. Causal hierarchy within the thalamo-cortical network in spike and wave discharges.

    Directory of Open Access Journals (Sweden)

    Anna E Vaudano

    Full Text Available BACKGROUND: Generalised spike wave (GSW discharges are the electroencephalographic (EEG hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges. METHODOLOGY AND PRINCIPAL FINDINGS: We analysed fMRI data from seven patients affected by Idiopathic Generalized Epilepsy (IGE with frequent GSW discharges and significant GSW-correlated haemodynamic signal changes in the thalamus, the prefrontal cortex and the precuneus. Using DCM we assessed their effective connectivity, i.e. which region drives another region. Three dynamic causal models were constructed: GSW was modelled as autonomous input to the thalamus (model A, ventromedial prefrontal cortex (model B, and precuneus (model C. Bayesian model comparison revealed Model C (GSW as autonomous input to precuneus, to be the best in 5 patients while model A prevailed in two cases. At the group level model C dominated and at the population-level the p value of model C was approximately 1. CONCLUSION: Our results provide strong evidence that activity in the precuneus gates GSW discharges in the thalamo-(fronto cortical network. This study is the first demonstration of a causal link between haemodynamic changes in the precuneus -- an index of awareness -- and the occurrence of pathological discharges in epilepsy.

  5. Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons.

    Science.gov (United States)

    Ju, Han; Dranias, Mark R; Banumurthy, Gokulakrishna; VanDongen, Antonius M J

    2015-03-04

    The ability to process complex spatiotemporal information is a fundamental process underlying the behavior of all higher organisms. However, how the brain processes information in the temporal domain remains incompletely understood. We have explored the spatiotemporal information-processing capability of networks formed from dissociated rat E18 cortical neurons growing in culture. By combining optogenetics with microelectrode array recording, we show that these randomly organized cortical microcircuits are able to process complex spatiotemporal information, allowing the identification of a large number of temporal sequences and classification of musical styles. These experiments uncovered spatiotemporal memory processes lasting several seconds. Neural network simulations indicated that both short-term synaptic plasticity and recurrent connections are required for the emergence of this capability. Interestingly, NMDA receptor function is not a requisite for these short-term spatiotemporal memory processes. Indeed, blocking the NMDA receptor with the antagonist APV significantly improved the temporal processing ability of the networks, by reducing spontaneously occurring network bursts. These highly synchronized events have disastrous effects on spatiotemporal information processing, by transiently erasing short-term memory. These results show that the ability to process and integrate complex spatiotemporal information is an intrinsic property of generic cortical networks that does not require specifically designed circuits. Copyright © 2015 the authors 0270-6474/15/354040-12$15.00/0.

  6. Rational Design of Transcranial Current Stimulation (TCS through Mechanistic Insights into Cortical Network Dynamics

    Directory of Open Access Journals (Sweden)

    Flavio eFrohlich

    2013-11-01

    Full Text Available Transcranial current stimulation (TCS is a promising method of non-invasive brain stimulation to modulate cortical network dynamics. Preliminary studies have demonstrated the ability of TCS to enhance cognition and reduce symptoms in both neurological and psychiatric illnesses. Despite the encouraging results of these studies, the mechanisms by which TCS and endogenous network dynamics interact remain poorly understood. Here, we propose that the development of the next generation of TCS paradigms with increased efficacy requires such mechanistic understanding of how weak electric fields imposed by TCS interact with the nonlinear dynamics of large-scale cortical networks. We highlight key recent advances in the study of the interaction dynamics between TCS and cortical network activity. In particular, we demonstrate the opportunities provided by an interdisciplinary approach that bridges neurobiology and electrical engineering. We discuss the use of (1 hybrid biological-electronic experimental approaches to disentangle feedback interactions, (2 large-scale computer simulations for the study of weak global perturbations imposed by TCS, and (3 optogenetic manipulations informed by dynamics systems theory to probe network dynamics. Together, we here provide the foundation for the use of rational design for the development of the next generation of TCS neurotherapeutics.

  7. Dysfunction and dysconnection in cortical-striatal networks during sustained attention: Genetic risk for schizophrenia or bipolar disorder and its impact on brain network function

    Directory of Open Access Journals (Sweden)

    Vaibhav A. Diwadkar

    2014-05-01

    Full Text Available Abnormalities in the brain’s attention network may represent early identifiable neurobiological impairments in individuals at increased risk for schizophrenia or bipolar disorder. Here we provide evidence of dysfunctional regional and network function in adolescents at higher genetic risk for schizophrenia or bipolar disorder (henceforth HGR. During fMRI, participants engaged in a sustained attention task with variable demands. The task alternated between attention (120 s, visual control (passive viewing; 120 s and rest (20 s epochs. Low and high demand attention conditions were created using the rapid presentation of 2- or 3-digit numbers. Subjects were required to detect repeated presentation of numbers. We demonstrate that the recruitment of cortical and striatal regions are disordered in HGR: Relative to typical controls (TC, HGR showed lower recruitment of the dorsal prefrontal cortex, but higher recruitment of the superior parietal cortex. This imbalance was more dramatic in the basal ganglia. There, a group by task demand interaction was observed, such that increased attention demand led to increased engagement in TC, but disengagement in HGR. These activation studies were complemented by network analyses using Dynamic Causal Modeling. Competing model architectures were assessed across a network of cortical-striatal regions, distinguished at a second level using random effects Bayesian model selection. In the winning architecture, HGR were characterized by significant reductions in coupling across both frontal-striatal and frontal-parietal pathways. The effective connectivity analyses indicate emergent network dysconnection, consistent with findings in patients with schizophrenia. Emergent patterns of regional dysfunction and disconnection in cortical-striatal pathways may provide functional biological signatures in the adolescent risk state for psychiatric illness.

  8. Self-organized Critical Model Based on Complex Brain Networks with Hierarchical Organization

    Institute of Scientific and Technical Information of China (English)

    ZHANG Ying-Yue; ZHANG Gui-Qing; YANG Qiu-Ying; CHEN Tian-Lun

    2008-01-01

    The dynamical behavior in the cortical brain network of macaque is studied by modelling each cortical area with a subnetwork of interacting excitable neurons.We find that the avalanche of our model on different levels exhibits power-law.Furthermore the power-law exponent of the distribution and the average avalanche Size are affected by the topology of the network.

  9. The causal inference of cortical neural networks during music improvisations.

    Science.gov (United States)

    Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft

    2014-01-01

    We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.

  10. The causal inference of cortical neural networks during music improvisations.

    Directory of Open Access Journals (Sweden)

    Xiaogeng Wan

    Full Text Available We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME, to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.

  11. Beyond Scale-Free Small-World Networks: Cortical Columns for Quick Brains

    Science.gov (United States)

    Stoop, Ralph; Saase, Victor; Wagner, Clemens; Stoop, Britta; Stoop, Ruedi

    2013-03-01

    We study to what extent cortical columns with their particular wiring boost neural computation. Upon a vast survey of columnar networks performing various real-world cognitive tasks, we detect no signs of enhancement. It is on a mesoscopic—intercolumnar—scale that the existence of columns, largely irrespective of their inner organization, enhances the speed of information transfer and minimizes the total wiring length required to bind distributed columnar computations towards spatiotemporally coherent results. We suggest that brain efficiency may be related to a doubly fractal connectivity law, resulting in networks with efficiency properties beyond those by scale-free networks.

  12. Dynamic Network Models

    CERN Document Server

    Armbruster, Benjamin

    2011-01-01

    We analyze random networks that change over time. First we analyze a dynamic Erdos-Renyi model, whose edges change over time. We describe its stationary distribution, its convergence thereto, and the SI contact process on the network, which has relevance for connectivity and the spread of infections. Second, we analyze the effect of node turnover, when nodes enter and leave the network, which has relevance for network models incorporating births, deaths, aging, and other demographic factors.

  13. Self-sustained asynchronous irregular states and Up-Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons.

    Science.gov (United States)

    Destexhe, Alain

    2009-12-01

    Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. However, it is not clear whether such activity states are specific to simple IF models, or if they also exist in networks where neurons are endowed with complex intrinsic properties similar to electrophysiological measurements. Here, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models. AI states can be found in each case, sometimes with surprisingly small network size of the order of a few tens of neurons. We show that the presence of LTS neurons in cortex or in thalamus, explains the robust emergence of AI states for relatively small network sizes. Finally, we investigate the role of spike-frequency adaptation (SFA). In cortical networks with strong SFA in RS cells, the AI state is transient, but when SFA is reduced, AI states can be self-sustained for long times. In thalamocortical networks, AI states are found when the cortex is itself in an AI state, but with strong SFA, the thalamocortical network displays Up and Down state transitions, similar to intracellular recordings during slow-wave sleep or anesthesia. Self-sustained Up and Down states could also be generated by two-layer cortical networks with LTS cells. These models suggest that intrinsic properties such as adaptation and low-threshold bursting activity are crucial for the genesis and control of AI states in thalamocortical networks.

  14. The Cortical Network for Braille Writing in the Blind.

    Science.gov (United States)

    Likova, Lora T; Tyler, Christopher W; Cacciamani, Laura; Mineff, Kristyo; Nicholas, Spero

    2016-01-01

    Fundamental forms of high-order cognition, such as reading and writing, are usually studied in the context of one modality - vision. People without sight, however, use the kinesthetic-based Braille writing, and haptic-based Braille reading. We asked whether the cognitive and motor control mechanisms underlying writing and reading are modality-specific or supramodal. While a number of previous functional Magnetic Resonance Imaging (fMRI) studies have investigated the brain network for Braille reading in the blind, such studies on Braille writing are lacking. Consequently, no comparative network analysis of Braille writing vs. reading exists. Here, we report the first study of Braille writing, and a comparison of the brain organization for Braille writing vs Braille reading. FMRI was conducted in a Siemens 3T Trio scanner. Our custom MRI-compatible drawing/writing lectern was further modified to provide for Braille reading and writing. Each of five paragraphs of novel Braille text describing objects, faces and navigation sequences was read, then reproduced twice by Braille writing from memory, then read a second time. During Braille reading, the haptic-sensing of the Braille letters strongly activated not only the early visual area V1 and V2, but some highly specialized areas, such as the classical visual grapheme area and the Exner motor grapheme area. Braille-writing-from-memory, engaged a significantly more extensive network in dorsal motor, somatosensory/kinesthetic, dorsal parietal and prefrontal cortex. However, in contrast to the largely extended V1 activation in drawing-from-memory in the blind after training (Likova, 2012), Braille writing from memory generated focal activation restricted to the most foveal part of V1, presumably reflecting topographically the focal demands of such a "pin-pricking" task.

  15. Characterization of Cortical Networks and Corticocortical Functional Connectivity Mediating Arbitrary Visuomotor Mapping.

    Science.gov (United States)

    Brovelli, Andrea; Chicharro, Daniel; Badier, Jean-Michel; Wang, Huifang; Jirsa, Viktor

    2015-09-16

    Adaptive behaviors are built on the arbitrary linkage of sensory inputs to actions and goals. Although the sensorimotor and associative frontostriatal circuits are known to mediate arbitrary visuomotor mappings, the underlying corticocortico dynamics remain elusive. Here, we take a novel approach exploiting gamma-band neural activity to study the human cortical networks and corticocortical functional connectivity mediating arbitrary visuomotor mapping. Single-trial gamma-power time courses were estimated for all Brodmann areas by combing magnetoencephalographic and MRI data with spectral analysis and beam-forming techniques. Linear correlation and Granger causality analyses were performed to investigate functional connectivity between cortical regions. The performance of visuomotor associations was characterized by an increase in gamma-power and functional connectivity over the sensorimotor and frontoparietal network, in addition to medial prefrontal areas. The superior parietal area played a driving role in the network, exerting Granger causality on the dorsal premotor area. Premotor areas acted as relay from parietal to medial prefrontal cortices, which played a receiving role in the network. Link community analysis further revealed that visuomotor mappings reflect the coordination of multiple subnetworks with strong overlap over motor and frontoparietal areas. We put forward an associative account of the underlying cognitive processes and corticocortical functional connectivity. Overall, our approach and results provide novel perspectives toward a better understanding of how distributed brain activity coordinates adaptive behaviors. In everyday life, most of our behaviors are based on the arbitrary linkage of sensory information to actions and goals, such as stopping at a red traffic light. Despite their automaticity, such behaviors rely on the activity of a large brain network and elusive interareal functional connectivity. We take a novel approach exploiting

  16. Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture.

    Science.gov (United States)

    Knight, James C; Furber, Steve B

    2016-01-01

    While the adult human brain has approximately 8.8 × 10(10) neurons, this number is dwarfed by its 1 × 10(15) synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows 4× more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously.

  17. Synapse-centric mapping of cortical models to the SpiNNaker neuromorphic architecture

    Directory of Open Access Journals (Sweden)

    James Courtney Knight

    2016-09-01

    Full Text Available While the adult human brain has approximately 8.8x10^10 neurons, this number is dwarfed by its 1x10^15 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously.

  18. A Possible Role of Prolonged Whirling Episodes on Structural Plasticity of the Cortical Networks and Altered Vertigo Perception: The Cortex of Sufi Whirling Dervishes

    Science.gov (United States)

    Cakmak, Yusuf O.; Ekinci, Gazanfer; Heinecke, Armin; Çavdar, Safiye

    2017-01-01

    Although minutes of a spinning episode may induce vertigo in the healthy human, as a result of a possible perceptional plasticity, Sufi Whirling Dervishes (SWDs) can spin continuously for an hour without a vertigo perception.This unique long term vestibular system stimulation presents a potential human model to clarify the cortical networks underlying the resistance against vertigo. This study, therefore, aimed to investigate the potential structural cortical plasticity in SWDs. Magnetic resonance imaging (MRI) of 10 SWDs and 10 controls were obtained, using a 3T scanner. Cortical thickness in the whole cortex was calculated. Results demonstrated significantly thinner cortical areas for SWD subjects compared with the control group in the hubs of the default mode network (DMN), as well as in the motion perception and discrimination areas including the right dorsolateral prefrontal cortex (DLPFC), the right lingual gyrus and the left visual area 5 (V5)/middle temporal (MT) and the left fusiform gyrus. In conclusion, this is the first report that warrants the potential relationship of the motion/body perception related cortical networks and the prolonged term of whirling ability without vertigo or dizziness. PMID:28167905

  19. Mathematically gifted adolescents mobilize enhanced workspace configuration of theta cortical network during deductive reasoning.

    Science.gov (United States)

    Zhang, L; Gan, J Q; Wang, H

    2015-03-19

    Previous studies have established the importance of the fronto-parietal brain network in the information processing of reasoning. At the level of cortical source analysis, this eletroencepalogram (EEG) study investigates the functional reorganization of the theta-band (4-8Hz) neurocognitive network of mathematically gifted adolescents during deductive reasoning. Depending on the dense increase of long-range phase synchronizations in the reasoning process, math-gifted adolescents show more significant adaptive reorganization and enhanced "workspace" configuration in the theta network as compared with average-ability control subjects. The salient areas are mainly located in the anterior cortical vertices of the fronto-parietal network. Further correlation analyses have shown that the enhanced workspace configuration with respect to the global topological metrics of the theta network in math-gifted subjects is correlated with the intensive frontal midline theta (fm theta) response that is related to strong neural effort for cognitive events. These results suggest that by investing more cognitive resources math-gifted adolescents temporally mobilize an enhanced task-related global neuronal workspace, which is manifested as a highly integrated fronto-parietal information processing network during the reasoning process.

  20. Altered modular organization of structural cortical networks in children with autism.

    Directory of Open Access Journals (Sweden)

    Feng Shi

    Full Text Available Autism is a complex developmental disability that characterized by deficits in social interaction, language skills, repetitive stereotyped behaviors and restricted interests. Although great heterogeneity exists, previous findings suggest that autism has atypical brain connectivity patterns and disrupted small-world network properties. However, the organizational alterations in the autistic brain network are still poorly understood. We explored possible organizational alterations of 49 autistic children and 51 typically developing controls, by investigating their brain network metrics that are constructed upon cortical thickness correlations. Three modules were identified in controls, including cortical regions associated with brain functions of executive strategic, spatial/auditory/visual, and self-reference/episodic memory. There are also three modules found in autistic children with similar patterns. Compared with controls, autism demonstrates significantly reduced gross network modularity, and a larger number of inter-module connections. However, the autistic brain network demonstrates increased intra- and inter-module connectivity in brain regions including middle frontal gyrus, inferior parietal gyrus, and cingulate, suggesting one underlying compensatory mechanism associated with brain functions of self-reference and episodic memory. Results also show that there is increased correlation strength between regions inside frontal lobe, as well as impaired correlation strength between frontotemporal and frontoparietal regions. This alteration of correlation strength may contribute to the organization alteration of network structures in autistic brains.

  1. Neuronal networks and mediators of cortical neurovascular coupling responses in normal and altered brain states.

    Science.gov (United States)

    Lecrux, C; Hamel, E

    2016-10-05

    Brain imaging techniques that use vascular signals to map changes in neuronal activity, such as blood oxygenation level-dependent functional magnetic resonance imaging, rely on the spatial and temporal coupling between changes in neurophysiology and haemodynamics, known as 'neurovascular coupling (NVC)'. Accordingly, NVC responses, mapped by changes in brain haemodynamics, have been validated for different stimuli under physiological conditions. In the cerebral cortex, the networks of excitatory pyramidal cells and inhibitory interneurons generating the changes in neural activity and the key mediators that signal to the vascular unit have been identified for some incoming afferent pathways. The neural circuits recruited by whisker glutamatergic-, basal forebrain cholinergic- or locus coeruleus noradrenergic pathway stimulation were found to be highly specific and discriminative, particularly when comparing the two modulatory systems to the sensory response. However, it is largely unknown whether or not NVC is still reliable when brain states are altered or in disease conditions. This lack of knowledge is surprising since brain imaging is broadly used in humans and, ultimately, in conditions that deviate from baseline brain function. Using the whisker-to-barrel pathway as a model of NVC, we can interrogate the reliability of NVC under enhanced cholinergic or noradrenergic modulation of cortical circuits that alters brain states.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

  2. Modeling worldwide highway networks

    Science.gov (United States)

    Villas Boas, Paulino R.; Rodrigues, Francisco A.; da F. Costa, Luciano

    2009-12-01

    This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model.

  3. Bayesian network classifiers for categorizing cortical GABAergic interneurons.

    Science.gov (United States)

    Mihaljević, Bojan; Benavides-Piccione, Ruth; Bielza, Concha; DeFelipe, Javier; Larrañaga, Pedro

    2015-04-01

    An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons.

  4. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.

    Directory of Open Access Journals (Sweden)

    Peng Fang

    Full Text Available Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients=86.4%, controls=96.2%; permutation test, p<0.0001 of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.

  5. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  6. Optogenetic stimulation of a meso-scale human cortical model

    Science.gov (United States)

    Selvaraj, Prashanth; Szeri, Andrew; Sleigh, Jamie; Kirsch, Heidi

    2015-03-01

    Neurological phenomena like sleep and seizures depend not only on the activity of individual neurons, but on the dynamics of neuron populations as well. Meso-scale models of cortical activity provide a means to study neural dynamics at the level of neuron populations. Additionally, they offer a safe and economical way to test the effects and efficacy of stimulation techniques on the dynamics of the cortex. Here, we use a physiologically relevant meso-scale model of the cortex to study the hypersynchronous activity of neuron populations during epileptic seizures. The model consists of a set of stochastic, highly non-linear partial differential equations. Next, we use optogenetic stimulation to control seizures in a hyperexcited cortex, and to induce seizures in a normally functioning cortex. The high spatial and temporal resolution this method offers makes a strong case for the use of optogenetics in treating meso scale cortical disorders such as epileptic seizures. We use bifurcation analysis to investigate the effect of optogenetic stimulation in the meso scale model, and its efficacy in suppressing the non-linear dynamics of seizures.

  7. Activity Changes Induced by Spatio-Temporally Correlated Stimuli in Cultured Cortical Networks

    Science.gov (United States)

    Takayama, Yuzo; Moriguchi, Hiroyuki; Jimbo, Yasuhiko

    Activity-dependent plasticity probably plays a key role in learning and memory in biological information processing systems. Though long-term potentiation and depression have been extensively studied in the filed of neuroscience, little is known on the mechanisms for integrating these modifications on network-wide activity changes. In this report, we studied effects of spatio-temporally correlated stimuli on the neuronal network activity. Rat cortical neurons were cultured on substrates with 64 embedded micro-electrodes and the evoked responses were extracellularly recorded and analyzed. We compared spatio-temporal patterns of the responses between before and after repetitive application of correlated stimuli. After the correlated stimuli, the networks showed significantly different responses from those in the initial states. The modified activity reflected structures of the repeatedly applied correlated stimuli. The results suggested that spatiotemporally correlated inputs systematically induced modification of synaptic strengths in neuronal networks, which could serve as an underlying mechanism of associative memory.

  8. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

  9. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  10. Altered Cortical Ensembles in Mouse Models of Schizophrenia.

    Science.gov (United States)

    Hamm, Jordan P; Peterka, Darcy S; Gogos, Joseph A; Yuste, Rafael

    2017-04-05

    In schizophrenia, brain-wide alterations have been identified at the molecular and cellular levels, yet how these phenomena affect cortical circuit activity remains unclear. We studied two mouse models of schizophrenia-relevant disease processes: chronic ketamine (KET) administration and Df(16)A(+/-), modeling 22q11.2 microdeletions, a genetic variant highly penetrant for schizophrenia. Local field potential recordings in visual cortex confirmed gamma-band abnormalities similar to patient studies. Two-photon calcium imaging of local cortical populations revealed in both models a deficit in the reliability of neuronal coactivity patterns (ensembles), which was not a simple consequence of altered single-neuron activity. This effect was present in ongoing and sensory-evoked activity and was not replicated by acute ketamine administration or pharmacogenetic parvalbumin-interneuron suppression. These results are consistent with the hypothesis that schizophrenia is an "attractor" disease and demonstrate that degraded neuronal ensembles are a common consequence of diverse genetic, cellular, and synaptic alterations seen in chronic schizophrenia. Published by Elsevier Inc.

  11. Modeling Network Interdiction Tasks

    Science.gov (United States)

    2015-09-17

    minimize the operating costs for manufacturing 50 the item. This simple example illustrates the hierarchical structure that can be modeled using...fixed. The resulting model is linearized and the product of the dual variable and the (1−γij) term replaced with βij. This allows certain...the standard network interdiction model based on its tight linear programming relaxation. 2.3.3 Network Disruption. In practice, whenever an object is

  12. Early magnetic resonance detection of cortical necrosis and acute network injury associated with neonatal and infantile cerebral infarction

    Energy Technology Data Exchange (ETDEWEB)

    Okabe, Tetsuhiko; Aida, Noriko; Nozawa, Kumiko [Kanagawa Children' s Medical Center, Department of Radiology, Yokohama (Japan); Niwa, Tetsu [Kanagawa Children' s Medical Center, Department of Radiology, Yokohama (Japan); Tokai University School of Medicine, Department of Radiology, Isehara (Japan); Shibasaki, Jun [Kanagawa Children' s Medical Center, Department of Neonatology, Yokohama (Japan); Osaka, Hitoshi [Kanagawa Children' s Medical Center, Department of Neurology, Yokohama (Japan)

    2014-05-15

    Knowledge of MRI findings in pediatric cerebral infarction is limited. To determine whether cortical necrosis and network injury appear in the acute phase in post-stroke children and to identify anatomical location of acute network injury and the ages at which these phenomena are seen. Images from 12 children (age range: 0-9 years; neonates [<1 month], n=5; infants [1 month-12 months], n=3; others [≥1 year], n=4) with acute middle cerebral artery (MCA) cortical infarction were retrospectively analyzed. Cortical necrosis was defined as hyperintense cortical lesions on T1-weighted imaging that lacked evidence of hemorrhage. Acute network injury was defined as hyperintense lesions on diffusion-weighted imaging that were not in the MCA territory and had fiber connections with the affected cerebral cortex. MRI was performed within the first week after disease onset. Cortical necrosis was only found in three neonates. Acute network injury was seen in the corticospinal tract (CST), thalamus and corpus callosum. Acute network injury along the CST was found in five neonates and one 7-month-old infant. Acute network injury was evident in the thalamus of four neonates and two infants (ages 4 and 7 months) and in the corpus callosum of five neonates and two infants (ages 4 and 7 months). The entire thalamus was involved in three children when infarction of MCA was complete. In acute MCA cortical infarction, MRI findings indicating cortical necrosis or acute network injury was frequently found in neonates and early infants. Response to injury in a developing brain may be faster than that in a mature one. (orig.)

  13. Differential motor and prefrontal cerebello-cortical network development: Evidence from multimodal neuroimaging.

    Science.gov (United States)

    Bernard, Jessica A; Orr, Joseph M; Mittal, Vijay A

    2016-01-01

    While our understanding of cerebellar structural development through adolescence and young adulthood has expanded, we still lack knowledge of the developmental patterns of cerebellar networks during this critical portion of the lifespan. Volume in lateral posterior cerebellar regions associated with cognition and the prefrontal cortex develops more slowly, reaching their peak volume in adulthood, particularly as compared to motor Lobule V. We predicted that resting state functional connectivity of the lateral posterior regions would show a similar pattern of development during adolescence and young adulthood. That is, we expected to see changes over time in Crus I and Crus II connectivity with the cortex, but no changes in Lobule V connectivity. Additionally, we were interested in how structural connectivity changes in cerebello-thalamo-cortical white matter are related to changes in functional connectivity. A sample of 23 individuals between 12 and 21years old underwent neuroimaging scans at baseline and 12months later. Functional networks of Crus I and Crus II showed significant connectivity decreases over 12months, though there were no differences in Lobule V. Furthermore, these functional connectivity changes were correlated with increases in white matter structural integrity in the corresponding cerebello-thalamo-cortical white matter tract. We suggest that these functional network changes are due to both later pruning in the prefrontal cortex as well as further development of the white matter tracts linking these brain regions.

  14. Simple models of human brain functional networks.

    Science.gov (United States)

    Vértes, Petra E; Alexander-Bloch, Aaron F; Gogtay, Nitin; Giedd, Jay N; Rapoport, Judith L; Bullmore, Edward T

    2012-04-10

    Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.

  15. Differences in human cortical gene expression match the temporal properties of large-scale functional networks.

    Directory of Open Access Journals (Sweden)

    Claudia Cioli

    Full Text Available We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas. Previous work suggests that functional cortical networks (resting state and task based are organized as two large networks (differentiated by their preferred information processing mode shaped like two rings. The first ring--Visual-Sensorimotor-Auditory (VSA--comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring--Parieto-Temporo-Frontal (PTF--comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found--with correspondence analysis--that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins--coded by genes that most differentiate the rings--were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1. Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium

  16. Chimera in a neuronal network model of the cat brain

    OpenAIRE

    Santos, M. S.; Szezech Jr., J. D.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.; Viana, R. L.; Kurths, J.

    2016-01-01

    Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, so...

  17. Moving Objects Detection Using Intersecting Cortical Model in Enhanced Fish-eye Image

    Institute of Scientific and Technical Information of China (English)

    WU Jian-hui; YANG Kun-tao; DU Jian-rong; ZHANG Nan-yang-sheng

    2009-01-01

    A new method of the moving objects detection using the enhanced fish-eye lens and the intersecting cortical model (ICM) algorithm is proposed. The improved fish-eye lens is designed through controlling the entrance pupils of the lens.This lens has an ultra field of view about 183 degrees,and can image resolution.The ICM is a model based on pulse coupled neural network(PCNN) which is especially designed for image processing.It is derived from several visual cortex models and is basically the intersection of these models.The theoretical foundation of the ICM is given.An improved ICM algorithm in which some parameters are modified is used to detect moving objects specially.The experiment indicated that moving objects can be detected reliably and efficiently using ICM algorithm from the elliptical fish-eye image.It can be used in the field of traffic monitoring and other security domains.

  18. A Model for Cortically Mediated Behaviors: A "New Think" Model for Some Old Thought Processes.

    Science.gov (United States)

    Anderson, James A.

    A model for dealing with ordinary, cortically-mediated behaviors is presented. The model's foundation is the set of motivational systems existing in the mature organism. Construction of the model follows the stimulus-response paradigm as interpreted by recent physiological research. The purpose of the model is that it requires a multivariate…

  19. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used......This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...

  20. Modeling Evolving Innovation Networks

    OpenAIRE

    Koenig, Michael D.; Battiston, Stefano; Schweitzer, Frank

    2007-01-01

    We develop a new framework for modeling innovation networks which evolve over time. The nodes in the network represent firms, whereas the directed links represent unilateral interactions between the firms. Both nodes and links evolve according to their own dynamics and on different time scales. The model assumes that firms produce knowledge based on the knowledge exchange with other firms, which involves both costs and benefits for the participating firms. In order to increase their knowledge...

  1. Role of altered cerebello-thalamo-cortical network in the neurobiology of essential tremor

    Energy Technology Data Exchange (ETDEWEB)

    Lenka, Abhishek; Bhalsing, Ketaki Swapnil; Jhunjhunwala, Ketan [National Institute of Mental Health and Neurosciences, Department of Neurology, Bangalore, Karnataka (India); National Institute of Mental Health and Neurosciences, Department of Clinical Neurosciences, Bangalore, Karnataka (India); Panda, Rajanikant; Saini, Jitender; Bharath, Rose Dawn [National Institute of Mental Health and Neurosciences, Department of Neuroimaging and Interventional Radiology, Bangalore, Karnataka (India); Naduthota, Rajini M.; Yadav, Ravi; Pal, Pramod Kumar [National Institute of Mental Health and Neurosciences, Department of Neurology, Bangalore, Karnataka (India)

    2017-02-15

    Essential tremor (ET) is the most common movement disorder among adults. Although ET has been recognized as a mono-symptomatic benign illness, reports of non-motor symptoms and non-tremor motor symptoms have increased its clinical heterogeneity. The neural correlates of ET are not clearly understood. The aim of this study was to understand the neurobiology of ET using resting state fMRI. Resting state functional MR images of 30 patients with ET and 30 age- and gender-matched healthy controls were obtained. The functional connectivity of the two groups was compared using whole-brain seed-to-voxel-based analysis. The ET group had decreased connectivity of several cortical regions especially of the primary motor cortex and the primary somatosensory cortex with several right cerebellar lobules compared to the controls. The thalamus on both hemispheres had increased connectivity with multiple posterior cerebellar lobules and vermis. Connectivity of several right cerebellar seeds with the cortical and thalamic seeds had significant correlation with an overall score of Fahn-Tolosa-Marin tremor rating scale (FTM-TRS) as well as the subscores for head tremor and limb tremor. Seed-to-voxel resting state connectivity analysis revealed significant alterations in the cerebello-thalamo-cortical network in patients with ET. These alterations correlated with the overall FTM scores as well as the subscores for limb tremor and head tremor in patients with ET. These results further support the previous evidence of cerebellar pathology in ET. (orig.)

  2. Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation.

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    James P Roach

    2015-08-01

    Full Text Available Acetylcholine (ACh is a regulator of neural excitability and one of the neurochemical substrates of sleep. Amongst the cellular effects induced by cholinergic modulation are a reduction in spike-frequency adaptation (SFA and a shift in the phase response curve (PRC. We demonstrate in a biophysical model how changes in neural excitability and network structure interact to create three distinct functional regimes: localized asynchronous, traveling asynchronous, and traveling synchronous. Our results qualitatively match those observed experimentally. Cortical activity during slow wave sleep (SWS differs from that during REM sleep or waking states. During SWS there are traveling patterns of activity in the cortex; in other states stationary patterns occur. Our model is a network composed of Hodgkin-Huxley type neurons with a M-current regulated by ACh. Regulation of ACh level can account for dynamical changes between functional regimes. Reduction of the magnitude of this current recreates the reduction in SFA the shift from a type 2 to a type 1 PRC observed in the presence of ACh. When SFA is minimal (in waking or REM sleep state, high ACh patterns of activity are localized and easily pinned by network inhomogeneities. When SFA is present (decreasing ACh, traveling waves of activity naturally arise. A further decrease in ACh leads to a high degree of synchrony within traveling waves. We also show that the level of ACh determines how sensitive network activity is to synaptic heterogeneity. These regimes may have a profound functional significance as stationary patterns may play a role in the proper encoding of external input as memory and traveling waves could lead to synaptic regularization, giving unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from the patterns.

  3. Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks.

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    Matteo Garofalo

    Full Text Available Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs. First we tested these "connectivity methods" on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic and PPC (Positive Precision Curve, a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.

  4. Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks

    Science.gov (United States)

    Garofalo, Matteo; Nieus, Thierry; Massobrio, Paolo; Martinoia, Sergio

    2009-01-01

    Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings. PMID:19652720

  5. Cortical networks for visual reaching: physiological and anatomical organization of frontal and parietal lobe arm regions.

    Science.gov (United States)

    Johnson, P B; Ferraina, S; Bianchi, L; Caminiti, R

    1996-01-01

    The functional and structural properties of the dorsolateral frontal lobe and posterior parietal proximal arm representations were studied in macaque monkeys. Physiological mapping of primary motor (MI), dorsal premotor (PMd), and posterior parietal (area 5) cortices was performed in behaving monkeys trained in an instructed-delay reaching task. The parietofrontal corticocortical connectivities of these same areas were subsequently examined anatomically by means of retrograde tracing techniques. Signal-, set-, movement-, and position-related directional neuronal activities were distributed nonuniformly within the task-related areas in both frontal and parietal cortices. Within the frontal lobe, moving caudally from PMd to the MI, the activity that signals for the visuo-spatial events leading to target localization decreased, while the activity more directly linked to movement generation increased. Physiological recordings in the superior parietal lobule revealed a gradient-like distribution of functional properties similar to that observed in the frontal lobe. Signal- and set-related activities were encountered more frequently in the intermediate and ventral part of the medial bank of the intraparietal sulcus (IPS), in area MIP. Movement-and position-related activities were distributed more uniformly within the superior parietal lobule (SPL), in both dorsal area 5 and in MIP. Frontal and parietal regions sharing similar functional properties were preferentially connected through their association pathways. As a result of this study, area MIP, and possibly areas MDP and 7m as well, emerge as the parietal nodes by which visual information may be relayed to the frontal lobe arm region. These parietal and frontal areas, along with their association connections, represent a potential cortical network for visual reaching. The architecture of this network is ideal for coding reaching as the result of a combination between visual and somatic information.

  6. Temporal Genetic Modifications after Controlled Cortical Impact—Understanding Traumatic Brain Injury through a Systematic Network Approach

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    Yung-Hao Wong

    2016-02-01

    Full Text Available Traumatic brain injury (TBI is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h, using a controlled cortical impact (CCI model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein–protein interactions (PPIs. We also identified UBC (Ubiquitin C, SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A, and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research.

  7. Histological analysis of the alterations on cortical bone channels network after radiotherapy: A rabbit study.

    Science.gov (United States)

    Rabelo, Gustavo Davi; Beletti, Marcelo Emílio; Dechichi, Paula

    2010-10-01

    The aim of this study was to evaluate the effects of radiotherapy in cortical bone channels network. Fourteen rabbits were divided in two groups and test group received single dose of 15 Gy cobalt-60 radiation in tibia, bilaterally. The animals were sacrificed and a segment of tibia was removed and histologically processed. Histological images were taken and had their bone channels segmented and called regions of interest (ROI). Images were analyzed through developed algorithms using the SCILAB mathematical environment, getting percentage of bone matrix, ROI areas, ROI perimeters, their standard deviations and Lacunarity. The osteocytes and empty lacunae were also counted. Data were evaluated using Kolmogorov-Smirnov, Mann Whitney, and Student's t test (P lacunarity were found between groups. In conclusion, the radiotherapy causes reduction of bone matrix and modifies the morphology of bone channels network. © 2010 Wiley-Liss, Inc.

  8. On visual hallucinations and cortical networks: a trans-diagnostic review.

    Science.gov (United States)

    Carter, Rowena; Ffytche, Dominic H

    2015-07-01

    Our current clinical approach to visual hallucinations is largely derived from work carried out by Georges de Morsier in the 1930s. Now, almost a century after his influential papers, we have the research tools to further explore the ideas he put forward. In this review, we address de Morsier's proposal that visual hallucinations in all clinical conditions have a similar neurological mechanism by comparing structural imaging studies of susceptibility to visual hallucinations in Parkinson's disease, Alzheimer's disease, Dementia with Lewy bodies and schizophrenia. Systematic review of the literature was undertaken using PubMed searches. A total of 18 studies across conditions were identified reporting grey matter differences between patients with and without visual hallucinations. Grey matter changes were categorised into brain regions relevant to current theories of visual hallucinations. The distribution of cortical atrophy supports de Morsier's premise that visual hallucinations are invariably linked to aberrant activity within visual thalamo-cortical networks. Further work is required to determine by what mechanism these networks become predisposed to spontaneous activation, and whether the frontal lobe and hippocampal changes identified are present in all conditions. The findings have implications for the development of effective treatments for visual hallucinations.

  9. Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks.

    Science.gov (United States)

    Hage, Ilige S; Hamade, Ramsey F

    2016-05-01

    In cortical bone, solid (lamellar and interstitial) matrix occupies space left over by porous microfeatures such as Haversian canals, lacunae, and canaliculi-containing clusters. In this work, pulse-coupled neural networks (PCNN) were used to automatically distinguish the microfeatures present in histology slides of cortical bone. The networks' parameters were optimized using particle swarm optimization (PSO). When forming the fitness functions for the PSO, we considered the microfeatures' geometric attributes-namely, their size (based on measures of elliptical perimeter or area), shape (based on measures of compactness or the ratio of minor axis length to major axis length), and a two-way combination of these two geometric attributes. This hybrid PCNN-PSO method was further enhanced for pulse evaluation by combination with yet another method, adaptive threshold (AT), where the PCNN algorithm is repeated until the best threshold is found corresponding to the maximum variance between two segmented regions. Together, this framework of using PCNN-PSO-AT constitutes, we believe, a novel framework in biomedical imaging. Using this framework and extracting microfeatures from only one training image, we successfully extracted microfeatures from other test images. The high fidelity of all resultant segments was established using quantitative metrics such as precision, specificity, and Dice indices.

  10. Situating the default-mode network along a principal gradient of macroscale cortical organization.

    Science.gov (United States)

    Margulies, Daniel S; Ghosh, Satrajit S; Goulas, Alexandros; Falkiewicz, Marcel; Huntenburg, Julia M; Langs, Georg; Bezgin, Gleb; Eickhoff, Simon B; Castellanos, F Xavier; Petrides, Michael; Jefferies, Elizabeth; Smallwood, Jonathan

    2016-11-01

    Understanding how the structure of cognition arises from the topographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, but an overarching framework for the association between structure and function is still lacking. Here, we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored by, at one end, regions serving primary sensory/motor functions and at the other end, transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface-and are precisely equidistant-from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional metaanalysis. Together, these observations provide a characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

  11. Auditory cortical delta-entrainment interacts with oscillatory power in multiple fronto-parietal networks.

    Science.gov (United States)

    Keitel, Anne; Ince, Robin A A; Gross, Joachim; Kayser, Christoph

    2017-02-15

    The timing of slow auditory cortical activity aligns to the rhythmic fluctuations in speech. This entrainment is considered to be a marker of the prosodic and syllabic encoding of speech, and has been shown to correlate with intelligibility. Yet, whether and how auditory cortical entrainment is influenced by the activity in other speech-relevant areas remains unknown. Using source-localized MEG data, we quantified the dependency of auditory entrainment on the state of oscillatory activity in fronto-parietal regions. We found that delta band entrainment interacted with the oscillatory activity in three distinct networks. First, entrainment in the left anterior superior temporal gyrus (STG) was modulated by beta power in orbitofrontal areas, possibly reflecting predictive top-down modulations of auditory encoding. Second, entrainment in the left Heschl's Gyrus and anterior STG was dependent on alpha power in central areas, in line with the importance of motor structures for phonological analysis. And third, entrainment in the right posterior STG modulated theta power in parietal areas, consistent with the engagement of semantic memory. These results illustrate the topographical network interactions of auditory delta entrainment and reveal distinct cross-frequency mechanisms by which entrainment can interact with different cognitive processes underlying speech perception.

  12. Trading speed and accuracy by coding time: a coupled-circuit cortical model.

    Directory of Open Access Journals (Sweden)

    Dominic Standage

    2013-04-01

    Full Text Available Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by 'climbing' activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.

  13. Predicting functional cortical ROIs via DTI-derived fiber shape models.

    Science.gov (United States)

    Zhang, Tuo; Guo, Lei; Li, Kaiming; Jing, Changfeng; Yin, Yan; Zhu, Dajiang; Cui, Guangbin; Li, Lingjiang; Liu, Tianming

    2012-04-01

    Studying structural and functional connectivities of human cerebral cortex has drawn significant interest and effort recently. A fundamental and challenging problem arises when attempting to measure the structural and/or functional connectivities of specific cortical networks: how to identify and localize the best possible regions of interests (ROIs) on the cortex? In our view, the major challenges come from uncertainties in ROI boundary definition, the remarkable structural and functional variability across individuals and high nonlinearities within and around ROIs. In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on their learned fiber shape models from multimodal task-based functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. In the training stage, shape models of white matter fibers are learnt from those emanating from the functional ROIs, which are activated brain regions detected from task-based fMRI data. In the prediction stage, functional ROIs are predicted in individual brains based only on DTI data. Our experiment results show that the average ROI prediction error is around 3.94 mm, in comparison with benchmark data provided by working memory and visual task-based fMRI. Our work demonstrated that fiber bundle shape models derived from DTI data are good predictors of functional cortical ROIs.

  14. Pragmatics in action: indirect requests engage theory of mind areas and the cortical motor network.

    Science.gov (United States)

    van Ackeren, Markus J; Casasanto, Daniel; Bekkering, Harold; Hagoort, Peter; Rueschemeyer, Shirley-Ann

    2012-11-01

    Research from the past decade has shown that understanding the meaning of words and utterances (i.e., abstracted symbols) engages the same systems we used to perceive and interact with the physical world in a content-specific manner. For example, understanding the word "grasp" elicits activation in the cortical motor network, that is, part of the neural substrate involved in planned and executing a grasping action. In the embodied literature, cortical motor activation during language comprehension is thought to reflect motor simulation underlying conceptual knowledge [note that outside the embodied framework, other explanations for the link between action and language are offered, e.g., Mahon, B. Z., & Caramazza, A. A critical look at the embodied cognition hypothesis and a new proposal for grouding conceptual content. Journal of Physiology, 102, 59-70, 2008; Hagoort, P. On Broca, brain, and binding: A new framework. Trends in Cognitive Sciences, 9, 416-423, 2005]. Previous research has supported the view that the coupling between language and action is flexible, and reading an action-related word form is not sufficient for cortical motor activation [Van Dam, W. O., van Dijk, M., Bekkering, H., & Rueschemeyer, S.-A. Flexibility in embodied lexical-semantic representations. Human Brain Mapping, doi: 10.1002/hbm.21365, 2011]. The current study goes one step further by addressing the necessity of action-related word forms for motor activation during language comprehension. Subjects listened to indirect requests (IRs) for action during an fMRI session. IRs for action are speech acts in which access to an action concept is required, although it is not explicitly encoded in the language. For example, the utterance "It is hot here!" in a room with a window is likely to be interpreted as a request to open the window. However, the same utterance in a desert will be interpreted as a statement. The results indicate (1) that comprehension of IR sentences activates cortical

  15. Disparity between dorsal and ventral networks in patients with obsessive-compulsive disorder: Evidence revealed by graph theoretical analysis based on cortical thickness from MRI

    Directory of Open Access Journals (Sweden)

    Seung-Goo eKim

    2013-07-01

    Full Text Available As one of the most widely accepted neuroanatomical models on OCD, it has been hypothesized that imbalance between an excitatory direct (ventral pathway and an inhibitory indirect (dorsal pathway in cortico-striato-thalamic circuit underlies the emergence of OCD. Here we examine the structural network in drug-free patients with OCD in terms of graph theoretical measures for the first time. We used a measure called efficiency which quantifies how a node transfers information efficiently. To construct brain networks, cortical thickness was automatically estimated using T1-weighted magnetic resonance imaging. We found that the network of the OCD patients was as efficient as that of healthy controls so that the both networks were in the small-world regime. More importantly, however, disparity between the dorsal and the ventral networks in the OCD patients was found, suggesting a positive evidence to the imbalance theory on the underlying pathophysiology of OCD.

  16. Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses.

    Science.gov (United States)

    Arnsten, Amy F T; Wang, Min J; Paspalas, Constantinos D

    2012-10-04

    This review describes unique neuromodulatory influences on working memory prefrontal cortical (PFC) circuits that coordinate cognitive strength with arousal state. Working memory arises from recurrent excitation within layer III PFC pyramidal cell NMDA circuits, which are afflicted in aging and schizophrenia. Neuromodulators rapidly and flexibly alter the efficacy of these synaptic connections, while leaving the synaptic architecture unchanged, a process called dynamic network connectivity (DNC). Increases in calcium-cAMP signaling open ion channels in long, thin spines, gating network connections. Inhibition of calcium-cAMP signaling by stimulating α2A-adrenoceptors on spines strengthens synaptic efficacy and increases network firing, whereas optimal stimulation of dopamine D1 receptors sculpts network inputs to refine mental representation. Generalized increases in calcium-cAMP signaling during fatigue or stress disengage dlPFC recurrent circuits, reduce firing and impair top-down cognition. Impaired DNC regulation contributes to age-related cognitive decline, while genetic insults to DNC proteins are commonly linked to schizophrenia.

  17. Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality.

    Science.gov (United States)

    Vukelić, Mathias; Gharabaghi, Alireza

    2015-05-01

    Neurofeedback of self-regulated brain activity in circumscribed cortical regions is used as a novel strategy to facilitate functional restoration following stroke. Basic knowledge about its impact on motor system oscillations and functional connectivity is however scarce. Specifically, a direct comparison between different feedback modalities and their neural signatures is missing. We assessed a neurofeedback training intervention of modulating β-activity in circumscribed sensorimotor regions by kinesthetic motor imagery (MI). Right-handed healthy participants received two different feedback modalities contingent to their MI-associated brain activity in a cross-over design: (I) visual feedback with a brain-computer interface (BCI) and (II) proprioceptive feedback with a brain-robot interface (BRI) orthosis attached to the right hand. High-density electroencephalography was used to examine the reactivity of the cortical motor system during the training session of each task by studying both local oscillatory power entrainment and distributed functional connectivity. Both feedback modalities activated a distributed functional connectivity network of coherent oscillations. A significantly higher skill and lower variability of self-controlled sensorimotor β-band modulation could, however, be achieved in the BRI condition. This gain in controlling regional motor oscillations was accompanied by functional coupling of remote β-band and θ-band activity in bilateral fronto-central regions and left parieto-occipital regions, respectively. The functional coupling of coherent θ-band oscillations correlated moreover with the skill of regional β-modulation thus revealing a motor learning related network. Our findings indicate that proprioceptive feedback is more suitable than visual feedback to entrain the motor network architecture during the interplay between motor imagery and feedback processing thus resulting in better volitional control of regional brain activity.

  18. Progressive brain damage, synaptic reorganization and NMDA activation in a model of epileptogenic cortical dysplasia.

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    Francesca Colciaghi

    Full Text Available Whether severe epilepsy could be a progressive disorder remains as yet unresolved. We previously demonstrated in a rat model of acquired focal cortical dysplasia, the methylazoxymethanol/pilocarpine - MAM/pilocarpine - rats, that the occurrence of status epilepticus (SE and subsequent seizures fostered a pathologic process capable of modifying the morphology of cortical pyramidal neurons and NMDA receptor expression/localization. We have here extended our analysis by evaluating neocortical and hippocampal changes in MAM/pilocarpine rats at different epilepsy stages, from few days after onset up to six months of chronic epilepsy. Our findings indicate that the process triggered by SE and subsequent seizures in the malformed brain i is steadily progressive, deeply altering neocortical and hippocampal morphology, with atrophy of neocortex and CA regions and progressive increase of granule cell layer dispersion; ii changes dramatically the fine morphology of neurons in neocortex and hippocampus, by increasing cell size and decreasing both dendrite arborization and spine density; iii induces reorganization of glutamatergic and GABAergic networks in both neocortex and hippocampus, favoring excitatory vs inhibitory input; iv activates NMDA regulatory subunits. Taken together, our data indicate that, at least in experimental models of brain malformations, severe seizure activity, i.e., SE plus recurrent seizures, may lead to a widespread, steadily progressive architectural, neuronal and synaptic reorganization in the brain. They also suggest the mechanistic relevance of glutamate/NMDA hyper-activation in the seizure-related brain pathologic plasticity.

  19. Use of cortical neuronal networks for in vitro material biocompatibility testing.

    Science.gov (United States)

    Charkhkar, Hamid; Frewin, Christopher; Nezafati, Maysam; Knaack, Gretchen L; Peixoto, Nathalia; Saddow, Stephen E; Pancrazio, Joseph J

    2014-03-15

    Neural interfaces aim to restore neurological function lost during disease or injury. Novel implantable neural interfaces increasingly capitalize on novel materials to achieve microscale coupling with the nervous system. Like any biomedical device, neural interfaces should consist of materials that exhibit biocompatibility in accordance with the international standard ISO10993-5, which describes in vitro testing involving fibroblasts where cytotoxicity serves as the main endpoint. In the present study, we examine the utility of living neuronal networks as functional assays for in vitro material biocompatibility, particularly for materials that comprise implantable neural interfaces. Embryonic mouse cortical tissue was cultured to form functional networks where spontaneous action potentials, or spikes, can be monitored non-invasively using a substrate-integrated microelectrode array. Taking advantage of such a platform, we exposed established positive and negative control materials to the neuronal networks in a consistent method with ISO 10993-5 guidance. Exposure to the negative controls, gold and polyethylene, did not significantly change the neuronal activity whereas the positive controls, copper and polyvinyl chloride (PVC), resulted in reduction of network spike rate. We also compared the functional assay with an established cytotoxicity measure using L929 fibroblast cells. Our findings indicate that neuronal networks exhibit enhanced sensitivity to positive control materials. In addition, we assessed functional neurotoxicity of tungsten, a common microelectrode material, and two conducting polymer formulations that have been used to modify microelectrode properties for in vivo recording and stimulation. These data suggest that cultured neuronal networks are a useful platform for evaluating the functional toxicity of materials intended for implantation in the nervous system.

  20. Models of educational institutions' networking

    OpenAIRE

    Shilova Olga Nikolaevna

    2015-01-01

    The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.

  1. Models of educational institutions' networking

    OpenAIRE

    Shilova Olga Nikolaevna

    2015-01-01

    The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.

  2. From neural plate to cortical arousal-a neuronal network theory of sleep derived from in vitro "model" systems for primordial patterns of spontaneous bioelectric activity in the vertebrate central nervous system.

    Science.gov (United States)

    Corner, Michael A

    2013-05-22

    In the early 1960s intrinsically generated widespread neuronal discharges were discovered to be the basis for the earliest motor behavior throughout the animal kingdom. The pattern generating system is in fact programmed into the developing nervous system, in a regionally specific manner, already at the early neural plate stage. Such rhythmically modulated phasic bursts were next discovered to be a general feature of developing neural networks and, largely on the basis of experimental interventions in cultured neural tissues, to contribute significantly to their morpho-physiological maturation. In particular, the level of spontaneous synchronized bursting is homeostatically regulated, and has the effect of constraining the development of excessive network excitability. After birth or hatching, this "slow-wave" activity pattern becomes sporadically suppressed in favor of sensory oriented "waking" behaviors better adapted to dealing with environmental contingencies. It nevertheless reappears periodically as "sleep" at several species-specific points in the diurnal/nocturnal cycle. Although this "default" behavior pattern evolves with development, its essential features are preserved throughout the life cycle, and are based upon a few simple mechanisms which can be both experimentally demonstrated and simulated by computer modeling. In contrast, a late onto- and phylogenetic aspect of sleep, viz., the intermittent "paradoxical" activation of the forebrain so as to mimic waking activity, is much less well understood as regards its contribution to brain development. Some recent findings dealing with this question by means of cholinergically induced "aroused" firing patterns in developing neocortical cell cultures, followed by quantitative electrophysiological assays of immediate and longterm sequelae, will be discussed in connection with their putative implications for sleep ontogeny.

  3. Spatiotemporal receptive fields: a dynamical model derived from cortical architectonics.

    Science.gov (United States)

    Krone, G; Mallot, H; Palm, G; Schüz, A

    1986-01-22

    We assume that the mammalian neocortex is built up out of some six layers which differ in their morphology and their external connections. Intrinsic connectivity is largely excitatory, leading to a considerable amount of positive feedback. The majority of cortical neurons can be divided into two main classes: the pyramidal cells, which are said to be excitatory, and local cells (most notably the non-spiny stellate cells), which are said to be inhibitory. The form of the dendritic and axonal arborizations of both groups is discussed in detail. This results in a simplified model of the cortex as a stack of six layers with mutual connections determined by the principles of fibre anatomy. This stack can be treated as a multi-input-multi-output system by means of the linear systems theory of homogeneous layers. The detailed equations for the simulation are derived in the Appendix. The results of the simulations show that the temporal and spatial behaviour of an excitation distribution cannot be treated separately. Further, they indicate specific processing in the different layers and some independence from details of wiring. Finally, the simulation results are applied to the theory of visual receptive fields. This yields some insight into the mechanisms possibly underlying hypercomplexity, putative nonlinearities, lateral inhibition, oscillating cell responses, and velocity-dependent tuning curves.

  4. Analytical characterization of spontaneous firing in networks of developing rat cultured cortical neurons

    Science.gov (United States)

    Tateno, Takashi; Kawana, Akio; Jimbo, Yasuhiko

    2002-05-01

    We have used a multiunit electrode array in extracellular recording to investigate changes in the firing patterns in networks of developing rat cortical neurons. The spontaneous activity of continual asynchronous firing or the alternation of asynchronous spikes and synchronous bursts changed over time so that activity in the later stages consisted exclusively of synchronized bursts. The spontaneous coordinated activity in bursts produced a variability in interburst interval (IBI) sequences that is referred to as ``form.'' The stochastic and nonlinear dynamical analysis of IBI sequences revealed that these sequences reflected a largely random process and that the form for relatively immature neurons was largely oscillatory while the form for the more mature neurons was Poisson-like. The observed IBI sequences thus showed changes in form associated with both the intrinsic properties of the developing cells and the neural response to correlated synaptic inputs due to interaction between the developing neural circuits.

  5. Impairment of GABA transporter GAT-1 terminates cortical recurrent network activity via enhanced phasic inhibition

    Directory of Open Access Journals (Sweden)

    Daniel Simon Razik

    2013-09-01

    Full Text Available In the central nervous system, GABA transporters (GATs very efficiently clear synaptically released GABA from the extracellular space, and thus exert a tight control on GABAergic inhibition. In neocortex, GABAergic inhibition is heavily recruited during recurrent phases of spontaneous action potential activity which alternate with neuronally quiet periods. Therefore, such activity should be quite sensitive to minute alterations of GAT function. Here, we explored the effects of a gradual impairment of GAT-1 and GAT-2/3 on spontaneous recurrent network activity – termed network bursts and silent periods – in organotypic slice cultures of rat neocortex. The GAT-1 specific antagonist NO-711 depressed activity already at nanomolar concentrations (IC50 for depression of spontaneous multiunit firing rate of 42 nM, reaching a level of 80% at 500-1000 nM. By contrast, the GAT-2/3 preferring antagonist SNAP-5114 had weaker and less consistent effects. Several lines of evidence pointed towards an enhancement of phasic GABAergic inhibition as the dominant activity-depressing mechanism: network bursts were drastically shortened, phasic GABAergic currents decayed slower, and neuronal excitability during ongoing activity was diminished. In silent periods, NO-711 had little effect on neuronal excitability or membrane resistance, quite in contrast to the effects of muscimol, a GABA mimetic which activates GABAA receptors tonically. Our results suggest that an enhancement of phasic GABAergic inhibition efficiently curtails cortical recurrent activity and may mediate antiepileptic effects of therapeutically relevant concentrations of GAT-1 antagonists.

  6. Mathematical approaches to modeling of cortical spreading depression

    Science.gov (United States)

    Miura, Robert M.; Huang, Huaxiong; Wylie, Jonathan J.

    2013-12-01

    Migraine with aura (MwA) is a debilitating disease that afflicts about 25%-30% of migraine sufferers. During MwA, a visual illusion propagates in the visual field, then disappears, and is followed by a sustained headache. MwA was conjectured by Lashley to be related to some neurological phenomenon. A few years later, Leão observed electrophysiological waves in the brain that are now known as cortical spreading depression (CSD). CSD waves were soon conjectured to be the neurological phenomenon underlying MwA that had been suggested by Lashley. However, the confirmation of the link between MwA and CSD was not made until 2001 by Hadjikhani et al. [Proc. Natl. Acad. Sci. U.S.A. 98, 4687-4692 (2001)] using functional MRI techniques. Despite the fact that CSD has been studied continuously since its discovery in 1944, our detailed understandings of the interactions between the mechanisms underlying CSD waves have remained elusive. The connection between MwA and CSD makes the understanding of CSD even more compelling and urgent. In addition to all of the information gleaned from the many experimental studies on CSD since its discovery, mathematical modeling studies provide a general and in some sense more precise alternative method for exploring a variety of mechanisms, which may be important to develop a comprehensive picture of the diverse mechanisms leading to CSD wave instigation and propagation. Some of the mechanisms that are believed to be important include ion diffusion, membrane ionic currents, osmotic effects, spatial buffering, neurotransmitter substances, gap junctions, metabolic pumps, and synaptic connections. Discrete and continuum models of CSD consist of coupled nonlinear differential equations for the ion concentrations. In this review of the current quantitative understanding of CSD, we focus on these modeling paradigms and various mechanisms that are felt to be important for CSD.

  7. Modeling Pediatric Brain Trauma: Piglet Model of Controlled Cortical Impact.

    Science.gov (United States)

    Pareja, Jennifer C Munoz; Keeley, Kristen; Duhaime, Ann-Christine; Dodge, Carter P

    2016-01-01

    The brain has different responses to traumatic injury as a function of its developmental stage. As a model of injury to the immature brain, the piglet shares numerous similarities in regards to morphology and neurodevelopmental sequence compared to humans. This chapter describes a piglet scaled focal contusion model of traumatic brain injury that accounts for the changes in mass and morphology of the brain as it matures, facilitating the study of age-dependent differences in response to a comparable mechanical trauma.

  8. A time and place for language comprehension : mapping the N400 and the P600 to a minimal cortical network

    NARCIS (Netherlands)

    Brouwer, Harm; Hoeks, John C. J.

    2013-01-01

    We propose a new functional-anatomical mapping of the N400 and the P600 to a minimal cortical network for language comprehension. Our work is an example of a recent research strategy in cognitive neuroscience, where researchers attempt to align data regarding the nature and time-course of cognitive

  9. Abnormal cortical-basal ganglia network in amyotrophic lateral sclerosis: A voxel-wise network efficiency analysis.

    Science.gov (United States)

    Xu, Jinping; Li, Hong; Li, Chong; Yao, Jen-Chih; Hu, Jun; Wang, Jian; Hu, Qingmao; Zhang, Yuanchao; Zhang, Jiuquan

    2017-08-30

    Evidence suggests that dysfunctional cortical-basal ganglia (CBG) network plays important roles in the motor symptoms in amyotrophic lateral sclerosis (ALS). However, little effort has been made to investigate the functional abnormalities of CBG network in ALS. Here, we constructed voxel-wise CBG networks using the resting-state fMRI data of 20 patients with ALS and 21 normal controls, and characterized the differences of their efficiency parameters between the two groups. Compared to normal controls, patients with ALS exhibited decreased nodal efficiency in the right thalamus (THA), the left caudate (CAU) and the right precentral gyrus (preCG), and increased nodal efficiency in the left preCG. In the patient group, we observed a significant negative correlation between the nodal efficiency of the right preCG and disease progression rate. These results demonstrate that both ineffective information transfer and compensatory mechanisms are involved in the pathophysiological mechanism underlying the motor dysfunctions in patients with ALS. In summary, the present study provides a novel perspective on pathophysiological explanation for the motor symptoms in patients with ALS. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Odor recognition and segmentation by coupled olfactory bulb and cortical networks

    CERN Document Server

    Li, Z; Li, Zhaoping; Hertz, John

    1999-01-01

    We present a model of a coupled system of the olfactory bulb and cortex. Odor inputs to the epithelium are transformed to oscillatory bulbar activities. The cortex recognizes the odor by resonating to the bulbar oscillating pattern when the amplitude and phase patterns from the bulb match an odor memory stored in the intracortical synapses. We assume a cortical structure which transforms the odor information in the oscillatory pattern to a slow DC feedback signal to the bulb. This feedback suppresses the bulbar response to the pre-existing odor, allowing subsequent odor objects to be segmented out for recognition.

  11. Directed network discovery with dynamic network modelling.

    Science.gov (United States)

    Anzellotti, Stefano; Kliemann, Dorit; Jacoby, Nir; Saxe, Rebecca

    2017-05-01

    Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modelling technique for network discovery (Dynamic Network Modelling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modelling introducing three key changes: (1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, (2) the tests take into account the magnitude and sign of each influence, and (3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. The encoding/retrieval flip: interactions between memory performance and memory stage and relationship to intrinsic cortical networks.

    Science.gov (United States)

    Huijbers, Willem; Schultz, Aaron P; Vannini, Patrizia; McLaren, Donald G; Wigman, Sarah E; Ward, Andrew M; Hedden, Trey; Sperling, Reisa A

    2013-07-01

    fMRI studies have linked the posteromedial cortex to episodic learning (encoding) and remembering (retrieval) processes. The posteromedial cortex is considered part of the default network and tends to deactivate during encoding but activate during retrieval, a pattern known as the encoding/retrieval flip. Yet, the exact relationship between the neural correlates of memory performance (hit/miss) and memory stage (encoding/retrieval) and the extent of overlap with intrinsic cortical networks remains to be elucidated. Using task-based fMRI, we isolated the pattern of activity associated with memory performance, memory stage, and the interaction between both. Using resting-state fMRI, we identified which intrinsic large-scale functional networks overlapped with regions showing task-induced effects. Our results demonstrated an effect of successful memory performance in regions associated with the control network and an effect of unsuccessful memory performance in the ventral attention network. We found an effect of memory retrieval in brain regions that span the default and control networks. Finally, we found an interaction between memory performance and memory stage in brain regions associated with the default network, including the posteromedial cortex, posterior parietal cortex, and parahippocampal cortex. We discuss these findings in relation to the encoding/retrieval flip. In general, the findings demonstrate that task-induced effects cut across intrinsic cortical networks. Furthermore, regions within the default network display functional dissociations, and this may have implications for the neural underpinnings of age-related memory disorders.

  13. A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings

    Directory of Open Access Journals (Sweden)

    Younes eZerouali

    2014-10-01

    Full Text Available Sleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity among cortical generators. For that purpose, we developed a wavelet-based approach aimed at imaging the synchronous oscillatory cortical networks from simultaneous MEG-EEG recordings. First, we detected spindles on the EEG and extracted the corresponding frequency-locked MEG activity under the form of an analytic ridge signal in the time-frequency plane (Zerouali et al., 2013. Secondly, we performed source reconstruction of the ridge signal within the Maximum Entropy on the Mean framework (Amblard et al., 2004, yielding a robust estimate of the cortical sources producing observed oscillations. Lastly, we quantified functional connectivity among cortical sources using phase-locking values. The main innovations of this methodology are 1 to reveal the dynamic behavior of functional networks resolved in the time-frequency plane and 2 to characterize functional connectivity among MEG sources through phase interactions. We showed, for the first time, that the switch from fast to slow oscillatory mode during sleep spindles is required for the emergence of specific patterns of connectivity. Moreover, we show that earlier synchrony during spindles was associated with mainly intra-hemispheric connectivity whereas later synchrony was associated with global long-range connectivity. We propose that our methodology can be a valuable tool for studying the connectivity underlying neural processes involving sleep spindles, such as memory, plasticity or aging.

  14. A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings.

    Science.gov (United States)

    Zerouali, Younes; Lina, Jean-Marc; Sekerovic, Zoran; Godbout, Jonathan; Dube, Jonathan; Jolicoeur, Pierre; Carrier, Julie

    2014-01-01

    Sleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity among cortical generators. For that purpose, we developed a wavelet-based approach aimed at imaging the synchronous oscillatory cortical networks from simultaneous MEG-EEG recordings. First, we detected spindles on the EEG and extracted the corresponding frequency-locked MEG activity under the form of an analytic ridge signal in the time-frequency plane (Zerouali et al., 2013). Secondly, we performed source reconstruction of the ridge signal within the Maximum Entropy on the Mean framework (Amblard et al., 2004), yielding a robust estimate of the cortical sources producing observed oscillations. Lastly, we quantified functional connectivity among cortical sources using phase-locking values. The main innovations of this methodology are (1) to reveal the dynamic behavior of functional networks resolved in the time-frequency plane and (2) to characterize functional connectivity among MEG sources through phase interactions. We showed, for the first time, that the switch from fast to slow oscillatory mode during sleep spindles is required for the emergence of specific patterns of connectivity. Moreover, we show that earlier synchrony during spindles was associated with mainly intra-hemispheric connectivity whereas later synchrony was associated with global long-range connectivity. We propose that our methodology can be a valuable tool for studying the connectivity underlying neural processes involving sleep spindles, such as memory, plasticity or aging.

  15. The slow oscillation in cortical and thalamic networks: mechanisms and functions

    Directory of Open Access Journals (Sweden)

    Garrett T. Neske

    2016-01-01

    Full Text Available During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz, synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states and almost complete silence (Down states. The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration.

  16. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim

    2014-12-03

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  17. Complement inhibition and statins prevent fetal brain cortical abnormalities in a mouse model of preterm birth.

    Science.gov (United States)

    Pedroni, Silvia M A; Gonzalez, Juan M; Wade, Jean; Jansen, Maurits A; Serio, Andrea; Marshall, Ian; Lennen, Ross J; Girardi, Guillermina

    2014-01-01

    Premature babies are particularly vulnerable to brain injury. In this study we focus on cortical brain damage associated with long-term cognitive, behavioral, attentional or socialization deficits in children born preterm. Using a mouse model of preterm birth (PTB), we demonstrated that complement component C5a contributes to fetal cortical brain injury. Disruption of cortical dendritic and axonal cytoarchitecture was observed in PTB-mice. Fetuses deficient in C5aR (-/-) did not show cortical brain damage. Treatment with antibody anti-C5, that prevents generation of C5a, also prevented cortical fetal brain injury in PTB-mice. C5a also showed a detrimental effect on fetal cortical neuron development and survival in vitro. Increased glutamate release was observed in cortical neurons in culture exposed to C5a. Blockade of C5aR prevented glutamate increase and restored neurons dendritic and axonal growth and survival. Similarly, increased glutamate levels - measured by (1)HMRS - were observed in vivo in PTB-fetuses compared to age-matched controls. The blockade of glutamate receptors prevented C5a-induced abnormal growth and increased cell death in isolated fetal cortical neurons. Simvastatin and pravastatin prevented cortical fetal brain developmental and metabolic abnormalities -in vivo and in vitro. Neuroprotective effects of statins were mediated by Akt/PKB signaling pathways. This study shows that complement activation plays a crucial role in cortical fetal brain injury in PTL and suggests that complement inhibitors and statins might be good therapeutic options to improve neonatal outcomes in preterm birth. © 2013.

  18. Brainlab: a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator

    Directory of Open Access Journals (Sweden)

    Richard P Drewes

    2009-05-01

    Full Text Available Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading ``glue'' tool for managing all sorts of complex programmatictasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS environment in particular. Brainlab is an integrated model building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS (the NeoCortical Simulator.

  19. Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator.

    Science.gov (United States)

    Drewes, Rich; Zou, Quan; Goodman, Philip H

    2009-01-01

    Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading "glue" tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS.

  20. Functional brain network organisation of children between 2 and 5 years derived from reconstructed activity of cortical sources of high-density EEG recordings.

    Science.gov (United States)

    Bathelt, Joe; O'Reilly, Helen; Clayden, Jonathan D; Cross, J Helen; de Haan, Michelle

    2013-11-15

    There is increasing interest in applying connectivity analysis to brain measures (Rubinov and Sporns, 2010), but most studies have relied on fMRI, which substantially limits the participant groups and numbers that can be studied. High-density EEG recordings offer a comparatively inexpensive easy-to-use alternative, but require channel-level connectivity analysis which currently lacks a common analytic framework and is very limited in spatial resolution. To address this problem, we have developed a new technique for studies of network development that overcomes the spatial constraint and obtains functional networks of cortical areas by using EEG source reconstruction with age-matched average MRI templates (He et al., 1999). In contrast to previously reported channel-level analysis, this approach provides information about the cortical areas most likely to be involved in the network as well as their functional relationship (Babiloni et al., 2005; De Vico Fallani et al., 2007). In this study, we applied source reconstruction with age-matched templates to task-free high-density EEG recordings in typically-developing children between 2 and 6 years of age (O'Reilly, 2012). Graph theory was then applied to the association strengths of 68 cortical regions of interest based on the Desikan-Killiany atlas. We found linear increases of mean node degree, mean clustering coefficient and maximum betweenness centrality between 2 years and 6 years of age. Characteristic path length was negatively correlated with age. The correlation of the network measures with age indicates network development towards more closely integrated networks similar to reports from other imaging modalities (Fair et al., 2008; Power et al., 2010). We also applied eigenvalue decomposition to obtain functional modules (Clayden et al., 2013). Connection strength within these modules did not change with age, and the modules resembled hub networks previously described for MRI (Hagmann et al., 2010; Power et al

  1. Structural alteration of the dorsal visual network in DLB patients with visual hallucinations: a cortical thickness MRI study.

    Science.gov (United States)

    Delli Pizzi, Stefano; Franciotti, Raffaella; Tartaro, Armando; Caulo, Massimo; Thomas, Astrid; Onofrj, Marco; Bonanni, Laura

    2014-01-01

    Visual hallucinations (VH) represent one of the core features in discriminating dementia with Lewy bodies (DLB) from Alzheimer's Disease (AD). Previous studies reported that in DLB patients functional alterations of the parieto-occipital regions were correlated with the presence of VH. The aim of our study was to assess whether morphological changes in specific cortical regions of DLB could be related to the presence and severity of VH. We performed a cortical thickness analysis on magnetic resonance imaging data in a cohort including 18 DLB patients, 15 AD patients and 14 healthy control subjects. Relatively to DLB group, correlation analysis between the cortical thickness and the Neuropsychiatric Inventory (NPI) hallucination item scores was also performed. Cortical thickness was reduced bilaterally in DLB compared to controls in the pericalcarine and lingual gyri, cuneus, precuneus, superior parietal gyrus. Cortical thinning was found bilaterally in AD compared to controls in temporal cortex including the superior and middle temporal gyrus, part of inferior temporal cortex, temporal pole and insula. Inferior parietal and supramarginal gyri were also affected bilaterally in AD as compared to controls. The comparison between DLB and AD evidenced cortical thinning in DLB group in the right posterior regions including superior parietal gyrus, precuneus, cuneus, pericalcarine and lingual gyri. Furthermore, the correlation analysis between cortical thickness and NPI hallucination item scores showed that the structural alteration in the dorsal visual regions including superior parietal gyrus and precuneus closely correlated with the occurrence and severity of VH. We suggest that structural changes in key regions of the dorsal visual network may play a crucial role in the physiopathology of VH in DLB patients.

  2. Structural alteration of the dorsal visual network in DLB patients with visual hallucinations: a cortical thickness MRI study.

    Directory of Open Access Journals (Sweden)

    Stefano Delli Pizzi

    Full Text Available Visual hallucinations (VH represent one of the core features in discriminating dementia with Lewy bodies (DLB from Alzheimer's Disease (AD. Previous studies reported that in DLB patients functional alterations of the parieto-occipital regions were correlated with the presence of VH. The aim of our study was to assess whether morphological changes in specific cortical regions of DLB could be related to the presence and severity of VH. We performed a cortical thickness analysis on magnetic resonance imaging data in a cohort including 18 DLB patients, 15 AD patients and 14 healthy control subjects. Relatively to DLB group, correlation analysis between the cortical thickness and the Neuropsychiatric Inventory (NPI hallucination item scores was also performed. Cortical thickness was reduced bilaterally in DLB compared to controls in the pericalcarine and lingual gyri, cuneus, precuneus, superior parietal gyrus. Cortical thinning was found bilaterally in AD compared to controls in temporal cortex including the superior and middle temporal gyrus, part of inferior temporal cortex, temporal pole and insula. Inferior parietal and supramarginal gyri were also affected bilaterally in AD as compared to controls. The comparison between DLB and AD evidenced cortical thinning in DLB group in the right posterior regions including superior parietal gyrus, precuneus, cuneus, pericalcarine and lingual gyri. Furthermore, the correlation analysis between cortical thickness and NPI hallucination item scores showed that the structural alteration in the dorsal visual regions including superior parietal gyrus and precuneus closely correlated with the occurrence and severity of VH. We suggest that structural changes in key regions of the dorsal visual network may play a crucial role in the physiopathology of VH in DLB patients.

  3. Model Diagnostics for Bayesian Networks

    Science.gov (United States)

    Sinharay, Sandip

    2006-01-01

    Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…

  4. An artificial network model for estimating the network structure underlying partially observed neuronal signals.

    Science.gov (United States)

    Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

    2014-01-01

    Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed.

  5. Estimation of cortical connectivity from EEG using state-space models.

    Science.gov (United States)

    Cheung, Bing Leung Patrick; Riedner, Brady Alexander; Tononi, Giulio; Van Veen, Barry D

    2010-09-01

    A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp-recorded EEG. A state equation represents the MVAR model of cortical dynamics, while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume that the cortical signals originate from known regions of cortex, but the spatial distribution of activity within each region is unknown. An expectation-maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next, an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.

  6. Mining and modeling character networks

    CERN Document Server

    Bonato, Anthony; Elenberg, Ethan R; Gleich, David F; Hou, Yangyang

    2016-01-01

    We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of beveridge, we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http://moviegalaxies.com/ database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we ...

  7. Optimization of multifocal transcranial current stimulation for weighted cortical pattern targeting from realistic modeling of electric fields.

    Science.gov (United States)

    Ruffini, Giulio; Fox, Michael D; Ripolles, Oscar; Miranda, Pedro Cavaleiro; Pascual-Leone, Alvaro

    2014-04-01

    Recently, multifocal transcranial current stimulation (tCS) devices using several relatively small electrodes have been used to achieve more focal stimulation of specific cortical targets. However, it is becoming increasingly recognized that many behavioral manifestations of neurological and psychiatric disease are not solely the result of abnormality in one isolated brain region but represent alterations in brain networks. In this paper we describe a method for optimizing the configuration of multifocal tCS for stimulation of brain networks, represented by spatially extended cortical targets. We show how, based on fMRI, PET, EEG or other data specifying a target map on the cortical surface for excitatory, inhibitory or neutral stimulation and a constraint on the maximal number of electrodes, a solution can be produced with the optimal currents and locations of the electrodes. The method described here relies on a fast calculation of multifocal tCS electric fields (including components normal and tangential to the cortical boundaries) using a five layer finite element model of a realistic head. Based on the hypothesis that the effects of current stimulation are to first order due to the interaction of electric fields with populations of elongated cortical neurons, it is argued that the optimization problem for tCS stimulation can be defined in terms of the component of the electric field normal to the cortical surface. Solutions are found using constrained least squares to optimize current intensities, while electrode number and their locations are selected using a genetic algorithm. For direct current tCS (tDCS) applications, we provide some examples of this technique using an available tCS system providing 8 small Ag/AgCl stimulation electrodes. We demonstrate the approach both for localized and spatially extended targets defined using rs-fcMRI and PET data, with clinical applications in stroke and depression. Finally, we extend these ideas to more general

  8. Cortical stimulation and tooth pulp evoked potentials in rats: a model of direct anti-nociception.

    Science.gov (United States)

    Rusina, Robert; Barek, Stephane; Vaculin, Simon; Azérad, Jean; Rokyta, Richard

    2010-01-01

    While the effect of cortex stimulation on pain control is widely accepted, its physiological basis remains poorly understood. We chose an animal model of pain to study the influence of sensorimotor cortex stimulation on tooth pulp stimulation evoked potentials (TPEPs). Fifteen awake rats implanted with tooth pulp, cerebral cortex, and digastric muscle electrodes were divided into three groups, receiving 60 Hz, 40 Hz and no cortical stimulation, respectively. TPEPs were recorded before, one, three and five hours after continuous stimulation. We observed an inverse relationship between TPEP amplitude and latency with increasing tooth pulp stimulation. The amplitudes of the early components of TPEPs increased and their latency decreased with increasing tooth pulp stimulation intensity. Cortical stimulation decreased the amplitude of TPEPs; however, neither the latencies of TPEPs nor the jaw-opening reflex were changed after cortical stimulation. The decrease in amplitude of TPEPs after cortical stimulation may reflect its anti-nociceptive effect.

  9. Prefrontal cortical network activity: Opposite effects of psychedelic hallucinogens and D1/D5 dopamine receptor activation.

    Science.gov (United States)

    Lambe, E K; Aghajanian, G K

    2007-03-30

    The fine-tuning of network activity provides a modulating influence on how information is processed and interpreted in the brain. Here, we use brain slices of rat prefrontal cortex to study how recurrent network activity is affected by neuromodulators known to alter normal cortical function. We previously determined that glutamate spillover and stimulation of extrasynaptic N-methyl-d-aspartic acid (NMDA) receptors are required to support hallucinogen-induced cortical network activity. Since microdialysis studies suggest that psychedelic hallucinogens and dopamine D1/D5 receptor agonists have opposite effects on extracellular glutamate in prefrontal cortex, we hypothesized that these two families of psychoactive drugs would have opposite effects on cortical network activity. We found that network activity can be enhanced by 2,5-dimethoxy-4-iodoamphetamine (DOI) (a psychedelic hallucinogen that is a partial agonist of 5-HT(2A/2C) receptors) and suppressed by the selective D1/D5 agonist SKF 38393. This suppression could be mimicked by direct activation of adenylyl cyclase with forskolin or by addition of a cAMP analog. These findings are consistent with previous work showing that activation of adenylyl cyclase can upregulate neuronal glutamate transporters, thereby decreasing synaptic spillover of glutamate. Consistent with this hypothesis, a low concentration of the glutamate transporter inhibitor threo-beta-benzoylaspartic acid (TBOA) restored electrically-evoked recurrent activity in the presence of a selective D1/D5 agonist, whereas recurrent activity in the presence of a low level of the GABA(A) antagonist bicuculline was not resistant to suppression by the D1/D5 agonist. The tempering of network UP states by D1/D5 receptor activation may have implications for the proposed use of D1/D5 agonists in the treatment of schizophrenia.

  10. Cortical Amyloid Beta in Cognitively Normal Elderly Adults is Associated with Decreased Network Efficiency within the Cerebro-Cerebellar System.

    Science.gov (United States)

    Steininger, Stefanie C; Liu, Xinyang; Gietl, Anton; Wyss, Michael; Schreiner, Simon; Gruber, Esmeralda; Treyer, Valerie; Kälin, Andrea; Leh, Sandra; Buck, Alfred; Nitsch, Roger M; Prüssmann, Klaas P; Hock, Christoph; Unschuld, Paul G

    2014-01-01

    Deposition of cortical amyloid beta (Aβ) is a correlate of aging and a risk factor for Alzheimer disease (AD). While several higher order cognitive processes involve functional interactions between cortex and cerebellum, this study aims to investigate effects of cortical Aβ deposition on coupling within the cerebro-cerebellar system. We included 15 healthy elderly subjects with normal cognitive performance as assessed by neuropsychological testing. Cortical Aβ was quantified using (11)carbon-labeled Pittsburgh compound B positron-emission-tomography late frame signals. Volumes of brain structures were assessed by applying an automated parcelation algorithm to three dimensional magnetization-prepared rapid gradient-echo T1-weighted images. Basal functional network activity within the cerebro-cerebellar system was assessed using blood-oxygen-level dependent resting state functional magnetic resonance imaging at the high field strength of 7 T for measuring coupling between cerebellar seeds and cerebral gray matter. A bivariate regression approach was applied for identification of brain regions with significant effects of individual cortical Aβ load on coupling. Consistent with earlier reports, a significant degree of positive and negative coupling could be observed between cerebellar seeds and cerebral voxels. Significant positive effects of cortical Aβ load on cerebro-cerebellar coupling resulted for cerebral brain regions located in inferior temporal lobe, prefrontal cortex, hippocampus, parahippocampal gyrus, and thalamus. Our findings indicate that brain amyloidosis in cognitively normal elderly subjects is associated with decreased network efficiency within the cerebro-cerebellar system. While the identified cerebral regions are consistent with established patterns of increased sensitivity for Aβ-associated neurodegeneration, additional studies are needed to elucidate the relationship between dysfunction of the cerebro-cerebellar system and risk for AD.

  11. A Computational Growth Model for Measuring Dynamic Cortical Development in the First Year of Life

    Science.gov (United States)

    Nie, Jingxin; Li, Gang; Wang, Li; Gilmore, John H.; Lin, Weili

    2012-01-01

    Human cerebral cortex develops extremely fast in the first year of life. Quantitative measurement of cortical development during this early stage plays an important role in revealing the relationship between cortical structural and high-level functional development. This paper presents a computational growth model to simulate the dynamic development of the cerebral cortex from birth to 1 year old by modeling the cerebral cortex as a deformable elastoplasticity surface driven via a growth model. To achieve a high accuracy, a guidance model is also incorporated to estimate the growth parameters and cortical shapes at later developmental stages. The proposed growth model has been applied to 10 healthy subjects with longitudinal brain MR images acquired at every 3 months from birth to 1 year old. The experimental results show that our proposed method can capture the dynamic developmental process of the cortex, with the average surface distance error smaller than 0.6 mm compared with the ground truth surfaces, and the results also show that 1) the curvedness and sharpness decrease from 2 weeks to 12 months and 2) the frontal lobe shows rapidly increasing cortical folding during this period, with relatively slower increase of the cortical folding in the occipital and parietal lobes. PMID:22047969

  12. Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization

    Science.gov (United States)

    Tan, Weng Chun; Mat Isa, Nor Ashidi

    2016-01-01

    In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581

  13. Mutation of Semaphorin-6A disrupts limbic and cortical connectivity and models neurodevelopmental psychopathology.

    LENUS (Irish Health Repository)

    2011-01-01

    Psychiatric disorders such as schizophrenia and autism are characterised by cellular disorganisation and dysconnectivity across the brain and can be caused by mutations in genes that control neurodevelopmental processes. To examine how neurodevelopmental defects can affect brain function and behaviour, we have comprehensively investigated the consequences of mutation of one such gene, Semaphorin-6A, on cellular organisation, axonal projection patterns, behaviour and physiology in mice. These analyses reveal a spectrum of widespread but subtle anatomical defects in Sema6A mutants, notably in limbic and cortical cellular organisation, lamination and connectivity. These mutants display concomitant alterations in the electroencephalogram and hyper-exploratory behaviour, which are characteristic of models of psychosis and reversible by the antipsychotic clozapine. They also show altered social interaction and deficits in object recognition and working memory. Mice with mutations in Sema6A or the interacting genes may thus represent a highly informative model for how neurodevelopmental defects can lead to anatomical dysconnectivity, resulting, either directly or through reactive mechanisms, in dysfunction at the level of neuronal networks with associated behavioural phenotypes of relevance to psychiatric disorders. The biological data presented here also make these genes plausible candidates to explain human linkage findings for schizophrenia and autism.

  14. Abnormal activation of the motor cortical network in idiopathic scoliosis demonstrated by functional MRI.

    Science.gov (United States)

    Domenech, Julio; García-Martí, G; Martí-Bonmatí, L; Barrios, C; Tormos, J M; Pascual-Leone, A

    2011-07-01

    The aetiology of idiopathic scoliosis (IS) remains unknown, but there is growing support for the possibility of an underlying neurological disorder. Functional magnetic resonance imaging (fMRI) can characterize the abnormal activation of the sensorimotor brain network in movement disorders and could provide further insights into the neuropathogenesis of IS. Twenty subjects were included in the study; 10 adolescents with IS (mean age of 15.2, 8 girls and 2 boys) and 10 age-matched healthy controls. The average Cobb angle of the primary curve in the IS patients was 35° (range 27°-55°). All participants underwent a block-design fMRI experiment in a 1.5-Tesla MRI scanner to explore cortical activation following a simple motor task. Rest periods alternated with activation periods during which participants were required to open and close their hand at an internally paced rate of approximately 1 Hz. Data were analyzed with Statistical Parametric Mapping (SPM5) including age, sex and laterality as nuisance variables to minimise the presence of bias in the results. Compared to controls, IS patients showed significant increases in blood oxygenation level dependent (BOLD) activity in contralateral supplementary motor area when performing the motor task with either hand. No significant differences were observed when testing between groups in the functional activation in the primary motor cortex, premotor cortex and somatosensory cortex. Additionally, the IS group showed a greater interhemispheric asymmetry index than the control group (0.30 vs. 0.13, p motor areas during movement execution in patients with IS. These findings support the hypothesis that a sensorimotor integration disorder underlies the pathogenesis of IS.

  15. Linking microcircuit dysfunction to cognitive impairment: effects of disinhibition associated with schizophrenia in a cortical working memory model.

    Science.gov (United States)

    Murray, John D; Anticevic, Alan; Gancsos, Mark; Ichinose, Megan; Corlett, Philip R; Krystal, John H; Wang, Xiao-Jing

    2014-04-01

    Excitation-inhibition balance (E/I balance) is a fundamental property of cortical microcircuitry. Disruption of E/I balance in prefrontal cortex is hypothesized to underlie cognitive deficits observed in neuropsychiatric illnesses such as schizophrenia. To elucidate the link between these phenomena, we incorporated synaptic disinhibition, via N-methyl-D-aspartate receptor perturbation on interneurons, into a network model of spatial working memory (WM). At the neural level, disinhibition broadens the tuning of WM-related, stimulus-selective persistent activity patterns. The model predicts that this change at the neural level leads to 2 primary behavioral deficits: 1) increased behavioral variability that degrades the precision of stored information and 2) decreased ability to filter out distractors during WM maintenance. We specifically tested the main model prediction, broadened WM representation under disinhibition, using behavioral data from human subjects performing a spatial WM task combined with ketamine infusion, a pharmacological model of schizophrenia hypothesized to induce disinhibition. Ketamine increased errors in a pattern predicted by the model. Finally, as proof-of-principle, we demonstrate that WM deteriorations in the model can be ameliorated by compensations that restore E/I balance. Our findings identify specific ways by which cortical disinhibition affects WM, suggesting new experimental designs for probing the brain mechanisms of WM deficits in schizophrenia.

  16. Resistor mesh model of a spherical head: part 2: a review of applications to cortical mapping.

    Science.gov (United States)

    Chauveau, N; Morucci, J P; Franceries, X; Celsis, P; Rigaud, B

    2005-11-01

    A resistor mesh model (RMM) has been validated with reference to the analytical model by consideration of a set of four dipoles close to the cortex. The application of the RMM to scalp potential interpolation was detailed in Part 1. Using the RMM and the same four dipoles, the different methods of cortical mapping were compared and have shown the potentiality of this RMM for obtaining current and potential cortical distributions. The lead-field matrices are well-adapted tools, but the use of a square matrix of high dimension does not permit the inverse solution to be improved in the presence of noise, as a regularisation technique is necessary with noisy data. With the RMM, the transfer matrix and the cortical imaging technique proved to be easy to implement. Further development of the RMM will include application to more realistic head models with more accurate conductivities.

  17. Increased interictal cerebral glucose metabolism in a cortical-subcortical network in drug naive patients with cryptogenic temporal lobe epilepsy.

    Science.gov (United States)

    Franceschi, M; Lucignani, G; Del Sole, A; Grana, C; Bressi, S; Minicucci, F; Messa, C; Canevini, M P; Fazio, F

    1995-01-01

    Positron emission tomography with [18F]-2-fluoro-2-deoxy-D-glucose ([18F]FDG) has been used to assess the pattern of cerebral metabolism in different types of epilepsies. However, PET with [18F]FDG has never been used to evaluate drug naive patients with cryptogenic temporal lobe epilepsy, in whom the mechanism of origin and diffusion of the epileptic discharge may differ from that underlying other epilepsies. In a group of patients with cryptogenic temporal lobe epilepsy, never treated with antiepileptic drugs, evidence has been found of significant interictal glucose hypermetabolism in a bilateral neural network including the temporal lobes, thalami, basal ganglia, and cingular cortices. The metabolism in these areas and frontal lateral cortex enables the correct classification of all patients with temporal lobe epilepsy and controls by discriminant function analysis. Other cortical areas--namely, frontal basal and lateral, temporal mesial, and cerebellar cortices--had bilateral increases of glucose metabolism ranging from 10 to 15% of normal controls, although lacking stringent statistical significance. This metabolic pattern could represent a pathophysiological state of hyperactivity predisposing to epileptic discharge generation or diffusion, or else a network of inhibitory circuits activated to prevent the diffusion of the epileptic discharge. PMID:7561924

  18. Regional vulnerability of longitudinal cortical association connectivity: Associated with structural network topology alterations in preterm children with cerebral palsy.

    Science.gov (United States)

    Ceschin, Rafael; Lee, Vince K; Schmithorst, Vince; Panigrahy, Ashok

    2015-01-01

    Preterm born children with spastic diplegia type of cerebral palsy and white matter injury or periventricular leukomalacia (PVL), are known to have motor, visual and cognitive impairments. Most diffusion tensor imaging (DTI) studies performed in this group have demonstrated widespread abnormalities using averaged deterministic tractography and voxel-based DTI measurements. Little is known about structural network correlates of white matter topography and reorganization in preterm cerebral palsy, despite the availability of new therapies and the need for brain imaging biomarkers. Here, we combined novel post-processing methodology of probabilistic tractography data in this preterm cohort to improve spatial and regional delineation of longitudinal cortical association tract abnormalities using an along-tract approach, and compared these data to structural DTI cortical network topology analysis. DTI images were acquired on 16 preterm children with cerebral palsy (mean age 5.6 ± 4) and 75 healthy controls (mean age 5.7 ± 3.4). Despite mean tract analysis, Tract-Based Spatial Statistics (TBSS) and voxel-based morphometry (VBM) demonstrating diffusely reduced fractional anisotropy (FA) reduction in all white matter tracts, the along-tract analysis improved the detection of regional tract vulnerability. The along-tract map-structural network topology correlates revealed two associations: (1) reduced regional posterior-anterior gradient in FA of the longitudinal visual cortical association tracts (inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, optic radiation, posterior thalamic radiation) correlated with reduced posterior-anterior gradient of intra-regional (nodal efficiency) metrics with relative sparing of frontal and temporal regions; and (2) reduced regional FA within frontal-thalamic-striatal white matter pathways (anterior limb/anterior thalamic radiation, superior longitudinal fasciculus and cortical spinal tract) correlated with

  19. Model cortical association fields account for the time course and dependence on target complexity of human contour perception.

    Directory of Open Access Journals (Sweden)

    Vadas Gintautas

    2011-10-01

    Full Text Available Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas distributed among groups of randomly rotated fragments (clutter. The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms, followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least [Formula: see text] ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas.

  20. Internet Network Resource Information Model

    Institute of Scientific and Technical Information of China (English)

    陈传峰; 李增智; 唐亚哲; 刘康平

    2002-01-01

    The foundation of any network management systens is a database that con-tains information about the network resources relevant to the management tasks. A networkinformation model is an abstraction of network resources, including both managed resources andmanaging resources. In the SNMP-based management framework, management information isdefined almost exclusively from a "device" viewpoint, namely, managing a network is equiva-lent to managing a collection of individual nodes. Aiming at making use of recent advances indistributed computing and in object-oriented analysis and design, the Internet management ar-chitecture can also be based on the Open Distributed Processing Reference Model (RM-ODP).The purpose of this article is to provide an Internet Network Resource Information Model.First, a layered management information architecture will be discussed. Then the Internetnetwork resource information model is presented. The information model is specified usingObject-Z.

  1. From working memory to epilepsy: Dynamics of facilitation and inhibition in a cortical network

    Science.gov (United States)

    Verduzco-Flores, Sergio; Ermentrout, Bard; Bodner, Mark

    2009-03-01

    Persistent states are believed to be the correlate for short-term or working memory. Using a previously derived model for working memory, we show that disruption of the lateral inhibition can lead to a variety of pathological states. These states are analogs of reflex or pattern-sensitive epilepsy. Simulations, numerical bifurcation analysis, and fast-slow decomposition are used to explore the dynamics of this network.

  2. A new cortical thickness mapping method with application to an in vivo finite element model.

    Science.gov (United States)

    Kim, Young Ho; Kim, Jong-Eun; Eberhardt, Alan W

    2014-01-01

    Finite element modelling of musculoskeletal systems, with geometrical structures constructed from computed tomography (CT) scans, is a useful and powerful tool for biomechanical studies. The use of CT scans from living human subjects, however, is still limited. Accurate reconstruction of thin cortical bone structures from CT scans of living human subjects is especially problematic, due to low CT resolution that results from mandatory low radiation doses and/or involuntary movements of the subject. In this study, a new method for mapping cortical thickness is described. Using the method, cortical thickness measurements of a coxal (pelvis) bone obtained from CT scans of a cadaver were mapped to the coxal geometry as obtained through CT scans of a live human subject, resulting in accurate cortical thickness while maintaining geometric fidelity of the live subject. The mapping procedure includes shape-preserving parameterisation, mesh movement and interpolation of thickness using a search algorithm. The methodology is applicable to modelling of other bones where accurate cortical thickness is needed and for which such data exist.

  3. Network Simulation Models

    Science.gov (United States)

    2008-12-01

    well, then a Euclidean distance would be appropriate. The quadratic assignment procedure ( QAP ) (Krackhardt, 1987) could be used to compare the...Networks. Journal of Applied Psychology, 71(1): 50-55. Krackhardt, D. (1987). QAP Partialling as a Test of Spuriousness. Social Networks, 9, 171-186

  4. Assortative model for social networks

    Science.gov (United States)

    Catanzaro, Michele; Caldarelli, Guido; Pietronero, Luciano

    2004-09-01

    In this Brief Report we present a version of a network growth model, generalized in order to describe the behavior of social networks. The case of study considered is the preprint archive at cul.arxiv.org. Each node corresponds to a scientist, and a link is present whenever two authors wrote a paper together. This graph is a nice example of degree-assortative network, that is, to say a network where sites with similar degree are connected to each other. The model presented is one of the few able to reproduce such behavior, giving some insight on the microscopic dynamics at the basis of the graph structure.

  5. Developing Personal Network Business Models

    DEFF Research Database (Denmark)

    Saugstrup, Dan; Henten, Anders

    2006-01-01

    on the 'state of the art' in the field of business modeling. Furthermore, the paper suggests three generic business models for PNs: a service oriented model, a self-organized model, and a combination model. Finally, examples of relevant services and applications in relation to three different cases......The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...... are presented and analyzed in light of business modeling of PN....

  6. On-off intermittency of thalamo-cortical neuronal network oscillations in the electroencephalogram of rodents with genetic predisposition to absence epilepsy

    Science.gov (United States)

    Hramov, Alexander E.; Grubov, Vadim V.; Pavlov, Alexey N.; Sitnikova, Evgenija Yu.; Koronovskii, Alexey A.; Runnova, Anastasija E.; Shurugina, Sveltlana A.; Ivanov, Alexey V.

    2013-02-01

    Spike-wave discharges are electroencephalographic hallmarks of absence epilepsy. Spike-wave discharges are known to originate from thalamo-cortical neuronal network that normally produces sleep spindle oscillations. Although both sleep spindles and spike-wave discharges are considered as thalamo-cortical oscillations, functional relationship between them is still uncertain. The present study describes temporal dynamics of spike-wave discharges and sleep spindles as determined in long-time electroencephalograms (EEG) recorded in WAG/Rij rat model of absence epilepsy. We have proposed the wavelet-based method for the automatic detection of spike-wave discharges, sleep spindles (10-15Hz) and 5-9Hz oscillations in EEG. It was found that non-linear dynamics of spike-wave discharges and sleep spindles fits well to the law of 'on-off intermittency'. Intermittency in sleep spindles and spike-wave discharges implies that (1) temporal dynamics of these oscillations are deterministic in nature, and (2) it might be controlled by a system-level mechanism responsible for circadian modulation of neuronal network activity.

  7. Network models in anatomical systems.

    Science.gov (United States)

    Esteve-Altava, Borja; Marugán-Lobón, Jesús; Botella, Héctor; Rasskin-Gutman, Diego

    2011-01-01

    Network theory has been extensively used to model the underlying structure of biological processes. From genetics to ecology, network thinking is changing our understanding of complex systems, specifically how their internal structure determines their overall behavior. Concepts such as hubs, scale-free or small-world networks, common in the complexity literature, are now used more and more in sociology, neurosciences, as well as other anthropological fields. Even though the use of network models is nowadays so widely applied, few attempts have been carried out to enrich our understanding in the classical morphological sciences such as in comparative anatomy or physical anthropology. The purpose of this article is to introduce the usage of network tools in morphology; specifically by building anatomical networks, dealing with the most common analyses and problems, and interpreting their outcome.

  8. Telecommunications network modelling, planning and design

    CERN Document Server

    Evans, Sharon

    2003-01-01

    Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

  9. Individual cortical current density reconstructions of the semantic N400 effect: using a generalized minimum norm model with different constraints (L1 and L2 norm).

    Science.gov (United States)

    Haan, H; Streb, J; Bien, S; Rösler, F

    2000-11-01

    Event-related brain potentials were recorded to study whether verbs and nouns activate topographically distinct cortical generators. Fifteen subjects performed a primed lexical decision task with verb/verb and noun/noun pairs. The relatedness between prime and target items was varied in three steps (unrelated, moderately, and strongly related) and the EEG was recorded from 124 scalp electrodes. The topography of cortical sources of the N400 effect was evaluated by standardized differences scores and by cortical current source estimates which were constrained by the individual MRI-determined cortex anatomy. A behavioral priming effect and a substantial N400 effect was found for both word categories. However, the topography of the grand average N400 effect of verbs and nouns did not differ, neither for raw nor for standardized amplitudes. Cortical current source estimates of the N400 effect revealed a very broad and scattered distribution of active locations with pronounced interindividual differences. Cortical current source estimates obtained with the L1-norm and L2-norm model, respectively, differed in the distribution of sources over the cortex but converged on the same "hot spots." The data give no indication that the N400 effect is generated by word category-specific networks which have a different topography. The marked individual differences are discussed with respect to the involved processes and the current source estimation procedures.

  10. Campus network security model study

    Science.gov (United States)

    Zhang, Yong-ku; Song, Li-ren

    2011-12-01

    Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.

  11. Human cortical control of hand movements: parietofrontal networks for reaching, grasping, and pointing.

    Science.gov (United States)

    Filimon, Flavia

    2010-08-01

    In primates, control of the limb depends on many cortical areas. Whereas specialized parietofrontal circuits have been proposed for different movements in macaques, functional neuroimaging in humans has revealed widespread, overlapping activations for hand and eye movements and for movements such as reaching and grasping. This review examines the involvement of frontal and parietal areas in hand and arm movements in humans as revealed with functional neuroimaging. The degree of functional specialization, possible homologies with macaque cortical regions, and differences between frontal and posterior parietal areas are discussed, as well as a possible organization of hand movements with respect to different spatial reference frames. The available evidence supports a cortical organization along gradients of sensory (visual to somatosensory) and effector (eye to hand) preferences.

  12. Motor Cortical Networks for Skilled Movements Have Dynamic Properties That Are Related to Accurate Reaching

    Directory of Open Access Journals (Sweden)

    David F. Putrino

    2011-01-01

    Full Text Available Neurons in the Primary Motor Cortex (MI are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total were acquired. A point process generalized linear model (GLM was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors.

  13. Optimizing finite element predictions of local subchondral bone structural stiffness using neural network-derived density-modulus relationships for proximal tibial subchondral cortical and trabecular bone.

    Science.gov (United States)

    Nazemi, S Majid; Amini, Morteza; Kontulainen, Saija A; Milner, Jaques S; Holdsworth, David W; Masri, Bassam A; Wilson, David R; Johnston, James D

    2017-01-01

    Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local structural stiffness at the proximal tibial subchondral surface. Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting stiffness. Finite element modeling predicted 81% of experimental stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5g/cm(3) density. In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral structural stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Microstructural changes in ischemic cortical gray matter predicted by a model of diffusion-weighted MRI

    DEFF Research Database (Denmark)

    Vestergaard-Poulsen, Peter; Hansen, Brian; Østergaard, Leif

    2007-01-01

    PURPOSE: To understand the diffusion attenuated MR signal from normal and ischemic brain tissue in order to extract structural and physiological information using mathematical modeling, taking into account the transverse relaxation rates in gray matter. MATERIALS AND METHODS: We fit our diffusion...... model to the diffusion-weighted MR signal obtained from cortical gray matter in healthy subjects. Our model includes variable volume fractions, intracellular restriction effects, and exchange between compartments in addition to individual diffusion coefficients and transverse relaxation rates for each...

  15. The cortical cytoskeletal network and cell-wall dynamics in the unicellular charophycean green alga Penium margaritaceum.

    Science.gov (United States)

    Ochs, Julie; LaRue, Therese; Tinaz, Berke; Yongue, Camille; Domozych, David S

    2014-10-01

    Penium margaritaceum is a unicellular charophycean green alga with a unique bi-directional polar expansion mechanism that occurs at the central isthmus zone prior to cell division. This entails the focused deposition of cell-wall polymers coordinated by the activities of components of the endomembrane system and cytoskeletal networks. The goal of this study was to elucidate the structural organization of the cortical cytoskeletal network during the cell cycle and identify its specific functional roles during key cell-wall developmental events: pre-division expansion and cell division. Microtubules and actin filaments were labelled during various cell cycle phases with an anti-tubulin antibody and rhodamine phalloidin, respectively. Chemically induced disruption of the cytoskeleton was used to elucidate specific functional roles of microtubules and actin during cell expansion and division. Correlation of cytoskeletal dynamics with cell-wall development included live cell labelling with wall polymer-specific antibodies and electron microscopy. The cortical cytoplasm of Penium is highlighted by a band of microtubules found at the cell isthmus, i.e. the site of pre-division wall expansion. This band, along with an associated, transient band of actin filaments, probably acts to direct the deposition of new wall material and to mark the plane of the future cell division. Two additional bands of microtubules, which we identify as satellite bands, arise from the isthmus microtubular band at the onset of expansion and displace toward the poles during expansion, ultimately marking the isthmus of future daughter cells. Treatment with microtubule and actin perturbation agents reversibly stops cell division. The cortical cytoplasm of Penium contains distinct bands of microtubules and actin filaments that persist through the cell cycle. One of these bands, termed the isthmus microtubule band, or IMB, marks the site of both pre-division wall expansion and the zone where a cross

  16. Dampened hippocampal oscillations and enhanced spindle activity in an asymptomatic model of developmental cortical malformations

    Science.gov (United States)

    Cid, Elena; Gomez-Dominguez, Daniel; Martin-Lopez, David; Gal, Beatriz; Laurent, François; Ibarz, Jose M.; Francis, Fiona; Menendez de la Prida, Liset

    2014-01-01

    Developmental cortical malformations comprise a large spectrum of histopathological brain abnormalities and syndromes. Their genetic, developmental and clinical complexity suggests they should be better understood in terms of the complementary action of independently timed perturbations (i.e., the multiple-hit hypothesis). However, understanding the underlying biological processes remains puzzling. Here we induced developmental cortical malformations in offspring, after intraventricular injection of methylazoxymethanol (MAM) in utero in mice. We combined extensive histological and electrophysiological studies to characterize the model. We found that MAM injections at E14 and E15 induced a range of cortical and hippocampal malformations resembling histological alterations of specific genetic mutations and transplacental mitotoxic agent injections. However, in contrast to most of these models, intraventricularly MAM-injected mice remained asymptomatic and showed no clear epilepsy-related phenotype as tested in long-term chronic recordings and with pharmacological manipulations. Instead, they exhibited a non-specific reduction of hippocampal-related brain oscillations (mostly in CA1); including theta, gamma and HFOs; and enhanced thalamocortical spindle activity during non-REM sleep. These data suggest that developmental cortical malformations do not necessarily correlate with epileptiform activity. We propose that the intraventricular in utero MAM approach exhibiting a range of rhythmopathies is a suitable model for multiple-hit studies of associated neurological disorders. PMID:24782720

  17. Dampened hippocampal oscillations and enhanced spindle activity in an asymptomatic model of developmental cortical malformations

    Directory of Open Access Journals (Sweden)

    Elena eCid

    2014-04-01

    Full Text Available Developmental cortical malformations comprise a large spectrum of histopathological brain abnormalities and syndromes. Their genetic, developmental and clinical complexity suggests they should be better understood in terms of the complementary action of independently timed perturbations (i.e. the multiple-hit hypothesis. However, understanding the underlying biological processes remains puzzling. Here we induced developmental cortical malformations in offspring, after intraventricular injection of methylazoxymethanol (MAM in utero in mice. We combined extensive histological and electrophysiological studies to characterize the model. We found that MAM injections at E14 and E15 induced a range of cortical and hippocampal malformations resembling histological alterations of specific genetic mutations and transplacental mitotoxic agent injections. However, in contrast to most of these models, intraventricularly MAM-injected mice remained asymptomatic and showed no clear epilepsy-related phenotype as tested in long-term chronic recordings and with pharmacological manipulations. Instead, they exhibited a non-specific reduction of hippocampal-related brain oscillations (mostly in CA1; including theta, gamma and HFOs; and enhanced thalamocortical spindle activity during non-REM sleep. These data suggest that developmental cortical malformations do not necessarily correlate with epileptiform activity. We propose that the intraventricular in utero MAM approach exhibiting a range of rhythmopathies is a suitable model for multiple-hit studies of associated neurological disorders.

  18. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  19. Nonequilibrium model on Apollonian networks.

    Science.gov (United States)

    Lima, F W S; Moreira, André A; Araújo, Ascânio D

    2012-11-01

    We investigate the majority-vote model with two states (-1,+1) and a noise parameter q on Apollonian networks. The main result found here is the presence of the phase transition as a function of the noise parameter q. Previous results on the Ising model in Apollonian networks have reported no presence of a phase transition. We also studied the effect of redirecting a fraction p of the links of the network. By means of Monte Carlo simulations, we obtained the exponent ratio γ/ν, β/ν, and 1/ν for several values of rewiring probability p. The critical noise q{c} and U were also calculated. Therefore, the results presented here demonstrate that the majority-vote model belongs to a different universality class than equilibrium Ising model on Apollonian network.

  20. Network structure implied by initial axon outgrowth in rodent cortex: empirical measurement and models.

    Science.gov (United States)

    Cahalane, Diarmuid J; Clancy, Barbara; Kingsbury, Marcy A; Graf, Ethan; Sporns, Olaf; Finlay, Barbara L

    2011-01-11

    The developmental mechanisms by which the network organization of the adult cortex is established are incompletely understood. Here we report on empirical data on the development of connections in hamster isocortex and use these data to parameterize a network model of early cortical connectivity. Using anterograde tracers at a series of postnatal ages, we investigate the growth of connections in the early cortical sheet and systematically map initial axon extension from sites in anterior (motor), middle (somatosensory) and posterior (visual) cortex. As a general rule, developing axons extend from all sites to cover relatively large portions of the cortical field that include multiple cortical areas. From all sites, outgrowth is anisotropic, covering a greater distance along the medial/lateral axis than along the anterior/posterior axis. These observations are summarized as 2-dimensional probability distributions of axon terminal sites over the cortical sheet. Our network model consists of nodes, representing parcels of cortex, embedded in 2-dimensional space. Network nodes are connected via directed edges, representing axons, drawn according to the empirically derived anisotropic probability distribution. The networks generated are described by a number of graph theoretic measurements including graph efficiency, node betweenness centrality and average shortest path length. To determine if connectional anisotropy helps reduce the total volume occupied by axons, we define and measure a simple metric for the extra volume required by axons crossing. We investigate the impact of different levels of anisotropy on network structure and volume. The empirically observed level of anisotropy suggests a good trade-off between volume reduction and maintenance of both network efficiency and robustness. Future work will test the model's predictions for connectivity in larger cortices to gain insight into how the regulation of axonal outgrowth may have evolved to achieve efficient

  1. Algebraic Statistics for Network Models

    Science.gov (United States)

    2014-02-19

    AFRL-OSR-VA-TR-2014-0070 (DARPA) Algebraic Statistics for Network Models SONJA PETROVIC PENNSYLVANIA STATE UNIVERSITY 02/19/2014 Final Report...DARPA GRAPHS Phase I Algebraic Statistics for Network Models FA9550-12-1-0392 Sonja Petrović petrovic@psu.edu1 Department of Statistics Pennsylvania...Department of Statistics, Heinz College , Machine Learning Department, Cylab Carnegie Mellon University 1. Abstract This project focused on the family of

  2. A structured model of video reproduces primary visual cortical organisation.

    Directory of Open Access Journals (Sweden)

    Pietro Berkes

    2009-09-01

    Full Text Available The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1. In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.

  3. Investigation of hyperelastic models for nonlinear elastic behavior of demineralized and deproteinized bovine cortical femur bone.

    Science.gov (United States)

    Hosseinzadeh, M; Ghoreishi, M; Narooei, K

    2016-06-01

    In this study, the hyperelastic models of demineralized and deproteinized bovine cortical femur bone were investigated and appropriate models were developed. Using uniaxial compression test data, the strain energy versus stretch was calculated and the appropriate hyperelastic strain energy functions were fitted on data in order to calculate the material parameters. To obtain the mechanical behavior in other loading conditions, the hyperelastic strain energy equations were investigated for pure shear and equi-biaxial tension loadings. The results showed the Mooney-Rivlin and Ogden models cannot predict the mechanical response of demineralized and deproteinized bovine cortical femur bone accurately, while the general exponential-exponential and general exponential-power law models have a good agreement with the experimental results. To investigate the sensitivity of the hyperelastic models, a variation of 10% in material parameters was performed and the results indicated an acceptable stability for the general exponential-exponential and general exponential-power law models. Finally, the uniaxial tension and compression of cortical femur bone were studied using the finite element method in VUMAT user subroutine of ABAQUS software and the computed stress-stretch curves were shown a good agreement with the experimental data.

  4. Advances in theoretical models of network science

    Institute of Scientific and Technical Information of China (English)

    FANG Jin-qing; BI Qiao; LI Yong

    2007-01-01

    In this review article, we will summarize the main advances in network science investigated by the CIAE Group of Complex Network in this field. Several theoretical models of network science were proposed and their topological and dynamical properties are reviewed and compared with the other models. Our models mainly include a harmonious unifying hybrid preferential model, a large unifying hybrid network model, a quantum interference network, a hexagonal nanowire network, and a small-world network with the same degree. The models above reveal some new phenomena and findings, which are useful for deeply understanding and investigating complex networks and their applications.

  5. Drilling in cortical bone: a finite element model and experimental investigations.

    Science.gov (United States)

    Lughmani, Waqas A; Bouazza-Marouf, Kaddour; Ashcroft, Ian

    2015-02-01

    Bone drilling is an essential part of many orthopaedic surgery procedures, including those for internal fixation and for attaching prosthetics. Estimation and control of bone drilling forces are critical to prevent drill-bit breakthrough, excessive heat generation, and mechanical damage to the bone. An experimental and computational study of drilling in cortical bone has been conducted. A 3D finite element (FE) model for prediction of thrust forces experienced during bone drilling has been developed. The model incorporates the dynamic characteristics involved in the process along with geometrical considerations. An elastic-plastic material model is used to predict the behaviour of cortical bone during drilling. The average critical thrust forces and torques obtained using FE analysis are found to be in good agreement with the experimental results.

  6. Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders

    Science.gov (United States)

    Kana, Rajesh K.; Libero, Lauren E.; Moore, Marie S.

    2011-12-01

    such as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD.

  7. Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders.

    Science.gov (United States)

    Kana, Rajesh K; Libero, Lauren E; Moore, Marie S

    2011-12-01

    as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD.

  8. Structure learning for Bayesian networks as models of biological networks.

    Science.gov (United States)

    Larjo, Antti; Shmulevich, Ilya; Lähdesmäki, Harri

    2013-01-01

    Bayesian networks are probabilistic graphical models suitable for modeling several kinds of biological systems. In many cases, the structure of a Bayesian network represents causal molecular mechanisms or statistical associations of the underlying system. Bayesian networks have been applied, for example, for inferring the structure of many biological networks from experimental data. We present some recent progress in learning the structure of static and dynamic Bayesian networks from data.

  9. Ketamine Dysregulates the Amplitude and Connectivity of High-Frequency Oscillations in Cortical-Subcortical Networks in Humans: Evidence From Resting-State Magnetoencephalography-Recordings.

    Science.gov (United States)

    Rivolta, Davide; Heidegger, Tonio; Scheller, Bertram; Sauer, Andreas; Schaum, Michael; Birkner, Katharina; Singer, Wolf; Wibral, Michael; Uhlhaas, Peter J

    2015-09-01

    Hypofunctioning of the N-methyl-D-aspartate receptor (NMDA-R) has been prominently implicated in the pathophysiology of schizophrenia (ScZ). The current study tested the effects of ketamine, a dissociative anesthetic and NMDA-R antagonist, on resting-state activity recorded with magnetoencephalography (MEG) in healthy volunteers. In a single-blind cross-over design, each participant (n = 12) received, on 2 different sessions, a subanesthetic dose of S-ketamine (0.006 mg/Kg) and saline injection. MEG-data were analyzed at sensor- and source-level in the beta (13-30 Hz) and gamma (30-90 Hz) frequency ranges. In addition, connectivity analysis at source-level was performed using transfer entropy (TE). Ketamine increased gamma-power while beta-band activity was decreased. Specifically, elevated 30-90 Hz activity was pronounced in subcortical (thalamus and hippocampus) and cortical (frontal and temporal cortex) regions, whilst reductions in beta-band power were localized to the precuneus, cerebellum, anterior cingulate, temporal and visual cortex. TE analysis demonstrated increased information transfer in a thalamo-cortical network after ketamine administration. The findings are consistent with the pronounced dysregulation of high-frequency oscillations following the inhibition of NMDA-R in animal models of ScZ as well as with evidence from electroencephalogram-data in ScZ-patients and increased functional connectivity during early illness stages. Moreover, our data highlight the potential contribution of thalamo-cortical connectivity patterns towards ketamine-induced neuronal dysregulation, which may be relevant for the understanding of ScZ as a disorder of disinhibition of neural circuits.

  10. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  11. Network model of security system

    Directory of Open Access Journals (Sweden)

    Adamczyk Piotr

    2016-01-01

    Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.

  12. A new transient network model for associative polymer networks

    NARCIS (Netherlands)

    Wientjes, R.H.W.; Jongschaap, R.J.J.; Duits, M.H.G.; Mellema, J.

    1999-01-01

    A new model for the linear viscoelastic behavior of polymer networks is developed. In this model the polymer system is described as a network of spring segments connected via sticky points (as in the Lodge model). [Lodge, A. S., “A network theory of flow birefringence and stress in concentrated poly

  13. Nonlinear hierarchical multiscale modeling of cortical bone considering its nanoscale microstructure.

    Science.gov (United States)

    Ghanbari, J; Naghdabadi, R

    2009-07-22

    We have used a hierarchical multiscale modeling scheme for the analysis of cortical bone considering it as a nanocomposite. This scheme consists of definition of two boundary value problems, one for macroscale, and another for microscale. The coupling between these scales is done by using the homogenization technique. At every material point in which the constitutive model is needed, a microscale boundary value problem is defined using a macroscopic kinematical quantity and solved. Using the described scheme, we have studied elastic properties of cortical bone considering its nanoscale microstructural constituents with various mineral volume fractions. Since the microstructure of bone consists of mineral platelet with nanometer size embedded in a protein matrix, it is similar to the microstructure of soft matrix nanocomposites reinforced with hard nanostructures. Considering a representative volume element (RVE) of the microstructure of bone as the microscale problem in our hierarchical multiscale modeling scheme, the global behavior of bone is obtained under various macroscopic loading conditions. This scheme may be suitable for modeling arbitrary bone geometries subjected to a variety of loading conditions. Using the presented method, mechanical properties of cortical bone including elastic moduli and Poisson's ratios in two major directions and shear modulus is obtained for different mineral volume fractions.

  14. Increased persistent sodium current determines cortical hyperexcitability in a genetic model of amyotrophic lateral sclerosis.

    Science.gov (United States)

    Pieri, Massimo; Carunchio, Irene; Curcio, Livia; Mercuri, Nicola Biagio; Zona, Cristina

    2009-02-01

    Cortical hyperexcitability has been observed in Amyotrophic Lateral Sclerosis (ALS) patients. Familial ALS accounts for 10% of all cases and mutations of the Cu,Zn superoxide dismutase (SOD1) gene have been identified in about 20% of the familial cases. The aim of this study was to investigate whether in a mouse model of ALS the cortical neurons developed hyperexcitability due to intrinsic properties of the single cell. We first examined the passive membrane properties and the pattern of repetitive firing in cultured cortical neurons from Control mice and transgenic mice expressing high levels of the human mutated protein (Gly(93)-->Ala, G93A). The former did not display significantly differing values between Control and G93A cortical neurons. However, the threshold potential and time of the first action potential decreased significantly and the firing frequency increased significantly in the G93A compared to Control neurons. The analysis of the voltage-dependent sodium currents revealed that the fast transient sodium current was unaffected by the SOD1 mutation whereas the persistent sodium current was significantly higher in the mutated neurons. Finally, Riluzole, a selective blocker of the persistent sodium current at low concentrations, decreased the firing frequency in G93A neurons, strongly indicating an involvement of this current in the observed hyperexcitability. These are the first data that demonstrate an intrinsic hyperexcitability in the G93A cortical neurons due to a higher current density of the persistent sodium current in the mutated neurons and open up new prospects of understanding ALS disease etiopathology.

  15. Analysis of Amygdalar-Cortical Network Covariance During Pre- versus Post-menopausal Estrogen Levels: Potential Relevance to Resting State Networks, Mood, and Cognition

    Science.gov (United States)

    Ottowitz, William E.; Derro, David; Dougherty, Darin D.; Lindquist, Martin A.; Fischman, Alan J.; Hall, Janet E.

    2014-01-01

    Objectives 1.) Expand the scope of neuroendocrine applications of functional neuroimaging techniques. 2.) Compare the covariance of amygdalar activity with that of the rest of the brain during pre- and post-menopausal levels of estrogen (E2). Based on the distribution of cortical E2 receptors and the neocortical regions where E2 has been shown to preferentially accumulate, we predict that E2 infusion will increase covariance of amygdalar activity with that of the temporal and frontal cortices. Design This basic physiology study employed a within-subject design. All participants were post-menopausal women (n =7). Analysis of covariance between whole brain and amygdalar regional cerebral glucose consumption (CMRglc) was conducted in a voxel-wise manner by means of the basic regression option in SPM2 and was applied to FDG-PET scans acquired at baseline and after a 24 hour graded E2 infusion. Setting an academic medical center; Massachusetts General Hospital, Boston, Massachusetts. Results E2 levels (mean ± sem) were significantly greater at 24 hours (257.9 pg/mL ± 29.7) than at 0 hours (28.1 pg/mL ± 3.4). Right amygdalar CMRglc showed a significant covariance with activity of three different regions of the temporal cortex during E2 infusion, but none at baseline. In addition, right amygdalar CMRglc covaried with that of the right medial and superior frontal gyri only during E2 infusion. Conclusions In addition to suggesting changes in amygdalar-cortical network connectivity as a result of short-term E2 exposure, these analyses provide evidence that basic neuroendocrine research may benefit from further use of FDG-PET and other functional neuroimaging modalities for network level analyses. PMID:18766152

  16. Assessment and Treatment of Peritumoral Cortical Veins in Parasagittal Meningiomas with Application of 3-Dimensional Imaging Fusion Model.

    Science.gov (United States)

    Yin, Tengkun; Gu, Jianjun; Huang, Yinxing; Wei, Liangfeng; Gao, Jinxi; Wang, Shousen

    2017-08-01

    Operation of cortical veins is the keystone of parasagittal meningioma (PSM) resection. Little is known about pathologic changes of the veins and proper treatment. We built 3-dimensional (3D) image fusion models by neuronavigation to analyze the features of peritumoral cortical veins for PSMs and explore intraoperative treatment options. We performed a prospective study of 42 consecutive surgically treated PSM patients who underwent preoperative evaluation of peritumoral cortical veins using a 3D venous-tumor fusion model established by a neuronavigation system. We categorized cortical veins into 3 types: single-end anastomosis (type a), tumor-to-end anastomosis (type b), and end-to-end anastomosis (type c). We present surgical strategies to operate these veins. Preoperative evaluation demonstrated 39 patients with peritumoral cortical veins. The 3D models show 100% of the veins (95 in total), which were confirmed intraoperation. The postoperative complication rates after vein injury were 60% (type a), 16.7% (type c), and 0% (type b). Ten patients (23.8%) had residual tumor because of venous protection (equal to Simpson grade III). After correlation analysis, type b and c cortical veins were positively correlated with tumor volume. The anastomoses of cortical veins may provide compensation for venous transaction. There may be a time-evolution relationship between different cortical veins (type a to c to b). Treatment of cortical veins should follow the following principles: single-end veins must be protected, tumor-to-end veins should be transacted directly, and end-to-end veins could be cut selectivity based on the degree of occlusion of the superior sagittal sinus. Detailed preoperative assessment of peritumoral cortical veins is critical for proper treatment. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory.

    Directory of Open Access Journals (Sweden)

    Shashaank Vattikuti

    2016-05-01

    Full Text Available It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.

  18. The functional roles of alpha-band phase synchronization in local and large-scale cortical networks

    Directory of Open Access Journals (Sweden)

    Satu ePalva

    2011-09-01

    Full Text Available Alpha-frequency band (8-14 Hz oscillations are among the most salient phenomena in human electroencephalography (EEG recordings and yet their functional roles have remained unclear. Much of research on alpha oscillations in human EEG has focused on peri-stimulus amplitude dynamics, which phenomenologically support an idea of alpha oscillations being negatively correlated with local cortical excitability and having a role in the suppression of task-irrelevant neuronal processing. This kind of an inhibitory role for alpha oscillations is also supported by several functional magnetic resonance imaging (fMRI and trans-cranial magnetic stimulation (TMS studies. Nevertheless, investigations of local and inter-areal alpha phase dynamics suggest that the alpha-frequency band rhythmicity may play a role also in active task-relevant neuronal processing. These data imply that inter-areal alpha phase synchronization could support attentional, executive, and contextual functions. In this review, we outline evidence supporting different views on the roles of alpha oscillations in cortical networks and unresolved issues that should be addressed to resolve or reconcile these apparently contrasting hypotheses.

  19. Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model.

    Directory of Open Access Journals (Sweden)

    Robert Becker

    2015-09-01

    Full Text Available Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG - functional magnetic resonance imaging (fMRI studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals.

  20. Effect of Anatomically Realistic Full-Head Model on Activation of Cortical Neurons in Subdural Cortical Stimulation—A Computational Study

    Science.gov (United States)

    Seo, Hyeon; Kim, Donghyeon; Jun, Sung Chan

    2016-06-01

    Electrical brain stimulation (EBS) is an emerging therapy for the treatment of neurological disorders, and computational modeling studies of EBS have been used to determine the optimal parameters for highly cost-effective electrotherapy. Recent notable growth in computing capability has enabled researchers to consider an anatomically realistic head model that represents the full head and complex geometry of the brain rather than the previous simplified partial head model (extruded slab) that represents only the precentral gyrus. In this work, subdural cortical stimulation (SuCS) was found to offer a better understanding of the differential activation of cortical neurons in the anatomically realistic full-head model than in the simplified partial-head models. We observed that layer 3 pyramidal neurons had comparable stimulation thresholds in both head models, while layer 5 pyramidal neurons showed a notable discrepancy between the models; in particular, layer 5 pyramidal neurons demonstrated asymmetry in the thresholds and action potential initiation sites in the anatomically realistic full-head model. Overall, the anatomically realistic full-head model may offer a better understanding of layer 5 pyramidal neuronal responses. Accordingly, the effects of using the realistic full-head model in SuCS are compelling in computational modeling studies, even though this modeling requires substantially more effort.

  1. Target-Centric Network Modeling

    DEFF Research Database (Denmark)

    Mitchell, Dr. William L.; Clark, Dr. Robert M.

    In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence repor....... Working through these cases, students will learn to manage and evaluate realistic intelligence accounts.......In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

  2. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

    DEFF Research Database (Denmark)

    Hagen, Espen; Dahmen, David; Stavrinou, Maria L

    2016-01-01

    and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...... on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network...... model for a ∼1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its...

  3. Enriched encoding: reward motivation organizes cortical networks for hippocampal detection of unexpected events.

    Science.gov (United States)

    Murty, Vishnu P; Adcock, R Alison

    2014-08-01

    Learning how to obtain rewards requires learning about their contexts and likely causes. How do long-term memory mechanisms balance the need to represent potential determinants of reward outcomes with the computational burden of an over-inclusive memory? One solution would be to enhance memory for salient events that occur during reward anticipation, because all such events are potential determinants of reward. We tested whether reward motivation enhances encoding of salient events like expectancy violations. During functional magnetic resonance imaging, participants performed a reaction-time task in which goal-irrelevant expectancy violations were encountered during states of high- or low-reward motivation. Motivation amplified hippocampal activation to and declarative memory for expectancy violations. Connectivity of the ventral tegmental area (VTA) with medial prefrontal, ventrolateral prefrontal, and visual cortices preceded and predicted this increase in hippocampal sensitivity. These findings elucidate a novel mechanism whereby reward motivation can enhance hippocampus-dependent memory: anticipatory VTA-cortical-hippocampal interactions. Further, the findings integrate literatures on dopaminergic neuromodulation of prefrontal function and hippocampus-dependent memory. We conclude that during reward motivation, VTA modulation induces distributed neural changes that amplify hippocampal signals and records of expectancy violations to improve predictions-a potentially unique contribution of the hippocampus to reward learning.

  4. Disrupted Cortical Connectivity as an Explanatory Model for Autism Spectrum Disorder

    Directory of Open Access Journals (Sweden)

    Jenniefer Drude Borup

    2014-02-01

    Full Text Available The aim of this article is to explain the theory of Disrupted Cortical Connectivity and discuss whether or not it can integrate the following three theories: Theory of Mind, Executive Functioning, and Weak Central Coherence that dominate the field of autism spectrum disorder research. Due to a lack of existing literature discussing this potential integration, we have consequentially undertaken such an endeavour. In our opinion, integration appears to be possible since this explanatory model can account for difficulties in both social cognition and executive functioning commonly found in autism spectrum disorder. Moreover, the theory of Disrupted Cortical Connectivity could be described as an extension of the theory of Weak Central Coherence.

  5. Prediction of the main cortical areas and connections involved in the tactile function of the visual cortex by network analysis.

    Science.gov (United States)

    Négyessy, László; Nepusz, Tamás; Kocsis, László; Bazsó, Fülöp

    2006-04-01

    We explored the cortical pathways from the primary somatosensory cortex to the primary visual cortex (V1) by analysing connectional data in the macaque monkey using graph-theoretical tools. Cluster analysis revealed the close relationship of the dorsal visual stream and the sensorimotor cortex. It was shown that prefrontal area 46 and parietal areas VIP and 7a occupy a central position between the different clusters in the visuo-tactile network. Among these structures all the shortest paths from primary somatosensory cortex (3a, 1 and 2) to V1 pass through VIP and then reach V1 via MT, V3 and PO. Comparison of the input and output fields suggested a larger specificity for the 3a/1-VIP-MT/V3-V1 pathways among the alternative routes. A reinforcement learning algorithm was used to evaluate the importance of the aforementioned pathways. The results suggest a higher role for V3 in relaying more direct sensorimotor information to V1. Analysing cliques, which identify areas with the strongest coupling in the network, supported the role of VIP, MT and V3 in visuo-tactile integration. These findings indicate that areas 3a, 1, VIP, MT and V3 play a major role in shaping the tactile information reaching V1 in both sighted and blind subjects. Our observations greatly support the findings of the experimental studies and provide a deeper insight into the network architecture underlying visuo-tactile integration in the primate cerebral cortex.

  6. A neural network model of attention-modulated neurodynamics.

    Science.gov (United States)

    Gu, Yuqiao; Liljenström, Hans

    2007-12-01

    Visual attention appears to modulate cortical neurodynamics and synchronization through various cholinergic mechanisms. In order to study these mechanisms, we have developed a neural network model of visual cortex area V4, based on psychophysical, anatomical and physiological data. With this model, we want to link selective visual information processing to neural circuits within V4, bottom-up sensory input pathways, top-down attention input pathways, and to cholinergic modulation from the prefrontal lobe. We investigate cellular and network mechanisms underlying some recent analytical results from visual attention experimental data. Our model can reproduce the experimental findings that attention to a stimulus causes increased gamma-frequency synchronization in the superficial layers. Computer simulations and STA power analysis also demonstrate different effects of the different cholinergic attention modulation action mechanisms.

  7. Control and amplification of cortical neurodynamics

    Science.gov (United States)

    Liljenstroem, Hans; Aronsson, P.

    1999-03-01

    We investigate different mechanisms for the control and amplification of cortical neurodynamics, using a neural network model of a three layered cortical structure. We show that different dynamical states can be obtained by changing a control parameter of the input-output relation, or by changing the noise level. Point attractor, limit cycle, and strange attractor dynamics occur at different values of the control parameter. For certain, optimal noise levels, system performance is maximized, analogous to stochastic resonance phenomena. Noise can also be used to induce different dynamical states. A few noisy network units distributed in a network layer can result in global synchronous oscillations, or waves of activity moving across the network. We further demonstrate that fast synchronization of network activity can be obtained by implementing electromagnetic interactions between network units.

  8. CNEM: Cluster Based Network Evolution Model

    Directory of Open Access Journals (Sweden)

    Sarwat Nizamani

    2015-01-01

    Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks

  9. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  10. Control of chaos in delay differential equations, in a network of oscillators and in model cortex

    Science.gov (United States)

    Babloyantz, A.; Lourenço, C.; Sepulchre, J. A.

    We extend the Ott-Grebogi-Yorke method to the stabilization of unstable orbits in a network of oscillators exhibiting spatiotemporal chaotic activity, wherein the perturbation is applied to the variables of the system. With the help of numerical simulations we show that a method developed by Pyragas can stabilize unstable orbits in a one variable delay differential equation and in a model cortical network with delay. We discuss the relevance of these results in the physiological processes of the brain.

  11. Abnormal dopaminergic modulation of striato-cortical networks underlies levodopa-induced dyskinesias in humans

    DEFF Research Database (Denmark)

    Herz, Damian M.; Haagensen, Brian N.; Christensen, Mark S.

    2015-01-01

    Dopaminergic signalling in the striatum contributes to reinforcement of actions and motivational enhancement of motor vigour. Parkinson's disease leads to progressive dopaminergic denervation of the striatum, impairing the function of cortico-basal ganglia networks. While levodopa therapy...

  12. Low and High-Frequency Field Potentials of Cortical Networks Exhibit Distinct Responses to Chemicals

    Science.gov (United States)

    Neural networks grown on microelectrode arrays (MEAs) have become an important, high content in vitro assay for assessing neuronal function. MEA experiments typically examine high- frequency (HF) (>200 Hz) spikes, and bursts which can be used to discriminate between differ...

  13. Cortical sources of ERP in prosaccade and antisaccade eye movements using realistic source models

    Science.gov (United States)

    Richards, John E.

    2013-01-01

    The cortical sources of event-related-potentials (ERP) using realistic source models were examined in a prosaccade and antisaccade procedure. College-age participants were presented with a preparatory interval and a target that indicated the direction of the eye movement that was to be made. In some blocks a cue was given in the peripheral location where the target was to be presented and in other blocks no cue was given. In Experiment 1 the prosaccade and antisaccade trials were presented randomly within a block; in Experiment 2 procedures were compared in which either prosaccade and antisaccade trials were mixed in the same block, or trials were presented in separate blocks with only one type of eye movement. There was a central negative slow wave occurring prior to the target, a slow positive wave over the parietal scalp prior to the saccade, and a parietal spike potential immediately prior to saccade onset. Cortical source analysis of these ERP components showed a common set of sources in the ventral anterior cingulate and orbital frontal gyrus for the presaccadic positive slow wave and the spike potential. In Experiment 2 the same cued- and non-cued blocks were used, but prosaccade and antisaccade trials were presented in separate blocks. This resulted in a smaller difference in reaction time between prosaccade and antisaccade trials. Unlike the first experiment, the central negative slow wave was larger on antisaccade than on prosaccade trials, and this effect on the ERP component had its cortical source primarily in the parietal and mid-central cortical areas contralateral to the direction of the eye movement. These results suggest that blocked prosaccade and antisaccade trials results in preparatory or set effects that decreases reaction time, eliminates some cueing effects, and is based on contralateral parietal-central brain areas. PMID:23847476

  14. Cortical Sources of ERP in Prosaccade and Antisaccade Eye Movements using Realistic Source Models

    Directory of Open Access Journals (Sweden)

    John E Richards

    2013-07-01

    Full Text Available The cortical sources of event-related-potentials (ERP using realistic source models were examined in a prosaccade and antisaccade task. College-age participants were presented with a preparatory interval and a target that indicated the direction of the eye movement that was to be made. In some blocks a cue was given in the peripheral location where the target was to be presented and in other blocks no cue was given. In Experiment 1 the prosaccade and antisaccade trials were presented randomly within a block; in Experiment 2 procedures were compared in which either prosaccade and antisaccade trials were mixed in the same block, or trials were presented in separate blocks with only one type of eye movement. There was a central negative slow wave occurring prior to the target, a slow positive wave over the parietal scalp prior to the saccade, and a parietal spike potential immediately prior to saccade onset. Cortical source analysis of these ERP components showed a common set of sources in the ventral anterior cingulate and orbital frontal gyrus for the presaccadic positive slow wave and the spike potential. In Experiment 2 the same cued- and non-cued blocks were used, but prosaccade and antisaccade trials were presented in separate blocks. This resulted in a smaller difference in reaction time between prosaccade and antisaccade trials. Unlike the first experiment, the central negative slow wave was larger on antisaccade than on prosaccade trials, and this effect on the ERP component had its cortical source primarily in the parietal and mid-central cortical areas contralateral to the direction of the eye movement. These results suggest that blocked prosaccade and antisaccade trials results in preparatory or set effects that decreases reaction time, eliminates some cueing effects, and is based on contralateral parietal-central brain areas.

  15. Inactivation of the anterior cingulate reveals enhanced reliance on cortical networks for remote spatial memory retrieval after sequential memory processing.

    Directory of Open Access Journals (Sweden)

    Brianne C Wartman

    Full Text Available One system consolidation model suggests that as time passes, ensembles of cortical neurons form strong connections to represent remote memories. In this model, the anterior cingulate cortex (ACC serves as a cortical region that represents remote memories. However, there is debate as to whether remote spatial memories go through this systems consolidation process and come to rely on the ACC. The present experiment examined whether increasing the processing demand on the hippocampus, by sequential training on two spatial tasks, would more fully engage the ACC during retrieval of a remote spatial memory. In this scenario, inactivation of the ACC at a remote time point was hypothesized to produce a severe memory deficit if rats had been trained on two, sequential spatial tasks. Rats were trained on a water maze (WM task only or a WM task followed by a radial arm maze task. A WM probe test was given recently or remotely to all rats. Prior to the probe test, rats received an injection of saline or muscimol into the ACC. A subtle deficit in probe performance was found at the remote time point in the group trained on only one spatial task and treated with muscimol. In the group trained on two spatial tasks and treated with muscimol, a subtle deficit in probe performance was noted at the recent time point and a substantial deficit in probe performance was observed at the remote time point. c-Fos labeling in the hippocampus revealed more labeling in the CA1 region in all remotely tested groups than recently tested groups. Findings suggest that spatial remote memories come to rely more fully on the ACC when hippocampal processing requirements are increased. Results also suggest continued involvement of the hippocampus in spatial memory retrieval along with a progressive strengthening of cortical connections as time progresses.

  16. Inactivation of the anterior cingulate reveals enhanced reliance on cortical networks for remote spatial memory retrieval after sequential memory processing.

    Science.gov (United States)

    Wartman, Brianne C; Gabel, Jennifer; Holahan, Matthew R

    2014-01-01

    One system consolidation model suggests that as time passes, ensembles of cortical neurons form strong connections to represent remote memories. In this model, the anterior cingulate cortex (ACC) serves as a cortical region that represents remote memories. However, there is debate as to whether remote spatial memories go through this systems consolidation process and come to rely on the ACC. The present experiment examined whether increasing the processing demand on the hippocampus, by sequential training on two spatial tasks, would more fully engage the ACC during retrieval of a remote spatial memory. In this scenario, inactivation of the ACC at a remote time point was hypothesized to produce a severe memory deficit if rats had been trained on two, sequential spatial tasks. Rats were trained on a water maze (WM) task only or a WM task followed by a radial arm maze task. A WM probe test was given recently or remotely to all rats. Prior to the probe test, rats received an injection of saline or muscimol into the ACC. A subtle deficit in probe performance was found at the remote time point in the group trained on only one spatial task and treated with muscimol. In the group trained on two spatial tasks and treated with muscimol, a subtle deficit in probe performance was noted at the recent time point and a substantial deficit in probe performance was observed at the remote time point. c-Fos labeling in the hippocampus revealed more labeling in the CA1 region in all remotely tested groups than recently tested groups. Findings suggest that spatial remote memories come to rely more fully on the ACC when hippocampal processing requirements are increased. Results also suggest continued involvement of the hippocampus in spatial memory retrieval along with a progressive strengthening of cortical connections as time progresses.

  17. Ising model for distribution networks

    CERN Document Server

    Hooyberghs, H; Giuraniuc, C; Van Schaeybroeck, B; Indekeu, J O

    2012-01-01

    An elementary Ising spin model is proposed for demonstrating cascading failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in realistic networks for distribution and delivery by suppliers to consumers. A ferromagnetic Hamiltonian with quenched random fields results from policies that maximize the gap between demand and delivery. Such policies can arise in a competitive market where firms artificially create new demand, or in a solidary environment where too high a demand cannot reasonably be met. Network failure in the context of a policy of solidarity is possible when an initially active state becomes metastable and decays to a stable inactive state. We explore the characteristics of the demand and delivery, as well as the topological properties, which make the distribution network susceptible of failure. An effective temperature is defined, which governs the strength of the activity fluctuations which can induce a collapse. Numerical results, obtained by Monte Carlo simulations of t...

  18. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....

  19. Inherited cortical HCN1 channel loss amplifies dendritic calcium electrogenesis and burst firing in a rat absence epilepsy model.

    Science.gov (United States)

    Kole, Maarten H P; Bräuer, Anja U; Stuart, Greg J

    2007-01-15

    While idiopathic generalized epilepsies are thought to evolve from temporal highly synchronized oscillations between thalamic and cortical networks, their cellular basis remains poorly understood. Here we show in a genetic rat model of absence epilepsy (WAG/Rij) that a rapid decline in expression of hyperpolarization-activated cyclic-nucleotide gated (HCN1) channels (I(h)) precedes the onset of seizures, suggesting that the loss of HCN1 channel expression is inherited rather than acquired. Loss of HCN1 occurs primarily in the apical dendrites of layer 5 pyramidal neurons in the cortex, leading to a spatially uniform 2-fold reduction in dendritic HCN current throughout the entire somato-dendritic axis. Dual whole-cell recordings from the soma and apical dendrites demonstrate that loss of HCN1 increases somato-dendritic coupling and significantly reduces the frequency threshold for generation of dendritic Ca2+ spikes by backpropagating action potentials. As a result of increased dendritic Ca2+ electrogenesis a large population of WAG/Rij layer 5 neurons showed intrinsic high-frequency burst firing. Using morphologically realistic models of layer 5 pyramidal neurons from control Wistar and WAG/Rij animals we show that the experimentally observed loss of dendritic I(h) recruits dendritic Ca2+ channels to amplify action potential-triggered dendritic Ca2+ spikes and increase burst firing. Thus, loss of function of dendritic HCN1 channels in layer 5 pyramidal neurons provides a somato-dendritic mechanism for increasing the synchronization of cortical output, and is therefore likely to play an important role in the generation of absence seizures.

  20. Predicting cortical ROIs via joint modeling of anatomical and connectional profiles.

    Science.gov (United States)

    Zhang, Tuo; Zhu, Dajiang; Jiang, Xi; Ge, Bao; Hu, Xintao; Han, Junwei; Guo, Lei; Liu, Tianming

    2013-08-01

    Localization of cortical regions of interests (ROIs) in structural neuroimaging data such as diffusion tensor imaging (DTI) and T1-weighted MRI images has significant importance in basic and clinical neurosciences. However, this problem is considerably challenging due to the lack of quantitative mapping between brain structure and function, which relies on the availability of multimodal training data including benchmark task-based functional MRI (fMRI) images and effective machine learning algorithms. This paper presents a novel joint modeling approach that learns predictive models of ROIs from concurrent task-based fMRI, DTI, and T1-weighted MRI datasets. In particular, the effective generalized multiple kernel learning (GMKL) algorithm and ROI coordinate principal component analysis (PCA) model are employed to infer the intrinsic relationships between anatomical T1-weighted MRI/connectional DTI features and task-based fMRI-derived functional ROIs. Then, these predictive models of cortical ROIs are evaluated by cross-validation studies, independent datasets, and reproducibility studies. Experimental results are promising. We envision that these predictive models can be potentially applied in many scenarios that have only DTI and/or T1-weighted MRI data, but without task-based fMRI data. Copyright © 2013 Elsevier B.V. All rights reserved.

  1. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.

    2013-01-01

    During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

  2. Field-programmable gate array implementation of a probabilistic neural network for motor cortical decoding in rats.

    Science.gov (United States)

    Zhou, Fan; Liu, Jun; Yu, Yi; Tian, Xiang; Liu, Hui; Hao, Yaoyao; Zhang, Shaomin; Chen, Weidong; Dai, Jianhua; Zheng, Xiaoxiang

    2010-01-15

    A practical brain-machine interface (BMI) requires real-time decoding algorithms to be realised in a portable device rather than a personal computer. In this article, a field-programmable gate array (FPGA) implementation of a probabilistic neural network (PNN) is proposed and developed to decode motor cortical ensemble recordings in rats performing a lever-pressing task for water rewards. A chronic 16-channel microelectrode array was implanted into the primary motor cortex of the rat to record neural activity, and the pressure signal of the lever were recorded simultaneously. To decode the pressure value from neural activity, both Matlab-based and FPGA-based mapping algorithms using a PNN were implemented and evaluated. In the FPGA architecture, training data of the network were stored in random access memory (RAM) blocks and multiply-add operations were realised by on-chip DSP48E slices. In the approximation of the activation function, a Taylor series and a look-up table (LUT) are used to achieve an accurate approximation. The results of FPGA implementation are as accurate as the realisation of Matlab, but the running speed is 37.9 times faster. This novel and feasible method indicates that the performance of current FPGAs is competent for portable BMI applications.

  3. Random Boolean network models and the yeast transcriptional network

    Science.gov (United States)

    Kauffman, Stuart; Peterson, Carsten; Samuelsson, Björn; Troein, Carl

    2003-12-01

    The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.

  4. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  5. Generalization performance of regularized neural network models

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai

    1994-01-01

    Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...

  6. A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

    Directory of Open Access Journals (Sweden)

    Xiaojin Li

    2013-01-01

    Full Text Available Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY and scale-free gene duplication model (SF-GD, that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

  7. Enriched Encoding: Reward Motivation Organizes Cortical Networks for Hippocampal Detection of Unexpected Events

    Science.gov (United States)

    Murty, Vishnu P.; Adcock, R. Alison

    2014-01-01

    Learning how to obtain rewards requires learning about their contexts and likely causes. How do long-term memory mechanisms balance the need to represent potential determinants of reward outcomes with the computational burden of an over-inclusive memory? One solution would be to enhance memory for salient events that occur during reward anticipation, because all such events are potential determinants of reward. We tested whether reward motivation enhances encoding of salient events like expectancy violations. During functional magnetic resonance imaging, participants performed a reaction-time task in which goal-irrelevant expectancy violations were encountered during states of high- or low-reward motivation. Motivation amplified hippocampal activation to and declarative memory for expectancy violations. Connectivity of the ventral tegmental area (VTA) with medial prefrontal, ventrolateral prefrontal, and visual cortices preceded and predicted this increase in hippocampal sensitivity. These findings elucidate a novel mechanism whereby reward motivation can enhance hippocampus-dependent memory: anticipatory VTA-cortical–hippocampal interactions. Further, the findings integrate literatures on dopaminergic neuromodulation of prefrontal function and hippocampus-dependent memory. We conclude that during reward motivation, VTA modulation induces distributed neural changes that amplify hippocampal signals and records of expectancy violations to improve predictions—a potentially unique contribution of the hippocampus to reward learning. PMID:23529005

  8. Contrasting activity profile of two distributed cortical networks as a function of attentional demands.

    Science.gov (United States)

    Popa, Daniela; Popescu, Andrei T; Paré, Denis

    2009-01-28

    Recent human functional MRI (fMRI) studies have revealed that two widely distributed groups of cortical areas display inverse changes in activity when attentional demands increase, with one group showing higher (task-on) and the second lower (task-off) blood oxygen level-dependent (BOLD) signals. Moreover, task-on and task-off regions also exhibit slow (cat homologues of task-on and task-off regions. In all states of vigilance, LFP power was lower in task-off than task-on regions with no difference in firing rates. Both sets of regions displayed slow (0.5-0.15 Hz) cyclical modulations in LFP power in all frequency bands but with large and variable phase differences such that task-on and task-off regions were often anticorrelated. Inversely correlated LFP power fluctuations were state-dependent in that they were much more frequent in waking and paradoxical sleep than in slow-wave sleep. Moreover, consistent with fMRI findings, when attentional demands increased, LFP power in task-on and task-off regions changed in opposite directions, further augmenting and decreasing, respectively. At odds with previous fMRI studies, however, the decreased LFP power in task-off regions was associated with increased firing rates, suggesting that the engagement of task-off regions might not be reduced but in fact enhanced during attention.

  9. Scalable Capacity Bounding Models for Wireless Networks

    OpenAIRE

    Du, Jinfeng; Medard, Muriel; Xiao, Ming; Skoglund, Mikael

    2014-01-01

    The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper (resp. lower) bounding models, which can be used to calculate the outer (resp. inner) bounds for the capacity region of the original noisy network. Based on the network equivalence framework, this paper presents scalable upper and lower bounding models for wireless networks with potentially many nodes. A channel decoupling method is proposed...

  10. Repeated Stimulation of Cultured Networks of Rat Cortical Neurons Induces Parallel Memory Traces

    Science.gov (United States)

    le Feber, Joost; Witteveen, Tim; van Veenendaal, Tamar M.; Dijkstra, Jelle

    2015-01-01

    During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and…

  11. Brand Marketing Model on Social Networks

    Directory of Open Access Journals (Sweden)

    Jolita Jezukevičiūtė

    2014-04-01

    Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

  12. Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training

    Science.gov (United States)

    Metzler-Baddeley, Claudia; Caeyenberghs, Karen; Foley, Sonya; Jones, Derek K.

    2016-01-01

    Novel activities and experiences shape the brain's structure and organisation and, hence, our behaviour. However, evidence from structural plasticity studies remains mixed and the neural correlates of learning and practice are still poorly understood. We conducted a robustly designed study into grey matter plasticity following 2 months of working memory training. We generated a priori hypotheses regarding the location of plastic effects across three cognitive control networks (executive, anterior salience and basal ganglia networks), and compared the effects of adaptive training (n = 20) with a well-matched active control group (n = 20) which differed in training complexity and included extensive cognitive assessment before and after the training. Adaptive training relative to control activities resulted in a complex pattern of subtle and localised structural changes: Training was associated with increases in cortical thickness in right-lateralised executive regions, notably the right caudal middle frontal cortex, as well as increases in the volume of the left pallidum. In addition the training group showed reductions of thickness in the right insula, which were correlated with training-induced improvements in backward digit span performance. Unexpectedly, control activities were associated with reductions in thickness in the right pars triangularis. These results suggest that the direction of activity-induced plastic changes depend on the level of training complexity as well as brain location. These observations are consistent with the view that the brain responds dynamically to environmental demands by focusing resources on task relevant networks and eliminating irrelevant processing for the purpose of energy reduction. PMID:26806288

  13. Apoptosis is not an invariable component of in vitro models of cortical cerebral ischaemia

    Institute of Scientific and Technical Information of China (English)

    Paul Alexander JONES; Gillian Ruth MAY; Joyce Ann MCLUCKIE; Akinori IWASHITA; John SHARKEY

    2004-01-01

    Characterising the mechanisms of cell death following focal cerebral ischaemia has been hampered by a lack of an in vitro assay emulating both the apoptotic and necrotic features observed in vivo. The present study systematically characterised oxygen-glucose-deprivation (OGD) in primary rat cortical neurones to establish a reproducible model with components of both cell-death endpoints. OGD induced a time-dependent reduction in cell viability, with 80% cell death occurring 24 h after 3 h exposure to 0% O2 and 0.5 mM glucose. Indicative of a necrotic component to OGDinduced cell death, N-methyl-D-aspartate (NMDA) receptor inhibition with MK-801 attenuated neuronal loss by 60%.The lack of protection by the caspase inhibitors DEVD-CHO and z-VAD-fmk suggested that under these conditions neurones did not die by an apoptotic mechanism. Moderating the severity of the insult by decreasing OGD exposure to 60 min did not reduce the amount of necrosis, but did induce a small degree of apoptosis (a slight reduction in cell death was observed in the presence of 10 μtM DEVD-CHO). In separate experiments purported to enhance the apoptotic component, cells were gradually deprived of O2, exposed to 4% O2 (as opposed to 0%) during the OGD period, or maintained in serum-containing media throughout. While NMDA receptor antagonism significantly reduced cortical cell death under all conditions, a caspase-inhibitor sensitive component of cell death was not uncovered. These studies suggest that OGD of cultured cortical cells models the excitotoxic, but not the apoptotic component of cell death observed in vivo.

  14. Segmentation of nanotomographic cortical bone images for quantitative characterization of the osteoctyte lacuno-canalicular network

    Energy Technology Data Exchange (ETDEWEB)

    Ciani, A.; Kewish, C. M. [Synchrotron Soleil, L’Orme des Merisiers, 91192 Saint-Aubin (France); Guizar-Sicairos, M.; Diaz, A.; Holler, M. [Paul Scherrer Institut, 5232 Villigen PSI (Switzerland); Pallu, S.; Achiou, Z.; Jennane, R.; Toumi, H.; Lespessailles, E. [Univ Orléans, I3MTO, Ea 4708, 45000 Orléans (France)

    2016-01-28

    A newly developed data processing method able to characterize the osteocytes lacuno-canalicular network (LCN) is presented. Osteocytes are the most abundant cells in the bone, living in spaces called lacunae embedded inside the bone matrix and connected to each other with an extensive network of canals that allows for the exchange of nutrients and for mechanotransduction functions. The geometrical three-dimensional (3D) architecture is increasingly thought to be related to the macroscopic strength or failure of the bone and it is becoming the focus for investigating widely spread diseases such as osteoporosis. To obtain 3D LCN images non-destructively has been out of reach until recently, since tens-of-nanometers scale resolution is required. Ptychographic tomography was validated for bone imaging in [1], showing clearly the LCN. The method presented here was applied to 3D ptychographic tomographic images in order to extract morphological and geometrical parameters of the lacuno-canalicular structures.

  15. Modulation of cortical-subcortical networks in Parkinson’s disease by applied field effects

    OpenAIRE

    Christopher William Hess

    2013-01-01

    Studies suggest that endogenous field effects may play a role in neuronal oscillations and communication. Non-invasive transcranial electrical stimulation with low-intensity currents can also have direct effects on the underlying cortex as well as distant network effects. While Parkinson’s disease (PD) is amenable to invasive neuromodulation in the basal ganglia by deep brain stimulation (DBS), techniques of non-invasive neuromodulation like transcranial direct current stimulation (tDCS) and ...

  16. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  17. A chronometric functional sub-network in the thalamo-cortical system regulates the flow of neural information necessary for conscious cognitive processes.

    Science.gov (United States)

    León-Domínguez, Umberto; Vela-Bueno, Antonio; Froufé-Torres, Manuel; León-Carrión, Jose

    2013-06-01

    The thalamo-cortical system has been defined as a neural network associated with consciousness. While there seems to be wide agreement that the thalamo-cortical system directly intervenes in vigilance and arousal, a divergence of opinion persists regarding its intervention in the control of other cognitive processes necessary for consciousness. In the present manuscript, we provide a review of recent scientific findings on the thalamo-cortical system and its role in the control and regulation of the flow of neural information necessary for conscious cognitive processes. We suggest that the axis formed by the medial prefrontal cortex and different thalamic nuclei (reticular nucleus, intralaminar nucleus, and midline nucleus), represents a core component for consciousness. This axis regulates different cerebral structures which allow basic cognitive processes like attention, arousal and memory to emerge. In order to produce a synchronized coherent response, neural communication between cerebral structures must have exact timing (chronometry). Thus, a chronometric functional sub-network within the thalamo-cortical system keeps us in an optimal and continuous functional state, allowing high-order cognitive processes, essential to awareness and qualia, to take place.

  18. Fusion of intraoperative cortical images with preoperative models for neurosurgical planning and guidance

    Science.gov (United States)

    Wang, An; Mirsattari, Seyed M.; Parrent, Andrew G.; Peters, Terry M.

    2009-02-01

    During surgery for epilepsy it is important for the surgeon to correlate the preoperative cortical morphology (from preoperative images) with the intraoperative environment. We extend our visualization method presented earlier, to achieves this goal by fusing a direct (photographic) view of the surgical field with the 3D patient model. To correlate the preoperative plan with the intraoperative surgical scene, an intensity-based perspective 3D-2D registration was employed for camera pose estimation. The 2D photographic image was then texture-mapped onto the 3D preoperative model using the solved camera pose. In the proposed method, we employ direct volume rendering to obtain a perspective view of the brain image using GPU-accelerated ray-casting. This is advantageous compared to the point-based or other feature-based registration since no intermediate processing is required. To validate our registration algorithm, we used a point-based 3D-2D registration, that was validated using ground truth from simulated data, and then the intensity-based 3D-2D registration method was validated using the point-based registration result as the gold standard. The registration error of the intensity-based 3D- 2D method was around 3mm when the initial pose is close to the gold standard. Application of the proposed method for correlating fMRI maps with intraoperative cortical stimulation is shown for surgical planning in an epilepsy patient.

  19. Orchestration of presto and largo synchrony in up-down activity of cortical networks

    Directory of Open Access Journals (Sweden)

    Francesca Gullo

    2010-04-01

    Full Text Available It has been demonstrated using single-cell and multiunit electrophysiology in layer III entorhinal cortex and disinhibited hippocampal CA3 slices that the balancing of the up-down activity is characterized by both GABAA and GABAB mechanisms. Here we report novel results obtained using multi-electrode array (MEA, 60 electrodes simultaneous recordings from reverberating postnatal neocortical networks containing 19.2±1.4% GABAergic neurons, typical of intact tissue. We observed that in each spontaneous active-state the total number of spikes in identified clusters of excitatory and inhibitory neurons is almost equal, thus suggesting a balanced average activity. Interestingly, in the active-state, the early phase is sustained by only 10 % of the total spikes and the firing rate follows a sigmoidal regenerative mode up to peak at 35 ms with the number of excitatory spikes greater than inhibitory, therefore indicating an early unbalance. Concentration-response pharmacology of up- and down-state lifetimes in clusters of excitatory (n=1067 and inhibitory (n=305 cells suggests that, besides the GABAA and GABAB mechanisms, others such as GAT-1-mediated uptake, Ih, INaP and IM ion channel activity, robustly govern both up- and down-activity. Some drugs resulted to affect up- and/or down-states with different IC50s, providing evidence that various mechanisms are involved. These results should reinforce not only the role of synchrony in CNS networks, but also the recognized analogies between the Hodgkin-Huxley action potential and the population bursts as basic mechanisms for originating membrane excitability and CNS network synchronization, respectively.

  20. Disruption of prefrontal cortical-hippocampal balance in a developmental model of schizophrenia: reversal by sulpiride.

    Science.gov (United States)

    Belujon, Pauline; Patton, Mary H; Grace, Anthony A

    2013-04-01

    The nucleus accumbens (NAc) receives converging inputs from the medial prefrontal cortex (mPFC) and the hippocampus which have competitive interactions in the NAc to influence motivational drive. We have previously shown altered synaptic plasticity in the hippocampal-NAc pathway in the methylazoxymethanol acetate (MAM) developmental model of schizophrenia in rodents that is dependent on cortical inputs. Thus, because mPFC-hippocampal balance is known to be partially altered in this model, we investigated potential pathological changes in the hippocampal influence over cortex-driven NAc spike activity. Here we show that the reciprocal interaction between the hippocampus and mPFC is absent in MAM animals but is able to be reinstated with administration of the antipsychotic drug, sulpiride. The lack of interaction between these structures in this model could explain the attentional deficits in schizophrenia patients and shed light onto their inability to focus on a single task.

  1. Cortical and trabecular bone adaptation to incremental load magnitudes using the mouse tibial axial compression loading model.

    Science.gov (United States)

    Weatherholt, Alyssa M; Fuchs, Robyn K; Warden, Stuart J

    2013-01-01

    The mouse tibial axial compression loading model has recently been described to allow simultaneous exploration of cortical and trabecular bone adaptation within the same loaded element. However, the model frequently induces cortical woven bone formation and has produced inconsistent results with regards to trabecular bone adaptation. The aim of this study was to investigate bone adaptation to incremental load magnitudes using the mouse tibial axial compression loading model, with the ultimate goal of revealing a load that simultaneously induced lamellar cortical and trabecular bone adaptation. Adult (16 weeks old) female C57BL/6 mice were randomly divided into three load magnitude groups (5, 7 and 9N), and had their right tibia axially loaded using a continuous 2-Hz haversine waveform for 360 cycles/day, 3 days/week for 4 consecutive weeks. In vivo peripheral quantitative computed tomography was used to longitudinally assess midshaft tibia cortical bone adaptation, while ex vivo micro-computed tomography and histomorphometry were used to assess both midshaft tibia cortical and proximal tibia trabecular bone adaptation. A dose response to loading magnitude was observed within cortical bone, with increasing load magnitude inducing increasing levels of lamellar cortical bone adaptation within the upper two thirds of the tibial diaphysis. Greatest cortical bone adaptation was observed at the midshaft where there was a 42% increase in estimated mechanical properties (polar moment of inertia) in the highest (9N) load group. A dose response to load magnitude was not clearly evident within trabecular bone, with only the highest load (9N) being able to induce measureable adaptation (31% increase in trabecular bone volume fraction at the proximal tibia). The ultimate finding was that a load of 9N (engendering a tensile strain of 1833 με on medial surface of the midshaft tibia) was able to simultaneously induce measurable lamellar cortical and trabecular bone adaptation

  2. Cortical reorganisation of cerebral networks after childhood stroke: impact on outcome.

    Science.gov (United States)

    Kornfeld, Salome; Delgado Rodríguez, Juan Antonio; Everts, Regula; Kaelin-Lang, Alain; Wiest, Roland; Weisstanner, Christian; Mordasini, Pasquale; Steinlin, Maja; Grunt, Sebastian

    2015-06-10

    Recovery after arterial ischaemic stroke is known to largely depend on the plastic properties of the brain. The present study examines changes in the network topography of the developing brain after stroke. Effects of brain damage are best assessed by examining entire networks rather than single sites of structural lesions. Relating these changes to post-stroke neuropsychological variables and motor abilities will improve understanding of functional plasticity after stroke. Inclusion of healthy controls will provide additional insight into children's normal brain development. Resting state functional magnetic resonance imaging is a valid approach to topographically investigate the reorganisation of functional networks after a brain lesion. Transcranial magnetic stimulation provides complementary output information. This study will investigate functional reorganisation after paediatric arterial ischaemic stroke by means of resting state functional magnetic resonance imaging and transcranial magnetic stimulation in a cross-sectional plus longitudinal study design. The general aim of this study is to better understand neuroplasticity of the developing brain after stroke in order to develop more efficacious therapy and to improve the post-stroke functional outcome. The cross-sectional part of the study will investigate the functional cerebral networks of 35 children with chronic arterial ischaemic stroke (time of the lesion >2 years). In the longitudinal part, 15 children with acute arterial ischaemic stroke (shortly after the acute phase of the stroke) will be included and investigations will be performed 3 times within the subsequent 9 months. We will also recruit 50 healthy controls, matched for age and sex. The neuroimaging and neurophysiological data will be correlated with neuropsychological and neurological variables. This study is the first to combine resting state functional magnetic resonance imaging and transcranial magnetic stimulation in a paediatric

  3. Technique for infrared and visible image fusion based on non-subsampled shearlet transform and spiking cortical model

    Science.gov (United States)

    Kong, Weiwei; Wang, Binghe; Lei, Yang

    2015-07-01

    Fusion of infrared and visible images is an active research area in image processing, and a variety of relevant algorithms have been developed. However, the existing techniques commonly cannot gain good fusion performance and acceptable computational complexity simultaneously. This paper proposes a novel image fusion approach that integrates the non-subsampled shearlet transform (NSST) with spiking cortical model (SCM) to overcome the above drawbacks. On the one hand, using NSST to conduct the decompositions and reconstruction not only consists with human vision characteristics, but also effectively decreases the computational complexity compared with the current popular multi-resolution analysis tools such as non-subsampled contourlet transform (NSCT). On the other hand, SCM, which has been considered to be an optimal neuron network model recently, is responsible for the fusion of sub-images from different scales and directions. Experimental results indicate that the proposed method is promising, and it does significantly improve the fusion quality in both aspects of subjective visual performance and objective comparisons compared with other current popular ones.

  4. Age-dependent alterations in the cortical entrainment of subthalamic nucleus neurons in the YAC128 mouse model of Huntington's disease.

    Science.gov (United States)

    Callahan, Joshua W; Abercrombie, Elizabeth D

    2015-06-01

    Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that results in motor, cognitive and psychiatric abnormalities. Dysfunction in neuronal processing between the cortex and the basal ganglia is fundamental to the onset and progression of the HD phenotype. The corticosubthalamic hyperdirect pathway plays a crucial role in motor selection and blockade of neuronal activity in the subthalamic nucleus (STN) results in hyperkinetic movement abnormalities, similar to the motor symptoms associated with HD. The aim of the present study was to examine whether changes in the fidelity of information transmission between the cortex and the STN emerge as a function of phenotypic severity in the YAC128 mouse model of HD. We obtained in vivo extracellular recordings in the STN and concomitant electrocorticogram (ECoG) recordings during discrete brain states that reflected global cortical network synchronization or desynchronization. At early ages in YAC128 mice, both the cortex and the STN exhibited patterns of hyperexcitability. As symptom severity progressed, cortical entrainment of STN activity was disrupted and there was an increase in the proportion of non-oscillating, tonically firing STN neurons that were less phase-locked to cortical activity. Concomitant to the dissipation of STN entrainment, there was a reduction in the evoked response of STN neurons to focal cortical stimulation. The spontaneous discharge of STN neurons in YAC128 mice also decreased with age and symptom severity. These results indicate dysfunction in the flow of information within the corticosubthalamic circuit and demonstrate progressive age-related disconnection of the hyperdirect pathway in a transgenic mouse model of HD.

  5. MODELS FOR NETWORK DYNAMICS - A MARKOVIAN FRAMEWORK

    NARCIS (Netherlands)

    LEENDERS, RTAJ

    1995-01-01

    A question not very often addressed in social network analysis relates to network dynamics and focuses on how networks arise and change. It alludes to the idea that ties do not arise or vanish randomly, but (partly) as a consequence of human behavior and preferences. Statistical models for modeling

  6. Modelling delay propagation within an airport network

    NARCIS (Netherlands)

    Pyrgiotis, N.; Malone, K.M.; Odoni, A.

    2013-01-01

    We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and capture

  7. Modelling delay propagation within an airport network

    NARCIS (Netherlands)

    Pyrgiotis, N.; Malone, K.M.; Odoni, A.

    2013-01-01

    We describe an analytical queuing and network decomposition model developed to study the complex phenomenon of the propagation of delays within a large network of major airports. The Approximate Network Delays (AND) model computes the delays due to local congestion at individual airports and

  8. A system dynamics model for communications networks

    Science.gov (United States)

    Awcock, A. J.; King, T. E. G.

    1985-09-01

    An abstract model of a communications network in system dynamics terminology is developed as implementation of this model by a FORTRAN program package developed at RSRE is discussed. The result of this work is a high-level simulation package in which the performance of adaptive routing algorithms and other network controls may be assessed for a network of arbitrary topology.

  9. Aberrant development and plasticity of excitatory visual cortical networks in the absence of cpg15.

    Science.gov (United States)

    Picard, Nathalie; Leslie, Jennifer H; Trowbridge, Sara K; Subramanian, Jaichandar; Nedivi, Elly; Fagiolini, Michela

    2014-03-05

    During development, experience plays a crucial role in sculpting neuronal connections. Patterned neural activity guides formation of functional neural circuits through the selective stabilization of some synapses and the pruning of others. Activity-regulated factors are fundamental to this process, but their roles in synapse stabilization and maturation is still poorly understood. CPG15, encoded by the activity-regulated gene candidate plasticity gene 15, is a small, glycosylphosphatidylinositol (GPI)-linked, extracellular protein that promotes synapse stabilization. Here we show that global knock-out of cpg15 results in abnormal postnatal development of the excitatory network in visual cortex and an associated disruption in development of visual receptive field properties. In addition, whereas repeated stimulation induced potentiation and depression in wild-type mice, the depression was slower in cpg15 knock-out mice, suggesting impairment in short-term depression-like mechanisms. These findings establish the requirement for cpg15 in activity-dependent development of the visual system and demonstrate the importance of timely excitatory network development for normal visual function.

  10. Effect of canagliflozin and metformin on cortical neurotransmitters in a diabetic rat model.

    Science.gov (United States)

    Arafa, Nadia M S; Marie, Mohamed-Assem S; AlAzimi, Sara Abdullah Mubarak

    2016-10-25

    The rapid economic development in the Arabian Gulf has resulted in lifestyle changes that have increased the prevalence of obesity and type 2 diabetes, with the greatest increases observed in Kuwait. Dyslipidemia and diabetes are risk factors for disruptions in cortical neurotransmitter homeostasis. This study investigated the effect of the antidiabetic medications canagliflozin (CAN) and metformin (MET) on the levels of cortical neurotransmitters in a diabetic rat model. The rats were assigned to the control (C) group, the diabetic group that did not receive treatment (D) or the diabetic group treated with either CAN (10 mg/kg) or MET (100 mg/kg) for 2 or 4 weeks. Blood and urine glucose levels and cortical acetylcholinesterase (AChE) activity were assayed, and amino acid and monoamine levels were measured using HPLC. The diabetic group exhibited a significant increase in AChE activity and a decrease in monoamine and amino acid neurotransmitter levels. In the CAN group, AChE was significantly lower than that in the D and D + MET groups after 2 weeks of treatment. In addition, a significant increase in some cortical monoamines and amino acids was observed in the D + MET and D + CAN groups compared with the D group. Histopathological analysis revealed the presence of severe focal hemorrhage, neuronal degeneration, and cerebral blood vessel congestion, with gliosis in the cerebrum of rats in the D group. The CAN-treated group exhibited severe cerebral blood vessel congestion after 2 weeks of treatment and focal gliosis in the cerebrum after 4 weeks of treatment. Focal gliosis in the cerebrum of rats in the MET-treated group was observed after 2 and 4 weeks of treatment. We conclude that the effect of CAN and MET on neurotransmitters is potentially mediated by their antihyperglycemic and antihyperlipidemic effects. In addition, the effects of CAN on neurotransmitters might be associated with its receptor activity, and the effect of MET on neurotransmitters

  11. Network models in epidemiology: an overview

    Science.gov (United States)

    Lloyd, Alun L.; Valeika, Steve

    In this chapter we shall discuss the development and use of network models in epidemiology. While network models have long been discussed in the theoretical epidemiology literature, they have recently received a large amount of attention amongst the statistical physics community. This has been fueled by the desire to better understand the structure of social and large-scale technological networks, and the increases in computational power that have made the simulation of reasonably-sized network models a feasible proposition. A main aim of this review is to bridge the epidemiologic and statistical physics approaches to network models for infectious diseases, highlighting the important contributions made by both research communities.

  12. Modeling gene regulatory networks: A network simplification algorithm

    Science.gov (United States)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  13. Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

    Directory of Open Access Journals (Sweden)

    Guido Gigante

    2015-11-01

    Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

  14. Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

    Science.gov (United States)

    Gigante, Guido; Deco, Gustavo; Marom, Shimon; Del Giudice, Paolo

    2015-11-01

    Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

  15. Eight challenges for network epidemic models

    Directory of Open Access Journals (Sweden)

    Lorenzo Pellis

    2015-03-01

    Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

  16. An evolutionary model of social networks

    Science.gov (United States)

    Ludwig, M.; Abell, P.

    2007-07-01

    Social networks in communities, markets, and societies self-organise through the interactions of many individuals. In this paper we use a well-known mechanism of social interactions — the balance of sentiment in triadic relations — to describe the development of social networks. Our model contrasts with many existing network models, in that people not only establish but also break up relations whilst the network evolves. The procedure generates several interesting network features such as a variety of degree distributions and degree correlations. The resulting network converges under certain conditions to a steady critical state where temporal disruptions in triangles follow a power-law distribution.

  17. Abnormal dopaminergic modulation of striato-cortical networks underlies levodopa-induced dyskinesias in humans

    DEFF Research Database (Denmark)

    Herz, Damian M.; Haagensen, Brian N.; Christensen, Mark S.

    2015-01-01

    Dopaminergic signalling in the striatum contributes to reinforcement of actions and motivational enhancement of motor vigour. Parkinson's disease leads to progressive dopaminergic denervation of the striatum, impairing the function of cortico-basal ganglia networks. While levodopa therapy...... of levodopa-induced dyskinesias. Twenty-six patients with Parkinson's disease (age range: 51–84 years; 11 females) received a single dose of levodopa and then performed a task in which they had to produce or suppress a movement in response to visual cues. Task-related activity was continuously mapped...... later developed levodopa-induced dyskinesias, but not patients without dyskinesias, showed a linear increase in connectivity between the putamen and primary motor cortex after levodopa intake during movement suppression. Individual dyskinesia severity was predicted by levodopa-induced modulation...

  18. The cortical acto-myosin network: from diffusion barrier to functional gateway in the transport of neurosecretory vesicles to the plasma membrane

    Directory of Open Access Journals (Sweden)

    Andreas ePapadopulos

    2013-10-01

    Full Text Available Dysregulation of regulated exocytosis is linked to an array of pathological conditions, including neurodegenerative disorders, asthma and diabetes. Understanding the molecular mechanisms underpinning neuroexocytosis including the processes that allow neurosecretory vesicles to access and fuse with the plasma membrane and to recycle post-fusion, is therefore critical to the design of future therapeutic drugs that will efficiently tackle these diseases. Despite considerable efforts to determine the principles of vesicular fusion, the mechanisms controlling the approach of vesicles to the plasma membrane in order to undergo tethering, docking, priming, and fusion remain poorly understood. All these steps involve the cortical actin network, a dense mesh of actin filaments localized beneath the plasma membrane. Recent work overturned the long-held belief that the cortical actin network only plays a passive constraining role in neuroexocytosis functioning as a physical barrier that partly breaks down upon entry of Ca2+ to allow secretory vesicles to reach the plasma membrane. A multitude of new roles for the cortical actin network in regulated exocytosis have now emerged and point to highly dynamic novel functions of key myosin molecular motors. Myosins are not only believed to help bring about dynamic changes in the actin cytoskeleton, tethering and guiding vesicles to their fusion sites, but they also regulate the size and duration of the fusion pore, thereby directly contributing to the release of neurotransmitters and hormones.Here we discuss the functions of the cortical actin network, myosins and their effectors in controlling the processes that lead to tethering, directed transport, docking, and fusion of exocytotic vesicles in regulated exocytosis.

  19. GABAergic synaptic inhibition is reduced before seizure onset in a genetic model of cortical malformation.

    Science.gov (United States)

    Trotter, Stacey A; Kapur, Jaideep; Anzivino, Matthew J; Lee, Kevin S

    2006-10-18

    Malformations of the neocortex are a common cause of human epilepsy; however, the critical issue of how disturbances in cortical organization render neurons epileptogenic remains controversial. The present study addressed this issue by studying inhibitory structure and function before seizure onset in the telencephalic internal structural heterotopia (tish) rat, which is a genetic model of heightened seizure susceptibility associated with a prominent neocortical malformation. Both normally positioned (normotopic) and misplaced (heterotopic) pyramidal neurons in the tish neocortex exhibited lower resting membrane potentials and a tendency toward higher input resistance compared with pyramidal neurons from control brains. GABAergic synaptic transmission was attenuated in the tish cortex, characterized by significant reductions in the frequency of spontaneous IPSCs (sIPSCs) and miniature IPSCs recorded from pyramidal neurons. In addition, the amplitudes of sIPSCs were reduced in the tish neocortex, an effect that was more profound in the normotopic cells. Immunohistochemical assessment of presynaptic GABAergic terminals showed a reduction in terminals surrounding pyramidal cell somata in normotopic and heterotopic tish neocortex. The attenuation of inhibitory innervation was more prominent for normotopic neurons and was associated with a reduction in a subset of GABAergic interneurons expressing the calcium-binding protein parvalbumin. Together, these findings indicate that key facets of inhibitory GABAergic neurotransmission are disturbed before seizure onset in a brain predisposed to developing seizures. Such alterations represent a rational substrate for reduced seizure thresholds associated with certain cortical malformations.

  20. Developmental abnormalities of cortical interneurons precede symptoms onset in a mouse model of Rett syndrome.

    Science.gov (United States)

    Tomassy, Giulio Srubek; Morello, Noemi; Calcagno, Eleonora; Giustetto, Maurizio

    2014-10-01

    Rett syndrome (RTT, MIM312750), a neurodevelopmental disorder predominantly occurring in females, is caused in the majority of cases by sporadic mutations in the gene encoding the transcriptional modulator methyl-CpG-binding protein 2 (MECP2). In mice, impaired MeCP2 function results in severe motor, cognitive, and emotional defects. The lack of Mecp2 in γ-aminobutyric acid-(GABA) releasing forebrain interneurons (INs) recapitulate many RTT features, however, the role of this gene in the development of the cortical inhibitory system is still unknown. Here, we found that MeCP2 expression varies among the three major classes of cortical INs and its nuclear localization differs between neuronal types. The density of calretinin(+) and parvalbumin(+) INs increases in Mecp2 knockout mice (Mecp2(-/y) ) already at early post-natal developmental stages. In contrast, the density of somatostatin(+) INs is not affected. We also found that the development of multipolar-calretinin(+) INs is selectively affected by the absence of Mecp2. Additionally, we show that in Mecp2 heterozygous female mice, a model closely mimicking human RTT condition, IN abnormalities are similar to those observed in Mecp2(-/y) mice. Together, our study indicates that loss of function of Mecp2 strongly interferes with the correct establishment of the neocortical inhibitory system producing effects that are specific to different IN subtypes.

  1. Cortical Spreading Depression Causes Unique Dysregulation of Inflammatory Pathways in a Transgenic Mouse Model of Migraine.

    Science.gov (United States)

    Eising, Else; Shyti, Reinald; 't Hoen, Peter A C; Vijfhuizen, Lisanne S; Huisman, Sjoerd M H; Broos, Ludo A M; Mahfouz, Ahmed; Reinders, Marcel J T; Ferrari, Michel D; Tolner, Else A; de Vries, Boukje; van den Maagdenberg, Arn M J M

    2017-05-01

    Familial hemiplegic migraine type 1 (FHM1) is a rare monogenic subtype of migraine with aura caused by mutations in CACNA1A that encodes the α1A subunit of voltage-gated CaV2.1 calcium channels. Transgenic knock-in mice that carry the human FHM1 R192Q missense mutation ('FHM1 R192Q mice') exhibit an increased susceptibility to cortical spreading depression (CSD), the mechanism underlying migraine aura. Here, we analysed gene expression profiles from isolated cortical tissue of FHM1 R192Q mice 24 h after experimentally induced CSD in order to identify molecular pathways affected by CSD. Gene expression profiles were generated using deep serial analysis of gene expression sequencing. Our data reveal a signature of inflammatory signalling upon CSD in the cortex of both mutant and wild-type mice. However, only in the brains of FHM1 R192Q mice specific genes are up-regulated in response to CSD that are implicated in interferon-related inflammatory signalling. Our findings show that CSD modulates inflammatory processes in both wild-type and mutant brains, but that an additional unique inflammatory signature becomes expressed after CSD in a relevant mouse model of migraine.

  2. The model of social crypto-network

    Directory of Open Access Journals (Sweden)

    Марк Миколайович Орел

    2015-06-01

    Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks

  3. Behavioral constraints in the development of neuronal properties: a cortical model embedded in a real-world device.

    Science.gov (United States)

    Almássy, N; Edelman, G M; Sporns, O

    1998-06-01

    The ability of organisms to categorize diverse and often novel stimuli depends on ongoing interactions with their environment. In a modality such as vision, categorization requires the generation of both selective and invariant responses of cortical neurons to complex visual stimuli. How does behavior contribute to shaping the responses of these neurons? Analysis of this question is made difficult by the complex multilevel interactions between many neural and behavioral variables. To mitigate this difficulty, we studied the development and ongoing plasticity of pattern-selective neuronal responses by means of synthetic neural modeling. For this purpose, we constructed Darwin V, which consists of a simulated neuronal model embedded in a real-world device that is capable of motion and autonomous behavior. The neuronal model consists of four major components: a visual system (containing cortical and subcortical networks); a taste system based on conductance; sets of motor neurons capable of triggering behavior; and a diffuse ascending (value) system. The modeled visual cortex consists of two areas: a topographic map responsive to elementary features connected to a higher-order map composed of initially non-selective neuronal units. During behavior over time in its environment, Darwin V encounters numerous objects consisting of black metal cubes displaying different patterns of white blobs and stripes. Initially, the lack of specific higher-order visual responses does not allow visual pattern discrimination, and appetitive and aversive behaviors are triggered by the 'taste' (surface conductivity of objects) alone. In the course of sensory experience, however, changes occur in visual and sensorimotor connection strengths, with two major consequences. First, units within the higher visual area acquire responses that are both pattern selective and translation invariant. Second, as a result of the operation of the value system, these responses become linked to appropriate

  4. A dynamic network model for interbank market

    Science.gov (United States)

    Xu, Tao; He, Jianmin; Li, Shouwei

    2016-12-01

    In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.

  5. Modeling Diagnostic Assessments with Bayesian Networks

    Science.gov (United States)

    Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

    2007-01-01

    This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

  6. Modeling Diagnostic Assessments with Bayesian Networks

    Science.gov (United States)

    Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

    2007-01-01

    This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

  7. Bayesian Network Webserver: a comprehensive tool for biological network modeling.

    Science.gov (United States)

    Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

    2013-11-01

    The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

  8. From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation

    Directory of Open Access Journals (Sweden)

    G. Cheron

    2012-01-01

    Full Text Available Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs. Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG, upper limb electromyogram (EMG, or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs or dynamic recurrent neural networks (DRNNs. Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

  9. Adaptation, expertise, and giftedness: towards an understanding of cortical, subcortical, and cerebellar network contributions.

    Science.gov (United States)

    Koziol, Leonard F; Budding, Deborah Ely; Chidekel, Dana

    2010-12-01

    Current cortico-centric models of cognition lack a cohesive neuroanatomic framework that sufficiently considers overlapping levels of function, from "pathological" through "normal" to "gifted" or exceptional ability. While most cognitive theories presume an evolutionary context, few actively consider the process of adaptation, including concepts of neurodevelopment. Further, the frequent co-occurrence of "gifted" and "pathological" function is difficult to explain from a cortico-centric point of view. This comprehensive review paper proposes a framework that includes the brain's vertical organization and considers "giftedness" from an evolutionary and neurodevelopmental vantage point. We begin by discussing the current cortico-centric model of cognition and its relationship to intelligence. We then review an integrated, dual-tiered model of cognition that better explains the process of adaptation by simultaneously allowing for both stimulus-based processing and higher-order cognitive control. We consider the role of the basal ganglia within this model, particularly in relation to reward circuitry and instrumental learning. We review the important role of white matter tracts in relation to speed of adaptation and development of behavioral mastery. We examine the cerebellum's critical role in behavioral refinement and in cognitive and behavioral automation, particularly in relation to expertise and giftedness. We conclude this integrated model of brain function by considering the savant syndrome, which we believe is best understood within the context of a dual-tiered model of cognition that allows for automaticity in adaptation as well as higher-order executive control.

  10. Gamma distribution model describes maturational curves for delta wave amplitude, cortical metabolic rate and synaptic density.

    Science.gov (United States)

    Feinberg, I; Thode, H C; Chugani, H T; March, J D

    1990-01-23

    We analyzed the available ontogenetic data (birth to 30 years of age) for: amplitude of delta EEG (DA) waves during sleep; cortical metabolic rate (CMR) measured with positron emission tomography; and synaptic density (SD) in frontal cortex. Each is at the adult level at birth, increases to about twice this level by 3 years of age, and then gradually falls back to the adult level over the next two decades. Statistical analyses revealed that individual gamma distribution models fit each data set as well as did the best ad hoc polynomial. A test of whether a single gamma distribution model could describe all three data sets gave good results for DA and CMR but the fit was unsatisfactory for SD. However, because so few data were available for SD, this test was not conclusive. We proposed the following model to account for these changes. First, cortical neurons are stimulated by birth to enter a proliferative state (PS) that creates many connections. Next, as a result of interactions in the PS, neurons are triggered into a transient organizational state (OS) in which they make enduring connections. The OS has a finite duration (minutes to years), and is characterized by high rates of information-processing and metabolism. Levels of CMR, SD and DA, therefore, are proportional to the number of neurons in the OS at any time. Thus, the cortex after birth duplicates, over a vastly greater time scale, the overproduction and regression of neural elements that occurs repeatedly in embryonic development. Finally, we discussed the implications of post-natal brain changes for normal and abnormal brain function. Mental disorders that have their onset after puberty (notably schizophrenia and manic-depressive psychoses) might be caused by errors in these late maturational processes. In addition to age of onset, this neurodevelopmental hypothesis might explain several other puzzling features of these subtle disorders.

  11. Increased susceptibility to cortical spreading depression in the mouse model of familial hemiplegic migraine type 2.

    Directory of Open Access Journals (Sweden)

    Loredana Leo

    2011-06-01

    Full Text Available Familial hemiplegic migraine type 2 (FHM2 is an autosomal dominant form of migraine with aura that is caused by mutations of the α2-subunit of the Na,K-ATPase, an isoform almost exclusively expressed in astrocytes in the adult brain. We generated the first FHM2 knock-in mouse model carrying the human W887R mutation in the Atp1a2 orthologous gene. Homozygous Atp1a2(R887/R887 mutants died just after birth, while heterozygous Atp1a2(+/R887 mice showed no apparent clinical phenotype. The mutant α2 Na,K-ATPase protein was barely detectable in the brain of homozygous mutants and strongly reduced in the brain of heterozygous mutants, likely as a consequence of endoplasmic reticulum retention and subsequent proteasomal degradation, as we demonstrate in transfected cells. In vivo analysis of cortical spreading depression (CSD, the phenomenon underlying migraine aura, revealed a decreased induction threshold and an increased velocity of propagation in the heterozygous FHM2 mouse. Since several lines of evidence involve a specific role of the glial α2 Na,K pump in active reuptake of glutamate from the synaptic cleft, we hypothesize that CSD facilitation in the FHM2 mouse model is sustained by inefficient glutamate clearance by astrocytes and consequent increased cortical excitatory neurotransmission. The demonstration that FHM2 and FHM1 mutations share the ability to facilitate induction and propagation of CSD in mouse models further support the role of CSD as a key migraine trigger.

  12. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

    Science.gov (United States)

    Ponzi, Adam; Wickens, Jeff

    2012-01-01

    The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

  13. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

    Directory of Open Access Journals (Sweden)

    Adam ePonzi

    2012-03-01

    Full Text Available The striatal medium spiny neuron (MSNs network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri stimulus time histograms (PSTH of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioural task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviourally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would in when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and delineate the range of parameters where this behaviour is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response

  14. GABAA Receptor-Mediated Activity in a Model of Cortical Dysplasia

    Science.gov (United States)

    2012-06-29

    epilepsy, autism and schizophrenia, and results from failure of immature neurons to appropriately migrate to their cortical targets. We developed a...schizophrenia, and autism , and results from failure of immature neurons to appropriately migrate and reach their cortical targets (Taylor et al., 1971; Choi and...appropriate cortical target is regulated by Brn1 and Brn2, POU-domain transcription factors that regulate the reelin /DAB1 pathway, and cyclin

  15. A Model for Cortical 40 Hz oscillations invokes inter-area interactions

    DEFF Research Database (Denmark)

    Cotterill, Rodney M J; Helix Nielsen, Claus

    1991-01-01

    COMPUTER simulation of the dynamics of neuronal assemblies within minicolumns, and of the interactions between minicolumns in different cortical areas, has produced a quantitative explanation of the 35-60 Hz oscillations recently observed in adult cat striate cortices. The observed behavior...... suggests an association mechanism that exploits the NMDA receptor's properties. Detectable oscillations are predicted in cortical areas not directly stimulated, provided these are associatively linked with areas receiving sensory input....

  16. Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model.

    Science.gov (United States)

    Stavisky, Sergey D; Kao, Jonathan C; Ryu, Stephen I; Shenoy, Krishna V

    2017-02-15

    Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models

  17. Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model.

    Science.gov (United States)

    Shuaiqi, Liu; Jie, Zhao; Mingzhu, Shi

    2015-01-01

    Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF) and spiking cortical model (SCM). Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.

  18. Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images

    Science.gov (United States)

    Zhang, Xuming; Li, Liu; Zhu, Fei; Hou, Wenguang; Chen, Xinjian

    2014-06-01

    Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.

  19. Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images.

    Science.gov (United States)

    Zhang, Xuming; Li, Liu; Zhu, Fei; Hou, Wenguang; Chen, Xinjian

    2014-06-01

    Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16  dB with a small 5.4% loss of similarity.

  20. Medical Image Fusion Based on Rolling Guidance Filter and Spiking Cortical Model

    Directory of Open Access Journals (Sweden)

    Liu Shuaiqi

    2015-01-01

    Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most of these approaches are sensitive to the noise and usually lead to fusion image distortion, and image information loss. Furthermore, they lack universality when dealing with different kinds of medical images. In this paper, we propose a new medical image fusion to overcome the aforementioned issues of the existing methods. It is achieved by combining with rolling guidance filter (RGF and spiking cortical model (SCM. Firstly, saliency of medical images can be captured by RGF. Secondly, a self-adaptive threshold of SCM is gained by utilizing the mean and variance of the source images. Finally, fused image can be gotten by SCM motivated by RGF coefficients. Experimental results show that the proposed method is superior to other current popular ones in both subjectively visual performance and objective criteria.

  1. Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis

    Directory of Open Access Journals (Sweden)

    Barker Jeffery L

    2009-08-01

    Full Text Available Abstract Background Cortical development is a complex process that includes sequential generation of neuronal progenitors, which proliferate and migrate to form the stratified layers of the developing cortex. To identify the individual microRNAs (miRNAs and mRNAs that may regulate the genetic network guiding the earliest phase of cortical development, the expression profiles of rat neuronal progenitors obtained at embryonic day 11 (E11, E12 and E13 were analyzed. Results Neuronal progenitors were purified from telencephalic dissociates by a positive-selection strategy featuring surface labeling with tetanus-toxin and cholera-toxin followed by fluorescence-activated cell sorting. Microarray analyses revealed the fractions of miRNAs and mRNAs that were up-regulated or down-regulated in these neuronal progenitors at the beginning of cortical development. Nearly half of the dynamically expressed miRNAs were negatively correlated with the expression of their predicted target mRNAs. Conclusion These data support a regulatory role for miRNAs during the transition from neuronal progenitors into the earliest differentiating cortical neurons. In addition, by supplying a robust data set in which miRNA and mRNA profiles originate from the same purified cell type, this empirical study may facilitate the development of new algorithms to integrate various "-omics" data sets.

  2. Bayesian estimation of the network autocorrelation model

    NARCIS (Netherlands)

    Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.

    2017-01-01

    The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of

  3. Object Oriented Modeling Of Social Networks

    NARCIS (Netherlands)

    Zeggelink, Evelien P.H.; Oosten, Reinier van; Stokman, Frans N.

    1996-01-01

    The aim of this paper is to explain principles of object oriented modeling in the scope of modeling dynamic social networks. As such, the approach of object oriented modeling is advocated within the field of organizational research that focuses on networks. We provide a brief introduction into the f

  4. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  5. Linear summation of outputs in a balanced network model of motor cortex.

    Science.gov (United States)

    Capaday, Charles; van Vreeswijk, Carl

    2015-01-01

    Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.

  6. Edge exchangeable models for network data

    CERN Document Server

    Crane, Harry

    2016-01-01

    Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing that edges, not vertices, act as the statistical units in most network datasets, making a theory of edge labeled networks more natural for most applications. Within this context we introduce the new invariance principle of {\\em edge exchangeability}, which unlike its vertex exchangeable counterpart can produce networks with sparse and/or power law structure. We characterize the class of all edge exchangeable network models and identify a particular two parameter family of models with suitable theoretical properties for statistical inference. We discuss issues of estimation from edge exchangeable models and compare our a...

  7. Evolutionary Phylogenetic Networks: Models and Issues

    Science.gov (United States)

    Nakhleh, Luay

    Phylogenetic networks are special graphs that generalize phylogenetic trees to allow for modeling of non-treelike evolutionary histories. The ability to sequence multiple genetic markers from a set of organisms and the conflicting evolutionary signals that these markers provide in many cases, have propelled research and interest in phylogenetic networks to the forefront in computational phylogenetics. Nonetheless, the term 'phylogenetic network' has been generically used to refer to a class of models whose core shared property is tree generalization. Several excellent surveys of the different flavors of phylogenetic networks and methods for their reconstruction have been written recently. However, unlike these surveys, this chapte focuses specifically on one type of phylogenetic networks, namely evolutionary phylogenetic networks, which explicitly model reticulate evolutionary events. Further, this chapter focuses less on surveying existing tools, and addresses in more detail issues that are central to the accurate reconstruction of phylogenetic networks.

  8. Modeling Network Evolution Using Graph Motifs

    CERN Document Server

    Conway, Drew

    2011-01-01

    Network structures are extremely important to the study of political science. Much of the data in its subfields are naturally represented as networks. This includes trade, diplomatic and conflict relationships. The social structure of several organization is also of interest to many researchers, such as the affiliations of legislators or the relationships among terrorist. A key aspect of studying social networks is understanding the evolutionary dynamics and the mechanism by which these structures grow and change over time. While current methods are well suited to describe static features of networks, they are less capable of specifying models of change and simulating network evolution. In the following paper I present a new method for modeling network growth and evolution. This method relies on graph motifs to generate simulated network data with particular structural characteristic. This technique departs notably from current methods both in form and function. Rather than a closed-form model, or stochastic ...

  9. Queuing theory models for computer networks

    Science.gov (United States)

    Galant, David C.

    1989-01-01

    A set of simple queuing theory models which can model the average response of a network of computers to a given traffic load has been implemented using a spreadsheet. The impact of variations in traffic patterns and intensities, channel capacities, and message protocols can be assessed using them because of the lack of fine detail in the network traffic rates, traffic patterns, and the hardware used to implement the networks. A sample use of the models applied to a realistic problem is included in appendix A. Appendix B provides a glossary of terms used in this paper. This Ames Research Center computer communication network is an evolving network of local area networks (LANs) connected via gateways and high-speed backbone communication channels. Intelligent planning of expansion and improvement requires understanding the behavior of the individual LANs as well as the collection of networks as a whole.

  10. Oxygen flux reduces Cux1 positive neurons and cortical growth in a gestational rodent model of growth restriction.

    Science.gov (United States)

    Fletcher, Elaine; Wade, Jean; Georgala, Petrina A; Gillespie, Trudi L; Price, David J; Pilley, Elizabeth; Becher, Julie-Clare

    2017-03-01

    The mammalian cerebral cortex forms in an inside-out manner, establishing deep cortical layers before superficial layers and is regulated by transcription factors which influence cell differentiation. Preterm birth interrupts the trajectory of normal neurodevelopment and adverse perinatal exposures have been implicated in cortical injury. We hypothesise that growth restriction (GR) and fluctuating hyperoxia (ΔO2) impair cortical laminar development. Sprague-Dawley rats received 18% (non-restricted, NR) or 9% (growth restricted, GR) protein diet from E15-P7. Litters were reared in air or fluctuating hyperoxia (circa 10kPa) from P0 to P7. Cortical laminae were stained and measured. Neuronal subtypes were quantified using immunofluorescence for subtype-specific transcription factors (Satb2, Cux1, Ctip2, Tbr1). ΔO2 did not affect brain weight at P7 but reduced cortical thickness in both NR (pdevelopment in a rodent model with preferential disadvantage to superficial neurons. Copyright © 2016 Elsevier GmbH. All rights reserved.

  11. Formulaic language in cortical and subcortical disease: Evidence of the dual process model.

    Directory of Open Access Journals (Sweden)

    Kelly Bridges

    2014-04-01

    Full Text Available Introduction: It is known that an intact cortical left hemisphere is crucial for language production. Recently, more credit is given to the right hemisphere and subcortical areas in the production of non-novel language, including formulaic language. John Hughlings Jackson (1874/1958, first described how propositional and non-propositional speech are differentially affected by neural impairment. Non-propositional language is often preserved following left hemisphere stroke even when aphasia is present (Code, 1982; Sidtis et al., 2009; Van Lancker Sidtis & Postman, 2006. With right hemisphere and subcortical stroke, formulaic language is reduced (Sidtis et al., 2009; Van Lancker Sidtis & Postman, 2006; Speedie et al., 1993. The dual process model of language competence states that propositional and non-propositional speech are processed differently in the brain, with novel speech controlled by the left hemisphere, and a right hemisphere/subcortical circuit modulating formulaic language (Van Lancker Sidtis, 2004; 2012. Two studies of formulaic language will be presented as further evidence of the dual process model: a study of formulaic language in Alzheimer’s disease, and a study of recited speech in Parkinson’s disease. Formulaic language includes overlearned words, phrases or longer linguistic units that are known to the native speaker, occur naturally in discourse, and are important for normal social interaction (Fillmore, 1979; Pawley & Syder, 1983; Van Lancker, 1988; Van Lancker Sidtis, 2004; Wray, 2002. Formulaic expressions include conversational speech formulas, idioms, proverbs, expletives, pause fillers, discourse elements, and sentence stems (stereotyped sentence-initials. Longer units of linguistic material, such as prayers, rhymes, and poems, termed recited speech, is another subtype of formulaic language that is learned in childhood and recited periodically throughout life. Cortical disease: Alzheimer’s disease and formulaic

  12. Altered Theta Oscillations and Aberrant Cortical Excitatory Activity in the 5XFAD Model of Alzheimer’s Disease

    Directory of Open Access Journals (Sweden)

    Magdalena Elisabeth Siwek

    2015-01-01

    Full Text Available Alzheimer’s disease (AD is an age-related neurodegenerative disorder characterized by impairment of memory function. The 5XFAD mouse model was analyzed and compared with wild-type (WT controls for aberrant cortical excitability and hippocampal theta oscillations by using simultaneous video-electroencephalogram (EEG monitoring. Seizure staging revealed that 5XFAD mice exhibited cortical hyperexcitability whereas controls did not. In addition, 5XFAD mice displayed a significant increase in hippocampal theta activity from the light to dark phase during nonmotor activity. We also observed a reduction in mean theta frequency in 5XFAD mice compared to controls that was again most prominent during nonmotor activity. Transcriptome analysis of hippocampal probes and subsequent qPCR validation revealed an upregulation of Plcd4 that might be indicative of enhanced muscarinic signalling. Our results suggest that 5XFAD mice exhibit altered cortical excitability, hippocampal dysrhythmicity, and potential changes in muscarinic signaling.

  13. Comparative proteomics of rat brain in the BCNU-induced model of cortical dysplasia

    Institute of Scientific and Technical Information of China (English)

    郭谊

    2014-01-01

    Objective To screen the differential proteins in the brain(neocortex and hippocampus)between the rats with cortical dysplasia(CD)and control ones,and investigate the role of their alteration in the development of epilepsy in CD.Methods Cortical dysplasia was induced in rat pups via in utero delivery of BCNU.A two-dimensional electrophoresis

  14. Unilateral and bilateral cortical resection: Effects on spike-wave discharges in a genetic absence epilepsy model

    NARCIS (Netherlands)

    Scicchitano, F.; Rijn, C.M. van; Luijtelaar, E.L.J.M. van

    2015-01-01

    Research Question Recent discoveries have challenged the traditional view that the thalamus is the primary source driving spike-and-wave discharges (SWDs). At odds, SWDs in genetic absence models have a cortical focal origin in the deep layers of the perioral region of the somatosensory cortex. The

  15. Computational model of cerebral blood flow redistribution during cortical spreading depression

    Science.gov (United States)

    Verisokin, Andrey Y.; Verveyko, Darya V.; Postnov, Dmitry E.

    2016-04-01

    In recent decades modelling studies on cortical spreading depression (CSD) and migraine waves successfully contributed to formation of modern view on these fundamental phenomena of brain physiology. However, due to the extreme complexity of object under study (brain cortex) and the diversity of involved physiological pathways, the development of new mathematical models of CSD is still a very relevant and challenging research problem. In our study we follow the functional modelling approach aimed to map the action of known physiological pathways to the specific nonlinear mechanisms that govern formation and evolution of CSD wave patterns. Specifically, we address the role of cerebral blood flow (CBF) redistribution that is caused by excessive neuronal activity by means of neurovascular coupling and mediates a spatial pattern of oxygen and glucose delivery. This in turn changes the local metabolic status of neural tissue. To build the model we simplify the web of known cell-to-cell interactions within a neurovascular unit by selecting the most relevant ones, such as local neuron-induced elevation of extracellular potassium concentration and biphasic response of arteriole radius. We propose the lumped description of distance-dependent hemodynamic coupling that fits the most recent experimental findings.

  16. Modeling the Formation Process of Grouping Stimuli Sets through Cortical Columns and Microcircuits to Feature Neurons

    Directory of Open Access Journals (Sweden)

    Frank Klefenz

    2013-01-01

    Full Text Available A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform.

  17. Random graph models for dynamic networks

    CERN Document Server

    Zhang, Xiao; Newman, M E J

    2016-01-01

    We propose generalizations of a number of standard network models, including the classic random graph, the configuration model, and the stochastic block model, to the case of time-varying networks. We assume that the presence and absence of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. In addition to computing equilibrium properties of these models, we demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data. This allows us, for instance, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate our methods with a selection of applications, both to computer-generated test networks and real-world examples.

  18. The development of neural synchrony and large-scale cortical networks during adolescence: relevance for the pathophysiology of schizophrenia and neurodevelopmental hypothesis.

    Science.gov (United States)

    Uhlhaas, Peter J; Singer, Wolf

    2011-05-01

    Recent data from developmental cognitive neuroscience highlight the profound changes in the organization and function of cortical networks during the transition from adolescence to adulthood. While previous studies have focused on the development of gray and white matter, recent evidence suggests that brain maturation during adolescence extends to fundamental changes in the properties of cortical circuits that in turn promote the precise temporal coding of neural activity. In the current article, we will highlight modifications in the amplitude and synchrony of neural oscillations during adolescence that may be crucial for the emergence of cognitive deficits and psychotic symptoms in schizophrenia. Specifically, we will suggest that schizophrenia is associated with impaired parameters of synchronous oscillations that undergo changes during late brain maturation, suggesting an important role of adolescent brain development for the understanding, treatment, and prevention of the disorder.

  19. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  20. Biodiversity: modelling angiosperms as networks.

    Science.gov (United States)

    Gottlieb, O R; Borin, M R

    2000-11-01

    In the neotropics, one of the last biological frontiers, the major ecological concern should not involve local strategies, but global effects often responsible for irreparable damage. For a holistic approach, angiosperms are ideal model systems dominating most land areas of the present world in an astonishing variety of form and function. Recognition of biogeographical patterns requires new methodologies and entails several questions, such as their nature, dynamics and mechanism. Demographical patterns of families, modelled via species dominance, reveal the existence of South American angiosperm networks converging at the central Brazilian plateau. Biodiversity of habitats, measured via taxonomic uniqueness, reveal higher creative power at this point of convergence than in more peripheral regions. Compositional affinities of habitats, measured via bioconnectivity, reveal the decisive role of ecotones in the exchange or redistribution of information, energy and organisms among the ecosystems. Forming dynamic boundaries, ecotones generate and relay evolutionary novelty, and integrate all neotropical ecosystems into a single vegetation net. Connectivity in such plant webs may depend on mycorrhizal links. If sufficiently general such means of metabolic transfer will require revision of the chemical composition of many plants.

  1. A Cortical Edge-integration Model of Object-Based Lightness Computation that Explains Effects of Spatial Context and Individual Differences

    Directory of Open Access Journals (Sweden)

    Michael E Rudd

    2014-08-01

    Full Text Available Previous work demonstrated that perceived surface reflectance (lightness can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatial integrates these steps along paths through the image to compute lightness (Rudd & Zemach, 2004, 2005, 2007. This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013 suggests that the human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010 further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer’s interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd & Zemach, 2005. Here, I show how the separate influences of grouping and attention on lightness can be together modeled by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013, and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4.

  2. A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

    Science.gov (United States)

    Rudd, Michael E.

    2014-01-01

    Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4. PMID:25202253

  3. Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error.

    Science.gov (United States)

    Stenroos, Matti; Hauk, Olaf

    2013-11-01

    The conductivity profile of the head has a major effect on EEG signals, but unfortunately the conductivity for the most important compartment, skull, is only poorly known. In dipole modeling studies, errors in modeled skull conductivity have been considered to have a detrimental effect on EEG source estimation. However, as dipole models are very restrictive, those results cannot be generalized to other source estimation methods. In this work, we studied the sensitivity of EEG and combined MEG+EEG source estimation to errors in skull conductivity using a distributed source model and minimum-norm (MN) estimation. We used a MEG/EEG modeling set-up that reflected state-of-the-art practices of experimental research. Cortical surfaces were segmented and realistically-shaped three-layer anatomical head models were constructed, and forward models were built with Galerkin boundary element method while varying the skull conductivity. Lead-field topographies and MN spatial filter vectors were compared across conductivities, and the localization and spatial spread of the MN estimators were assessed using intuitive resolution metrics. The results showed that the MN estimator is robust against errors in skull conductivity: the conductivity had a moderate effect on amplitudes of lead fields and spatial filter vectors, but the effect on corresponding morphologies was small. The localization performance of the EEG or combined MEG+EEG MN estimator was only minimally affected by the conductivity error, while the spread of the estimate varied slightly. Thus, the uncertainty with respect to skull conductivity should not prevent researchers from applying minimum norm estimation to EEG or combined MEG+EEG data. Comparing our results to those obtained earlier with dipole models shows that general judgment on the performance of an imaging modality should not be based on analysis with one source estimation method only.

  4. Modeling of hysteresis in gene regulatory networks.

    Science.gov (United States)

    Hu, J; Qin, K R; Xiang, C; Lee, T H

    2012-08-01

    Hysteresis, observed in many gene regulatory networks, has a pivotal impact on biological systems, which enhances the robustness of cell functions. In this paper, a general model is proposed to describe the hysteretic gene regulatory network by combining the hysteresis component and the transient dynamics. The Bouc-Wen hysteresis model is modified to describe the hysteresis component in the mammalian gene regulatory networks. Rigorous mathematical analysis on the dynamical properties of the model is presented to ensure the bounded-input-bounded-output (BIBO) stability and demonstrates that the original Bouc-Wen model can only generate a clockwise hysteresis loop while the modified model can describe both clockwise and counter clockwise hysteresis loops. Simulation studies have shown that the hysteresis loops from our model are consistent with the experimental observations in three mammalian gene regulatory networks and two E.coli gene regulatory networks, which demonstrate the ability and accuracy of the mathematical model to emulate natural gene expression behavior with hysteresis. A comparison study has also been conducted to show that this model fits the experiment data significantly better than previous ones in the literature. The successful modeling of the hysteresis in all the five hysteretic gene regulatory networks suggests that the new model has the potential to be a unified framework for modeling hysteresis in gene regulatory networks and provide better understanding of the general mechanism that drives the hysteretic function.

  5. Cortical metabolites as biomarkers in the R6/2 model of Huntington's disease.

    Science.gov (United States)

    Zacharoff, Lori; Tkac, Ivan; Song, Qingfeng; Tang, Chuanning; Bolan, Patrick J; Mangia, Silvia; Henry, Pierre-Gilles; Li, Tongbin; Dubinsky, Janet M

    2012-03-01

    To improve the ability to move from preclinical trials in mouse models of Huntington's disease (HD) to clinical trials in humans, biomarkers are needed that can track similar aspects of disease progression across species. Brain metabolites, detectable by magnetic resonance spectroscopy (MRS), have been suggested as potential biomarkers in HD. In this study, the R6/2 transgenic mouse model of HD was used to investigate the relative sensitivity of the metabolite profiling and the brain volumetry to anticipate the disease progression. Magnetic resonance imaging (MRI) and (1)H MRS data were acquired at 9.4 T from the R6/2 mice and wild-type littermates at 4, 8, 12, and 15 weeks. Brain shrinkage was detectable in striatum, cortex, thalamus, and hypothalamus by 12 weeks. Metabolite changes in cortex paralleled and sometimes preceded those in striatum. The entire set of metabolite changes was compressed into principal components (PCs) using Partial Least Squares-Discriminant Analysis (PLS-DA) to increase the sensitivity for monitoring disease progression. In comparing the efficacy of volume and metabolite measurements, the cortical PC1 emerged as the most sensitive single biomarker, distinguishing R6/2 mice from littermates at all time points. Thus, neurochemical changes precede volume shrinkage and become potential biomarkers for HD mouse models.

  6. Nonconsensus opinion model on directed networks

    NARCIS (Netherlands)

    Qu, B.; Li, Q.; Havlin, S.; Stanley, E.; Wang, H.

    2014-01-01

    Dynamic social opinion models have been widely studied on undirected networks, and most of them are based on spin interaction models that produce a consensus. In reality, however, many networks such as Twitter and the World Wide Web are directed and are composed of both unidirectional and bidirectio

  7. Radio channel modeling in body area networks

    NARCIS (Netherlands)

    An, L.; Bentum, M.J.; Meijerink, A.; Scanlon, W.G.

    2010-01-01

    A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to detect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation in

  8. Radio channel modeling in body area networks

    NARCIS (Netherlands)

    An, L.; Bentum, M.J.; Meijerink, A.; Scanlon, W.G.

    2009-01-01

    A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to de- tect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation

  9. Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation Is State-Dependent.

    Directory of Open Access Journals (Sweden)

    Sankaraleengam Alagapan

    2016-03-01

    Full Text Available Cortical oscillations play a fundamental role in organizing large-scale functional brain networks. Noninvasive brain stimulation with temporally patterned waveforms such as repetitive transcranial magnetic stimulation (rTMS and transcranial alternating current stimulation (tACS have been proposed to modulate these oscillations. Thus, these stimulation modalities represent promising new approaches for the treatment of psychiatric illnesses in which these oscillations are impaired. However, the mechanism by which periodic brain stimulation alters endogenous oscillation dynamics is debated and appears to depend on brain state. Here, we demonstrate with a static model and a neural oscillator model that recurrent excitation in the thalamo-cortical circuit, together with recruitment of cortico-cortical connections, can explain the enhancement of oscillations by brain stimulation as a function of brain state. We then performed concurrent invasive recording and stimulation of the human cortical surface to elucidate the response of cortical oscillations to periodic stimulation and support the findings from the computational models. We found that (1 stimulation enhanced the targeted oscillation power, (2 this enhancement outlasted stimulation, and (3 the effect of stimulation depended on behavioral state. Together, our results show successful target engagement of oscillations by periodic brain stimulation and highlight the role of nonlinear interaction between endogenous network oscillations and stimulation. These mechanistic insights will contribute to the design of adaptive, more targeted stimulation paradigms.

  10. A mathematical model of cortical bone remodeling at cellular level under mechanical stimulus

    Institute of Scientific and Technical Information of China (English)

    Qing-Hua Qin; Ya-Nan Wang

    2012-01-01

    A bone cell population dynamics model for cortical bone remodeling under mechanical stimulus is developed in this paper.The external experiments extracted from the literature which have not been used in the creation of the model are used to test the validity of the model.Not only can the model compare reasonably well with these experimental results such as the increase percentage of final values of bone mineral content (BMC) and bone fracture energy (BFE) among different loading schemes (which proves the validity of the model),but also predict the realtime development pattern of BMC and BFE,as well as the dynamics of osteoblasts (OBA),osteoclasts (OCA),nitric oxide (NO) and prostaglandin E2 (PGE2) for each loading scheme,which can hardly be monitored through experiment.In conclusion,the model is the first of its kind that is able to provide an insight into the quantitative mechanism of bone remodeling at cellular level by which bone cells are activated by mechanical stimulus in order to start resorption/formation of bone mass.More importantly,this model has laid a solid foundation based on which future work such as systemic control theory analysis of bone remodeling under mechanical stimulus can be investigated.The to-be identified control mechanism will help to develop effective drugs and combined nonpharmacological therapies to combat bone loss pathologies.Also this deeper understanding of how mechanical forces quantitatively interact with skeletal tissue is essential for the generation of bone tissue for tissue replacement purposes in tissue engineering.

  11. Network models in economics and finance

    CERN Document Server

    Pardalos, Panos; Rassias, Themistocles

    2014-01-01

    Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis  that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

  12. Unified Hybrid Network Theoretical Model Trilogy

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    The first of the unified hybrid network theoretical model trilogy (UHNTF) is the harmonious unification hybrid preferential model (HUHPM), seen in the inner loop of Fig. 1, the unified hybrid ratio is defined.

  13. A Network Formation Model Based on Subgraphs

    CERN Document Server

    Chandrasekhar, Arun

    2016-01-01

    We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed. We illustrate the models via a series of applications including testing for incentives to form cross-caste relationships in rural India, testing to see whether network structure is used to enforce risk-sharing, testing as to whether networks change in response to a community's exposure to microcredit, and show that these models significantly outperform stochastic block models in matching observed network characteristics. We also establish asymptotic properties of the models and various estimators, which requires proving a new Central Limit Theorem for correlated random variables.

  14. An in silico agent-based model demonstrates Reelin function in directing lamination of neurons during cortical development.

    Directory of Open Access Journals (Sweden)

    James R Caffrey

    Full Text Available The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration. A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.

  15. Gossip spread in social network Models

    Science.gov (United States)

    Johansson, Tobias

    2017-04-01

    Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

  16. Strategic games on a hierarchical network model

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Among complex network models, the hierarchical network model is the one most close to such real networks as world trade web, metabolic network, WWW, actor network, and so on. It has not only the property of power-law degree distribution, but growth based on growth and preferential attachment, showing the scale-free degree distribution property. In this paper, we study the evolution of cooperation on a hierarchical network model, adopting the prisoner's dilemma (PD) game and snowdrift game (SG) as metaphors of the interplay between connected nodes. BA model provides a unifying framework for the emergence of cooperation. But interestingly, we found that on hierarchical model, there is no sign of cooperation for PD game, while the frequency of cooperation decreases as the common benefit decreases for SG. By comparing the scaling clustering coefficient properties of the hierarchical network model with that of BA model, we found that the former amplifies the effect of hubs. Considering different performances of PD game and SG on complex network, we also found that common benefit leads to cooperation in the evolution. Thus our study may shed light on the emergence of cooperation in both natural and social environments.

  17. Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

    Science.gov (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

    2016-09-14

    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  18. Towards reproducible descriptions of neuronal network models.

    Directory of Open Access Journals (Sweden)

    Eilen Nordlie

    2009-08-01

    Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

  19. A mathematical model relating cortical oxygenated and deoxygenated hemoglobin flows and volumes to neural activity

    Science.gov (United States)

    Cornelius, Nathan R.; Nishimura, Nozomi; Suh, Minah; Schwartz, Theodore H.; Doerschuk, Peter C.

    2015-08-01

    Objective. To describe a toolkit of components for mathematical models of the relationship between cortical neural activity and space-resolved and time-resolved flows and volumes of oxygenated and deoxygenated hemoglobin motivated by optical intrinsic signal imaging (OISI). Approach. Both blood flow and blood volume and both oxygenated and deoxygenated hemoglobin and their interconversion are accounted for. Flow and volume are described by including analogies to both resistive and capacitive electrical circuit elements. Oxygenated and deoxygenated hemoglobin and their interconversion are described by generalization of Kirchhoff's laws based on well-mixed compartments. Main results. Mathematical models built from this toolkit are able to reproduce experimental single-stimulus OISI results that are described in papers from other research groups and are able to describe the response to multiple-stimuli experiments as a sublinear superposition of responses to the individual stimuli. Significance. The same assembly of tools from the toolkit but with different parameter values is able to describe effects that are considered distinctive, such as the presence or absence of an initial decrease in oxygenated hemoglobin concentration, indicating that the differences might be due to unique parameter values in a subject rather than different fundamental mechanisms.

  20. Symbolic regression of generative network models

    CERN Document Server

    Menezes, Telmo

    2014-01-01

    Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world netwo...

  1. Synchronized Beta-Band Oscillations in a Model of the Globus Pallidus-Subthalamic Nucleus Network under External Input

    Science.gov (United States)

    Ahn, Sungwoo; Zauber, S. Elizabeth; Worth, Robert M.; Rubchinsky, Leonid L.

    2016-01-01

    Hypokinetic symptoms of Parkinson's disease are usually associated with excessively strong oscillations and synchrony in the beta frequency band. The origin of this synchronized oscillatory dynamics is being debated. Cortical circuits may be a critical source of excessive beta in Parkinson's disease. However, subthalamo-pallidal circuits were also suggested to be a substantial component in generation and/or maintenance of Parkinsonian beta activity. Here we study how the subthalamo-pallidal circuits interact with input signals in the beta frequency band, representing cortical input. We use conductance-based models of the subthalamo-pallidal network and two types of input signals: artificially-generated inputs and input signals obtained from recordings in Parkinsonian patients. The resulting model network dynamics is compared with the dynamics of the experimental recordings from patient's basal ganglia. Our results indicate that the subthalamo-pallidal model network exhibits multiple resonances in response to inputs in the beta band. For a relatively broad range of network parameters, there is always a certain input strength, which will induce patterns of synchrony similar to the experimentally observed ones. This ability of the subthalamo-pallidal network to exhibit realistic patterns of synchronous oscillatory activity under broad conditions may indicate that these basal ganglia circuits are directly involved in the expression of Parkinsonian synchronized beta oscillations. Thus, Parkinsonian synchronized beta oscillations may be promoted by the simultaneous action of both cortical (or some other) and subthalamo-pallidal network mechanisms. Hence, these mechanisms are not necessarily mutually exclusive. PMID:28066222

  2. Complex networks analysis in socioeconomic models

    CERN Document Server

    Varela, Luis M; Ausloos, Marcel; Carrete, Jesus

    2014-01-01

    This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including res...

  3. [Cortical blindness].

    Science.gov (United States)

    Chokron, S

    2014-02-01

    Cortical blindness refers to a visual loss induced by a bilateral occipital lesion. The very strong cooperation between psychophysics, cognitive psychology, neurophysiology and neuropsychology these latter twenty years as well as recent progress in cerebral imagery have led to a better understanding of neurovisual deficits, such as cortical blindness. It thus becomes possible now to propose an earlier diagnosis of cortical blindness as well as new perspectives for rehabilitation in children as well as in adults. On the other hand, studying complex neurovisual deficits, such as cortical blindness is a way to infer normal functioning of the visual system.

  4. Reduced GABAergic inhibition explains cortical hyperexcitability in the wobbler mouse model of ALS

    DEFF Research Database (Denmark)

    Nieto-Gonzalez, Jose Luis; Moser, Jakob; Lauritzen, Martin

    2011-01-01

    Amyotrophic lateral sclerosis (ALS) is a progressive degenerative disease of the central nervous system. Symptomatic and presymptomatic ALS patients demonstrate cortical hyperexcitability, which raises the possibility that alterations in inhibitory gamma-aminobutyric acid (GABA)ergic system could...

  5. Evolving Models of Pavlovian Conditioning : Cerebellar Cortical Dynamics in Awake Behaving Mice

    NARCIS (Netherlands)

    Ten Brinke, Michiel M; Boele, Henk-Jan; Spanke, Jochen K; Potters, Jan-Willem; Kornysheva, Katja; Wulff, Peer; IJpelaar, Anna C H G; Koekkoek, Sebastiaan K E; De Zeeuw, Chris I

    2015-01-01

    Three decades of electrophysiological research on cerebellar cortical activity underlying Pavlovian conditioning have expanded our understanding of motor learning in the brain. Purkinje cell simple spike suppression is considered to be crucial in the expression of conditional blink responses (CRs).

  6. Boolean networks as modelling framework

    Directory of Open Access Journals (Sweden)

    Florian eGreil

    2012-08-01

    Full Text Available In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and the immediate application in plant biology, references for more in-depths treatment of the mathematical details are also given.

  7. Deep supervised, but not unsupervised, models may explain IT cortical representation.

    Directory of Open Access Journals (Sweden)

    Seyed-Mahdi Khaligh-Razavi

    2014-11-01

    Full Text Available Inferior temporal (IT cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total, testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network. We compared the representational dissimilarity matrices (RDMs of the model representations with the RDMs obtained from human IT (measured with fMRI and monkey IT (measured with cell recording for the same set of stimuli (not used in training the models. Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining

  8. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation

    Science.gov (United States)

    Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus

    2014-01-01

    Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires

  9. Implementing network constraints in the EMPS model

    Energy Technology Data Exchange (ETDEWEB)

    Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.

    2010-02-15

    This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)

  10. Early development of synchrony in cortical activations in the human.

    Science.gov (United States)

    Koolen, N; Dereymaeker, A; Räsänen, O; Jansen, K; Vervisch, J; Matic, V; Naulaers, G; De Vos, M; Van Huffel, S; Vanhatalo, S

    2016-05-13

    Early intermittent cortical activity is thought to play a crucial role in the growth of neuronal network development, and large scale brain networks are known to provide the basis for higher brain functions. Yet, the early development of the large scale synchrony in cortical activations is unknown. Here, we tested the hypothesis that the early intermittent cortical activations seen in the human scalp EEG show a clear developmental course during the last trimester of pregnancy, the period of intensive growth of cortico-cortical connections. We recorded scalp EEG from altogether 22 premature infants at post-menstrual age between 30 and 44 weeks, and the early cortical synchrony was quantified using recently introduced activation synchrony index (ASI). The developmental correlations of ASI were computed for individual EEG signals as well as anatomically and mathematically defined spatial subgroups. We report two main findings. First, we observed a robust and statistically significant increase in ASI in all cortical areas. Second, there were significant spatial gradients in the synchrony in fronto-occipital and left-to-right directions. These findings provide evidence that early cortical activity is increasingly synchronized across the neocortex. The ASI-based metrics introduced in our work allow direct translational comparison to in vivo animal models, as well as hold promise for implementation as a functional developmental biomarker in future research on human neonates.

  11. Modelling Microwave Devices Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Andrius Katkevičius

    2012-04-01

    Full Text Available Artificial neural networks (ANN have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics.Article in Lithuanian

  12. Delivery Time Reliability Model of Logistics Network

    OpenAIRE

    Liusan Wu; Qingmei Tan; Yuehui Zhang

    2013-01-01

    Natural disasters like earthquake and flood will surely destroy the existing traffic network, usually accompanied by delivery delay or even network collapse. A logistics-network-related delivery time reliability model defined by a shortest-time entropy is proposed as a means to estimate the actual delivery time reliability. The less the entropy is, the stronger the delivery time reliability remains, and vice versa. The shortest delivery time is computed separately based on two different assum...

  13. Modeling Evolution of Weighted Clique Networks

    Institute of Scientific and Technical Information of China (English)

    杨旭华; 蒋峰岭; 陈胜勇; 王万良

    2011-01-01

    We propose a weighted clique network evolution model, which expands continuously by the addition of a new clique (maximal complete sub-graph) at. each time step. And the cliques in the network overlap with each other. The structural expansion of the weighted clique network is combined with the edges' weight and vertices' strengths dynamical evolution. The model is based on a weight-driven dynamics and a weights' enhancement mechanism combining with the network growth. We study the network properties, which include the distribution of vertices' strength and the distribution o~ edges' weight, and find that both the distributions follow the scale-free distribution. At the same time, we also find that the relationship between strength and degree of a vertex are linear correlation during the growth of the network. On the basis of mean-field theory, we study the weighted network model and prove that both vertices' strength and edges' weight of this model follow the scale-free distribution. And we exploit an algorithm to forecast the network dynamics, which can be used to reckon the distributions and the corresponding scaling exponents. Furthermore, we observe that mean-field based theoretic results are consistent with the statistical data of the model, which denotes the theoretical result in this paper is effective.

  14. Modeling Evolution of Weighted Clique Networks

    Science.gov (United States)

    Yang, Xu-Hua; Jiang, Feng-Ling; Chen, Sheng-Yong; Wang, Wan-Liang

    2011-11-01

    We propose a weighted clique network evolution model, which expands continuously by the addition of a new clique (maximal complete sub-graph) at each time step. And the cliques in the network overlap with each other. The structural expansion of the weighted clique network is combined with the edges' weight and vertices' strengths dynamical evolution. The model is based on a weight-driven dynamics and a weights' enhancement mechanism combining with the network growth. We study the network properties, which include the distribution of vertices' strength and the distribution of edges' weight, and find that both the distributions follow the scale-free distribution. At the same time, we also find that the relationship between strength and degree of a vertex are linear correlation during the growth of the network. On the basis of mean-field theory, we study the weighted network model and prove that both vertices' strength and edges' weight of this model follow the scale-free distribution. And we exploit an algorithm to forecast the network dynamics, which can be used to reckon the distributions and the corresponding scaling exponents. Furthermore, we observe that mean-field based theoretic results are consistent with the statistical data of the model, which denotes the theoretical result in this paper is effective.

  15. Survey of propagation Model in wireless Network

    Directory of Open Access Journals (Sweden)

    Hemant Kumar Sharma

    2011-05-01

    Full Text Available To implementation of mobile ad hoc network wave propagation models are necessary to determine propagation characteristic through a medium. Wireless mobile ad hoc networks are self creating and self organizing entity. Propagation study provides an estimation of signal characteristics. Accurate prediction of radio propagation behaviour for MANET is becoming a difficult task. This paper presents investigation of propagation model. Radio wave propagation mechanisms are absorption, reflection, refraction, diffraction and scattering. This paper discuss free space model, two rays model, and cost 231 hata and its variants and fading model, and summarized the advantages and disadvantages of these model. This study would be helpful in choosing the correct propagation model.

  16. Modelling of virtual production networks

    Directory of Open Access Journals (Sweden)

    2011-03-01

    Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.

  17. Modeling the interdependent network based on two-mode networks

    Science.gov (United States)

    An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

    2017-10-01

    Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

  18. Introducing Synchronisation in Deterministic Network Models

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.;

    2006-01-01

    The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading....... The suggested models are intended for incorporation into an existing analysis tool a.k.a. CyNC based on the MATLAB/SimuLink framework for graphical system analysis and design....

  19. Neural network models of protein domain evolution

    OpenAIRE

    Sylvia Nagl

    2000-01-01

    Protein domains are complex adaptive systems, and here a novel procedure is presented that models the evolution of new functional sites within stable domain folds using neural networks. Neural networks, which were originally developed in cognitive science for the modeling of brain functions, can provide a fruitful methodology for the study of complex systems in general. Ethical implications of developing complex systems models of biomolecules are discussed, with particular reference to molecu...

  20. A 3D Multiscale Modelling of Cortical Bone Structure, Using the Inverse Identification Method: Microfibril Scale Study

    CERN Document Server

    Barkaoui, Abdelwahed

    2011-01-01

    Complexity and heterogeneity of bone tissue require a multiscale modelling to understand their mechanical behaviour and their remodelling mechanism. Human cortical bone structure consists of six structural scale levels which are the (macroscopic) cortical bone, osteonal, lamellar, fibrous, fibril and microfibril. In this paper, a 3D model based on finite elements method was achieved to study the nanomechanical behaviour of collagen Microfibril. The mechanical properties and the geometry (gap, overlap and diameter) of both tropocollagen and mineral were taken into consideration as well as the effects of cross-links. An inverse identification method has been applied to determine equivalent averaged properties in order to link up these nanoscopic characteristics to the macroscopic mechanical behaviour of bone tissue. Results of nanostructure modelling of the nanomechanical properties of strain deformation under varying cross-links were investigated in this work.

  1. Homophyly/Kinship Model: Naturally Evolving Networks

    Science.gov (United States)

    Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

    2015-10-01

    It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

  2. Ion channel density and threshold dynamics of repetitive firing in a cortical neuron model.

    Science.gov (United States)

    Arhem, Peter; Blomberg, Clas

    2007-01-01

    Modifying the density and distribution of ion channels in a neuron (by natural up- and down-regulation, by pharmacological intervention or by spontaneous mutations) changes its activity pattern. In the present investigation, we analyze how the impulse patterns are regulated by the density of voltage-gated channels in a model neuron, based on voltage clamp measurements of hippocampal interneurons. At least three distinct oscillatory patterns, associated with three distinct regions in the Na-K channel density plane, were found. A stability analysis showed that the different regions are characterized by saddle-node, double-orbit, and Hopf bifurcation threshold dynamics, respectively. Single strongly graded action potentials occur in an area outside the oscillatory regions, but less graded action potentials occur together with repetitive firing over a considerable range of channel densities. The presently found relationship between channel densities and oscillatory behavior may be relevance for understanding principal spiking patterns of cortical neurons (regular firing and fast spiking). It may also be of relevance for understanding the action of pharmacological compounds on brain oscillatory activity.

  3. Spiking cortical model based non-local means method for despeckling multiframe optical coherence tomography data

    Science.gov (United States)

    Gu, Yameng; Zhang, Xuming

    2017-05-01

    Optical coherence tomography (OCT) images are severely degraded by speckle noise. Existing methods for despeckling multiframe OCT data cannot deliver sufficient speckle suppression while preserving image details well. To address this problem, the spiking cortical model (SCM) based non-local means (NLM) method has been proposed in this letter. In the proposed method, the considered frame and two neighboring frames are input into three SCMs to generate the temporal series of pulse outputs. The normalized moment of inertia (NMI) of the considered patches in the pulse outputs is extracted to represent the rotational and scaling invariant features of the corresponding patches in each frame. The pixel similarity is computed based on the Euclidean distance between the NMI features and used as the weight. Each pixel in the considered frame is restored by the weighted averaging of all pixels in the pre-defined search window in the three frames. Experiments on the real multiframe OCT data of the pig eye demonstrate the advantage of the proposed method over the frame averaging method, the multiscale sparsity based tomographic denoising method, the wavelet-based method and the traditional NLM method in terms of visual inspection and objective metrics such as signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), equivalent number of looks (ENL) and cross-correlation (XCOR).

  4. Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor.

    Science.gov (United States)

    Zhang, Xuming; Ren, Jinxia; Huang, Zhiwen; Zhu, Fei

    2016-09-15

    Multimodal medical image fusion (MIF) plays an important role in clinical diagnosis and therapy. Existing MIF methods tend to introduce artifacts, lead to loss of image details or produce low-contrast fused images. To address these problems, a novel spiking cortical model (SCM) based MIF method has been proposed in this paper. The proposed method can generate high-quality fused images using the weighting fusion strategy based on the firing times of the SCM. In the weighting fusion scheme, the weight is determined by combining the entropy information of pulse outputs of the SCM with the Weber local descriptor operating on the firing mapping images produced from the pulse outputs. The extensive experiments on multimodal medical images show that compared with the numerous state-of-the-art MIF methods, the proposed method can preserve image details very well and avoid the introduction of artifacts effectively, and thus it significantly improves the quality of fused images in terms of human vision and objective evaluation criteria such as mutual information, edge preservation index, structural similarity based metric, fusion quality index, fusion similarity metric and standard deviation.

  5. Spiking Cortical Model Based Multimodal Medical Image Fusion by Combining Entropy Information with Weber Local Descriptor

    Directory of Open Access Journals (Sweden)

    Xuming Zhang

    2016-09-01

    Full Text Available Multimodal medical image fusion (MIF plays an important role in clinical diagnosis and therapy. Existing MIF methods tend to introduce artifacts, lead to loss of image details or produce low-contrast fused images. To address these problems, a novel spiking cortical model (SCM based MIF method has been proposed in this paper. The proposed method can generate high-quality fused images using the weighting fusion strategy based on the firing times of the SCM. In the weighting fusion scheme, the weight is determined by combining the entropy information of pulse outputs of the SCM with the Weber local descriptor operating on the firing mapping images produced from the pulse outputs. The extensive experiments on multimodal medical images show that compared with the numerous state-of-the-art MIF methods, the proposed method can preserve image details very well and avoid the introduction of artifacts effectively, and thus it significantly improves the quality of fused images in terms of human vision and objective evaluation criteria such as mutual information, edge preservation index, structural similarity based metric, fusion quality index, fusion similarity metric and standard deviation.

  6. Nonlinear analysis and modeling of cortical activation and deactivation patterns in the immature fetal electrocorticogram

    Science.gov (United States)

    Schwab, Karin; Groh, Tobias; Schwab, Matthias; Witte, Herbert

    2009-03-01

    An approach combining time-continuous nonlinear stability analysis and a parametric bispectral method was introduced to better describe cortical activation and deactivation patterns in the immature fetal electroencephalogram (EEG). Signal models and data-driven investigations were performed to find optimal parameters of the nonlinear methods and to confirm the occurrence of nonlinear sections in the fetal EEG. The resulting measures were applied to the in utero electrocorticogram (ECoG) of fetal sheep at 0.7 gestation when organized sleep states were not developed and compared to previous results at 0.9 gestation. Cycling of the nonlinear stability of the fetal ECoG occurred already at this early gestational age, suggesting the presence of premature sleep states. This was accompanied by cycling of the time-variant biamplitude which reflected ECoG synchronization effects during premature sleep states associated with nonrapid eye movement sleep later in gestation. Thus, the combined nonlinear and time-variant approach was able to provide important insights into the properties of the immature fetal ECoG.

  7. Progesterone Treatment Shows Benefit in Female Rats in a Pediatric Model of Controlled Cortical Impact Injury.

    Directory of Open Access Journals (Sweden)

    Rastafa I Geddes

    Full Text Available We recently showed that progesterone treatment can reduce lesion size and behavioral deficits after moderate-to-severe bilateral injury to the medial prefrontal cortex in immature male rats. Whether there are important sex differences in response to injury and progesterone treatment in very young subjects has not been given sufficient attention. Here we investigated progesterone's effects in the same model of brain injury but with pre-pubescent females.Twenty-eight-day-old female Sprague-Dawley rats received sham (n = 14 or controlled cortical impact (CCI (n = 21 injury, were given progesterone (8 mg/kg body weight or vehicle injections on post-injury days (PID 1-7, and underwent behavioral testing from PID 9-27. Brains were evaluated for lesion size at PID 28.Lesion size in vehicle-treated female rats with CCI injury was smaller than that previously reported for similarly treated age-matched male rats. Treatment with progesterone reduced the effect of CCI on extent of damage and behavioral deficits.Pre-pubescent female rats with midline CCI injury to the frontal cortex have reduced morphological and functional deficits following progesterone treatment. While gender differences in susceptibility to this injury were observed, progesterone treatment produced beneficial effects in young rats of both sexes following CCI.

  8. Spreading effect of tDCS in individuals with attention-deficit/hyperactivity disorder as shown by functional cortical networks: a randomized, double-blind, sham-controlled trial

    Directory of Open Access Journals (Sweden)

    Camila eCosmo

    2015-08-01

    Full Text Available Transcranial direct current stimulation (tDCS is known to modulate spontaneous neural network excitability. The cognitive improvement observed in previous trials raises the potential of this technique as a possible therapeutic tool for use in attention-deficit/hyperactivity disorder (ADHD population. However, to explore the potential of this technique as a treatment approach the functional parameters of brain connectivity and the extent of its effects need to be more fully investigated.The aim of this study was to investigate a functional cortical network model based on electroencephalographic activity for studying the dynamic patterns of brain connectivity modulated by tDCS and the distribution of its effects in individuals with attention-deficit/hyperactivity disorder (ADHD.Sixty ADHD patients participated in a parallel, randomized, double-blind, sham-controlled trial. Individuals underwent a single session of sham or anodal tDCS at 1 mA of current intensity over the left dorsolateral prefrontal cortex for 20 minutes. The acute effects of stimulation on brain connectivity were assessed using the functional cortical network model based on electroencephalography (EEG activity.Comparing the weighted node degree within groups prior to and following the intervention, a statistically significant difference was found in the electrodes located on the target and correlated areas in the active group (p<0.05, while no statistically significant results were found in the sham group (p ≥0.05; paired-sample Wilcoxon signed rank test. Anodal tDCS increased functional brain connectivity in individuals with ADHD compared to data recorded in the baseline resting state. In addition, although some studies have suggested that the effects of tDCS are selective, the present findings show that its modulatory activity spreads. Further studies need to be performed to investigate the dynamic patterns and physiological mechanisms underlying the modulatory effects of tDCS.

  9. Simple cortical and thalamic neuron models for digital arithmetic circuit implementation

    Directory of Open Access Journals (Sweden)

    Takuya eNanami

    2016-05-01

    Full Text Available Trade-off between reproducibility of neuronal activities and computational efficiency is one ofcrucial subjects in computational neuroscience and neuromorphic engineering. A wide variety ofneuronal models have been studied from different viewpoints. The digital spiking silicon neuron(DSSN model is a qualitative model that focuses on efficient implementation by digital arithmeticcircuits. We expanded the DSSN model and found appropriate parameter sets with which itreproduces the dynamical behaviors of the ionic-conductance models of four classes of corticaland thalamic neurons. We first developed a 4-variable model by reducing the number of variablesin the ionic-conductance models and elucidated its mathematical structures using bifurcationanalysis. Then, expanded DSSN models were constructed that reproduce these mathematicalstructures and capture the characteristic behavior of each neuron class. We confirmed thatstatistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductancemodels. Computational cost of the DSSN model is larger than that of the recent sophisticatedIntegrate-and-Fire-based models, but smaller than the ionic-conductance models. This modelis intended to provide another meeting point for above trade-off that satisfies the demand forlarge-scale neuronal network simulation with closer-to-biology models.

  10. Simple model for river network evolution

    Energy Technology Data Exchange (ETDEWEB)

    Leheny, R.L. [The James Franck Institute and The Department of Physics, The University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637 (United States)

    1995-11-01

    We simulate the evolution of a drainage basin by erosion from precipitation and avalanching on hillslopes. The avalanches create a competition in growth between neighboring basins and play the central role in driving the evolution. The simulated landscapes form drainage systems that share many qualitative features with Glock`s model for natural network evolution and maintain statistical properties that characterize real river networks. We also present results from a second model with a modified, mass conserving avalanche scheme. Although the terrains from these two models are qualitatively dissimilar, their drainage networks share the same general evolution and statistical features.

  11. Characterization and Modeling of Network Traffic

    DEFF Research Database (Denmark)

    Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur

    2011-01-01

    This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter...

  12. Modeling the hydrodynamics in tidal networks

    NARCIS (Netherlands)

    Alebregtse, N.C.

    2016-01-01

    This thesis covers tidal propagation through networks of channels. Such networks are widespread and are often subject to discordant human and natural interests. First, the effect of a secondary channel on the tides in a main channel is explained with the use of an idealized model and the responsible

  13. Network Design Models for Container Shipping

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund

    This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...

  14. Cyber threat model for tactical radio networks

    Science.gov (United States)

    Kurdziel, Michael T.

    2014-05-01

    The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

  15. Multiscaling in an YX model of networks.

    Science.gov (United States)

    Holme, Petter; Wu, Zhi-Xi; Minnhagen, Petter

    2009-09-01

    We investigate a Hamiltonian model of networks. The model is a mirror formulation of the XY model (hence the name)--instead of letting the XY spins vary, keeping the coupling topology static, we keep the spins conserved and sample different underlying networks. Our numerical simulations show complex scaling behaviors with various exponents as the system grows and temperature approaches zero, but no finite-temperature universal critical behavior. The ground-state and low-order excitations for sparse, finite graphs are a fragmented set of isolated network clusters. Configurations of higher energy are typically more connected. The connected networks of lowest energy are stretched out giving the network large average distances. For the finite sizes we investigate, there are three regions--a low-energy regime of fragmented networks, an intermediate regime of stretched-out networks, and a high-energy regime of compact, disordered topologies. Scaling up the system size, the borders between these regimes approach zero temperature algebraically, but different network-structural quantities approach their T=0 values with different exponents. We argue this is a, perhaps rare, example of a statistical-physics model where finite sizes show a more interesting behavior than the thermodynamic limit.

  16. The relationship of three cortical regions to an information-processing model.

    Science.gov (United States)

    Anderson, John R; Qin, Yulin; Stenger, V Andrew; Carter, Cameron S

    2004-05-01

    This research tests a model of the computational role of three cortical regions in tasks like algebra equation solving. The model assumes that there is a left parietal region-of-interest (ROI) where the problem expression is represented and transformed, a left prefrontal ROI where information for solving the task is retrieved, and a motor ROI where hand movements to produce the answer are programmed. A functional magnetic resonance imaging (fMRI) study of an abstract symbol-manipulation task was performed to articulate the roles of these three regions. Participants learned to associate words with instructions for transforming strings of letters. The study manipulated the need to retrieve these instructions, the need to transform the strings, and whether there was a delay between calculation of the answer and the output of the answer. As predicted, the left parietal ROI mainly reflected the need for a transformation and the left prefrontal ROI the need for retrieval. Homologous right ROIs showed similar but weaker responses. Neither the prefrontal nor the parietal ROIs responded to delay, but the motor ROI did respond to delay, implying motor rehearsal over the delay. Except for the motor ROI, these patterns of activity did not vary with response hand. In an ACT-R model, it was shown that the activity of an imaginal buffer predicted the blood oxygen level-dependent (BOLD) response of the parietal ROI, the activity of a retrieval buffer predicted the response of the prefrontal ROI, and the activity of a manual buffer predicted the response of the motor ROI.

  17. Cortical and hippocampal correlates of deliberation during model-based decisions for rewards in humans.

    Directory of Open Access Journals (Sweden)

    Aaron M Bornstein

    Full Text Available How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward - such as when planning routes using a cognitive map or chess moves using predicted countermoves - and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to

  18. Cortical imaging on a head template: a simulation study using a resistor mesh model (RMM).

    Science.gov (United States)

    Chauveau, Nicolas; Franceries, Xavier; Aubry, Florent; Celsis, Pierre; Rigaud, Bernard

    2008-09-01

    The T1 head template model used in Statistical Parametric Mapping Version 2000 (SPM2), was segmented into five layers (scalp, skull, CSF, grey and white matter) and implemented in 2 mm voxels. We designed a resistor mesh model (RMM), based on the finite volume method (FVM) to simulate the electrical properties of this head model along the three axes for each voxel. Then, we introduced four dipoles of high eccentricity (about 0.8) in this RMM, separately and simultaneously, to compute the potentials for two sets of conductivities. We used the direct cortical imaging technique (CIT) to recover the simulated dipoles, using 60 or 107 electrodes and with or without addition of Gaussian white noise (GWN). The use of realistic conductivities gave better CIT results than standard conductivities, lowering the blurring effect on scalp potentials and displaying more accurate position areas when CIT was applied to single dipoles. Simultaneous dipoles were less accurately localized, but good qualitative and stable quantitative results were obtained up to 5% noise level for 107 electrodes and up to 10% noise level for 60 electrodes, showing that a compromise must be found to optimize both the number of electrodes and the noise level. With the RMM defined in 2 mm voxels, the standard 128-electrode cap and 5% noise appears to be the upper limit providing reliable source positions when direct CIT is used. The admittance matrix defining the RMM is easy to modify so as to adapt to different conductivities. The next step will be the adaptation of individual real head T2 images to the RMM template and the introduction of anisotropy using diffusion imaging (DI).

  19. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  20. Stochastic discrete model of karstic networks

    Science.gov (United States)

    Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.

    Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.

  1. A Model for Telestrok Network Evaluation

    DEFF Research Database (Denmark)

    Storm, Anna; Günzel, Franziska; Theiss, Stephan

    2011-01-01

    Different telestroke network concepts have been implemented worldwide to enable fast and efficient treatment of stroke patients in underserved rural areas. Networks could demonstrate the improvement in clinical outcome, but have so far excluded a cost-effectiveness analysis. With health economic...... was developed from the third-party payer perspective. In principle, it enables telestroke networks to conduct cost-effectiveness studies, because the majority of the required data can be extracted from health insurance companies’ databases and the telestroke network itself. The model presents a basis...

  2. Model-based control of networked systems

    CERN Document Server

    Garcia, Eloy; Montestruque, Luis A

    2014-01-01

    This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled.   The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control.   Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...

  3. Complex networks repair strategies: Dynamic models

    Science.gov (United States)

    Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang

    2017-09-01

    Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree and enhances network invulnerability.

  4. How the cortex gets its folds: an inside-out, connectivity-driven model for the scaling of mammalian cortical folding

    Directory of Open Access Journals (Sweden)

    Bruno eMota

    2012-02-01

    Full Text Available Larger mammalian cerebral cortices tend to have increasingly folded surfaces, often considered to result from the lateral expansion of the grey matter (GM, which, in a volume constrained by the cranium, causes mechanical compression that is relieved by inward folding of the white matter (WM, or to result from differential expansion of cortical layers. Across species, thinner cortices, presumably more pliable, would offer less resistance and hence become more folded than thicker cortices of a same size. However, such models do not acknowledge evidence in favor of a tension-based pull onto the GM from the inside, holding it in place even when the constraint imposed by the cranium is removed. Here we propose a testable, quantitative model of cortical folding driven by tension along the length of axons in the WM that assumes that connections through the WM are formed early in development, at the same time as the GM becomes folded, and considers that axonal connections through the WM generate tension that leads to inward folding of the WM surface, which pulls the GM surface inwards. Cortical folding is thus driven by WM connectivity, and is a function of the fraction of cortical neurons connected through the WM, the average length and the average cross-sectional area of the axons in the WM. Our model predicts that the different scaling of cortical folding across mammalian orders corresponds to different combinations of scaling of connectivity, axonal cross-sectional area and tension along WM axons, instead of being a simple function of the number of GM neurons. Our model also explains variations in cortical thickness as a result of the factors that lead to cortical folding, rather than as a determinant of folding; predicts that for a same tension, folding increases with connectivity through the WM and increased axonal cross-section; and that, for a same number of neurons, higher connectivity through the WM leads to a higher degree of folding as well

  5. Modeling trust context in networks

    CERN Document Server

    Adali, Sibel

    2013-01-01

    We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout

  6. Regulation of the fear network by mediators of stress: Norepinephrine alters the balance between Cortical and Subcortical afferent excitation of the Lateral Amygdala

    Directory of Open Access Journals (Sweden)

    Luke R Johnson

    2011-05-01

    Full Text Available Pavlovian auditory fear conditioning crucially involves the integration of information about and acoustic conditioned stimulus (CS and an aversive unconditioned stimulus (US in the lateral nucleus of the amygdala (LA. The auditory CS reaches the LA subcortically via a direct connection from the auditory thalamus and also from the auditory association cortex itself. How neural modulators, especially those activated during stress, such as norepinephrine (NE, regulate synaptic transmission and plasticity in this network is poorly understood. Here we show that NE inhibits synaptic transmission in both the subcortical and cortical input pathway but that sensory processing is biased towards the subcortical pathway. In addition binding of NE to β-adrenergic receptors further dissociates sensory processing in the LA. These findings suggest a network mechanism that shifts sensory balance towards the faster but more primitive subcortical input.

  7. Graphical Model Theory for Wireless Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Davis, William B.

    2002-12-08

    Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.

  8. Modelling subtle growth of linguistic networks

    CERN Document Server

    Kulig, Andrzej; Kwapien, Jaroslaw; Oswiecimka, Pawel

    2014-01-01

    We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest path length. Then, we identify grammar induced local chain-like linear growth as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.

  9. Network models of dissolution of porous media

    CERN Document Server

    Budek, Agnieszka

    2012-01-01

    We investigate the chemical dissolution of porous media using a network model in which the system is represented as a series of interconnected pipes with the diameter of each segment increasing in proportion to the local reactant consumption. Moreover, the topology of the network is allowed to change dynamically during the simulation: as the diameters of the eroding pores become comparable with the interpore distances, the pores are joined together thus changing the interconnections within the network. With this model, we investigate different growth regimes in an evolving porous medium, identifying the mechanisms responsible for the emergence of specific patterns. We consider both the random and regular network and study the effect of the network geometry on the patterns. Finally, we consider practically important problem of finding an optimum flow rate that gives a maximum increase in permeability for a given amount of reactant.

  10. A quantitative framework to evaluate modeling of cortical development by neural stem cells

    Science.gov (United States)

    Stein, Jason L.; de la Torre-Ubieta, Luis; Tian, Yuan; Parikshak, Neelroop N.; Hernandez, Israel A.; Marchetto, Maria C.; Baker, Dylan K.; Lu, Daning; Hinman, Cassidy R.; Lowe, Jennifer K.; Wexler, Eric M.; Muotri, Alysson R.; Gage, Fred H.; Kosik, Kenneth S.; Geschwind, Daniel H.

    2014-01-01

    Summary Neural stem cells have been adopted to model a wide range of neuropsychiatric conditions in vitro. However, how well such models correspond to in vivo brain has not been evaluated in an unbiased, comprehensive manner. We used transcriptomic analyses to compare in vitro systems to developing human fetal brain and observed strong conservation of in vivo gene expression and network architecture in differentiating primary human neural progenitor cells (phNPCs). Conserved modules are enriched in genes associated with ASD, supporting the utility of phNPCs for studying neuropsychiatric disease. We also developed and validated a machine learning approach called CoNTExT that identifies the developmental maturity and regional identity of in vitro models. We observed strong differences between in vitro models, including hiPSC-derived neural progenitors from multiple laboratories. This work provides a systems biology framework for evaluating in vitro systems and supports their value in studying the molecular mechanisms of human neurodevelopmental disease. PMID:24991955

  11. Modeling Network Traffic in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Sheng Ma

    2004-12-01

    Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.

  12. Functional connectivity of motor cortical network in patients with brachial plexus avulsion injury after contralateral cervical nerve transfer: a resting-state fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Aihong; Cheng, Xiaoguang; Liang, Wei; Bai, Rongjie [The 4th Medical College of Peking University, Department of Radiology, Beijing Jishuitan Hospital, Xicheng Qu, Beijing (China); Wang, Shufeng; Xue, Yunhao; Li, Wenjun [The 4th Medical College of Peking University, Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing (China)

    2017-03-15

    The purpose of this study is to assess the functional connectivity of the motor cortical network in patients with brachial plexus avulsion injury (BPAI) after contralateral C7 nerve transfer, using resting-state functional magnetic resonance imaging (RS-fMRI). Twelve patients with total brachial plexus root avulsion underwent RS-fMRI after contralateral C7 nerve transfer. Seventeen healthy volunteers were also included in this fMRI study as controls. The hand motor seed regions were defined as region of interests in the bilateral hemispheres. The seed-based functional connectivity was calculated in all the subjects. Differences in functional connectivity of the motor cortical network between patients and healthy controls were compared. The inter-hemispheric functional connectivity of the M1 areas was increased in patients with BPAI compared with the controls. The inter-hemispheric functional connectivity between the supplementary motor areas was reduced bilaterally. The resting-state inter-hemispheric functional connectivity of the bilateral M1 areas is altered in patients after contralateral C7 nerve transfer, suggesting a functional reorganization of cerebral cortex. (orig.)

  13. Spatiotemporal 16p11.2 protein network implicates cortical late mid-fetal brain development and KCTD13-Cul3-RhoA pathway in psychiatric diseases.

    Science.gov (United States)

    Lin, Guan Ning; Corominas, Roser; Lemmens, Irma; Yang, Xinping; Tavernier, Jan; Hill, David E; Vidal, Marc; Sebat, Jonathan; Iakoucheva, Lilia M

    2015-02-18

    The psychiatric disorders autism and schizophrenia have a strong genetic component, and copy number variants (CNVs) are firmly implicated. Recurrent deletions and duplications of chromosome 16p11.2 confer a high risk for both diseases, but the pathways disrupted by this CNV are poorly defined. Here we investigate the dynamics of the 16p11.2 network by integrating physical interactions of 16p11.2 proteins with spatiotemporal gene expression from the developing human brain. We observe profound changes in protein interaction networks throughout different stages of brain development and/or in different brain regions. We identify the late mid-fetal period of cortical development as most critical for establishing the connectivity of 16p11.2 proteins with their co-expressed partners. Furthermore, our results suggest that the regulation of the KCTD13-Cul3-RhoA pathway in layer 4 of the inner cortical plate is crucial for controlling brain size and connectivity and that its dysregulation by de novo mutations may be a potential determinant of 16p11.2 CNV deletion and duplication phenotypes.

  14. IP Network Management Model Based on NGOSS

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jin-yu; LI Hong-hui; LIU Feng

    2004-01-01

    This paper addresses a management model for IP network based on Next Generation Operation Support System (NGOSS). It makes the network management on the base of all the operation actions of ISP, It provides QoS to user service through the whole path by providing end-to-end Service Level Agreements (SLA) management through whole path. Based on web and coordination technology, this paper gives an implement architecture of this model.

  15. Model-driven SOA for sensor networks

    Science.gov (United States)

    Ibbotson, John; Gibson, Christopher; Geyik, Sahin; Szymanski, Boleslaw K.; Mott, David; Braines, David; Klapiscak, Tom; Bergamaschi, Flavio

    2011-06-01

    Our previous work has explored the application of enterprise middleware techniques at the edge of the network to address the challenges of delivering complex sensor network solutions over heterogeneous communications infrastructures. In this paper, we develop this approach further into a practicable, semantically rich, model-based design and analysis approach that considers the sensor network and its contained services as a service-oriented architecture. The proposed model enables a systematic approach to service composition, analysis (using domain-specific techniques), and deployment. It also enables cross intelligence domain integration to simplify intelligence gathering, allowing users to express queries in structured natural language (Controlled English).

  16. A thalamic reticular networking model of consciousness

    Directory of Open Access Journals (Sweden)

    Min Byoung-Kyong

    2010-03-01

    Full Text Available Abstract [Background] It is reasonable to consider the thalamus a primary candidate for the location of consciousness, given that the thalamus has been referred to as the gateway of nearly all sensory inputs to the corresponding cortical areas. Interestingly, in an early stage of brain development, communicative innervations between the dorsal thalamus and telencephalon must pass through the ventral thalamus, the major derivative of which is the thalamic reticular nucleus (TRN. The TRN occupies a striking control position in the brain, sending inhibitory axons back to the thalamus, roughly to the same region where they receive afferents. [Hypotheses] The present study hypothesizes that the TRN plays a pivotal role in dynamic attention by controlling thalamocortical synchronization. The TRN is thus viewed as a functional networking filter to regulate conscious perception, which is possibly embedded in thalamocortical networks. Based on the anatomical structures and connections, modality-specific sectors of the TRN and the thalamus appear to be responsible for modality-specific perceptual representation. Furthermore, the coarsely overlapped topographic maps of the TRN appear to be associated with cross-modal or unitary conscious awareness. Throughout the latticework structure of the TRN, conscious perception could be accomplished and elaborated through accumulating intercommunicative processing across the first-order input signal and the higher-order signals from its functionally associated cortices. As the higher-order relay signals run cumulatively through the relevant thalamocortical loops, conscious awareness becomes more refined and sophisticated. [Conclusions] I propose that the thalamocortical integrative communication across first- and higher-order information circuits and repeated feedback looping may account for our conscious awareness. This TRN-modulation hypothesis for conscious awareness provides a comprehensive rationale regarding

  17. A geometric network model of intrinsic grey-matter connectivity of the human brain

    Science.gov (United States)

    Lo, Yi-Ping; O'Dea, Reuben; Crofts, Jonathan J.; Han, Cheol E.; Kaiser, Marcus

    2015-10-01

    Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuroscience is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct ‘shortcuts’ through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections.

  18. River Network Modeling Beyond Discharge at Gauges

    Science.gov (United States)

    David, C. H.; Famiglietti, J. S.; Salas, F. R.; Whiteaker, T. L.; Maidment, D. R.; Tolle, K.

    2014-12-01

    Over the past two decades, the estimation of water flow in river networks within hydro-meteorological models has mostly focused on simulations of natural processes and on their verification at available river gauges. Despite valuable existing skills in hydrologic modeling the accounting for anthropogenic actions in current models remains limited. The emerging availability of datasets containing measured dam outflows and reported irrigation withdrawals motivates their inclusion into simulations of flow in river networks. However, the development of advanced river network models accounting for such datasets of anthropogenic influences requires a detailed data model and a thorough handling of the various data types, sources and time scales. This contribution details the development of a consistent data model suitable for accounting some observations of anthropogenic modifications of the surface water cycle and presents the impact of such inclusion on simulations using the Routing Application for Parallel computatIon of Discharge (RAPID).

  19. Posterior Predictive Model Checking in Bayesian Networks

    Science.gov (United States)

    Crawford, Aaron

    2014-01-01

    This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…

  20. Kitaev spin models from topological nanowire networks

    NARCIS (Netherlands)

    Kells, G.; Lahtinen, V.; Vala, J.

    2014-01-01

    We show that networks of superconducting topological nanowires can realize the physics of exactly solvable Kitaev spin models on trivalent lattices. This connection arises from the low-energy theory of both systems being described by a tight-binding model of Majorana modes. In Kitaev spin models the

  1. Fractal modeling of natural fracture networks

    Energy Technology Data Exchange (ETDEWEB)

    Ferer, M.; Dean, B.; Mick, C.

    1995-06-01

    West Virginia University will implement procedures for a fractal analysis of fractures in reservoirs. This procedure will be applied to fracture networks in outcrops and to fractures intersecting horizontal boreholes. The parameters resulting from this analysis will be used to generate synthetic fracture networks with the same fractal characteristics as the real networks. Recovery from naturally fractured, tight-gas reservoirs is controlled by the fracture network. Reliable characterization of the actual fracture network in the reservoir is severely limited. The location and orientation of fractures intersecting the borehole can be determined, but the length of these fractures cannot be unambiguously determined. Because of the lack of detailed information about the actual fracture network, modeling methods must represent the porosity and permeability associated with the fracture network, as accurately as possible with very little a priori information. In the sections following, the authors will (1) present fractal analysis of the MWX site, using the box-counting procedure; (2) review evidence testing the fractal nature of fracture distributions and discuss the advantages of using the fractal analysis over a stochastic analysis; and (3) present an efficient algorithm for producing a self-similar fracture networks which mimic the real MWX outcrop fracture network.

  2. A simple model for studying interacting networks

    Science.gov (United States)

    Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.

    2011-03-01

    Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.

  3. Automated modelling of signal transduction networks

    Directory of Open Access Journals (Sweden)

    Aach John

    2002-11-01

    Full Text Available Abstract Background Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved. Results We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles. Conclusion We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.

  4. A survey of statistical network models

    CERN Document Server

    Goldenberg, Anna; Fienberg, Stephen E; Airoldi, Edoardo M

    2009-01-01

    Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online networking communities such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry poin...

  5. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  6. Cellular mechanisms underlying spontaneous interictal spikes in an acute model of focal cortical epileptogenesis.

    Science.gov (United States)

    de Curtis, M; Radici, C; Forti, M

    1999-01-01

    The cellular mechanisms involved in the generation of spontaneous epileptiform potentials were investigated in the pirifom cortex of the in vitro isolated guinea-pig brain. A single, unilateral injection of bicuculline (150-200 nmol) in the anterior piriform cortex induced locally spontaneous interictal spikes that recurred with a period of 8.81+/-4.47 s and propagated caudally to the ipsi- and contralateral hemispheres. Simultaneous extra- and intracellular recordings from layer II and III principal cells showed that the spontaneous interictal spike correlates to a burst of action potentials followed by a large afterdepolarization. Intracellular application of the sodium conductance blocker, QX-314 (80 mM), abolished bursting activity and unmasked a high-threshold slow spike enhanced by the calcium chelator EGTA (50 mM). The slow spike was abolished by membrane hyperpolarization and by local perfusion with 2 mM cadmium. The depolarizing potential that followed the primary burst was reduced by arterial perfusion with the N-methyl-D-aspartate receptor antagonist, DL-2-amino-5-phosphonopentanoic acid (100-200 microM). The non-N-methyl-D-aspartate glutamate receptor antagonist, 6-cyano-7-nitroquinoxaline-2,3-dione (20 microM), completely and reversibly blocked the spontaneous spikes. The interictal spikes were terminated by a large afterpotential blocked either by intracellular QX-314 (80 mM) or by extracellular application of phaclofen and 2-hydroxysaclofen (10 and 4 mM, respectively). The present study demonstrates that, in an acute model of epileptogenesis, spontaneous interictal spikes are fostered by a primary burst of fast action potentials that ride on a regenerative high-threshold, possibly calcium-mediated spike, which activates a recurrent, glutamate-mediated potential responsible for the entrainment of adjacent and remote cortical regions. The bursting activity is controlled by a GABA(B) receptor-mediated inhibitory synaptic potential.

  7. Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

    Science.gov (United States)

    Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

    2017-02-01

    Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

  8. Network Modeling and Simulation (NEMSE)

    Science.gov (United States)

    2013-07-01

    Emulator (CORE) modules, hardware components, wireless cards with links modifiable using Radio Frequency (RF) cabling and variable attenuators, and GNU ...universal Network Interface Card (NIC) cards - Cardbus, PCI, or miniPCI. o GNU radio : Free & open-source software development toolkit that provides...Layer Emulator (FPLE), GNU Radio , CORE & EMANE, Tech Warrior, CASCON (CAS Connectivity), and Rate Adaptive Video Coding (RAVC). Also described is the

  9. A Model of Network Porosity

    Science.gov (United States)

    2016-11-09

    standpoint remains more of an art than a science. Even when well executed, the ongoing evolution of the network may violate initial, security-critical design... Internet and compromises the LAN (this step compromises only the LAN). We describe this as a functional information flow between the Internet and the...vulnerabilities will stem from the delivery of email, access to the Internet , or access to an internal document or data repository. If any of these are

  10. Telestroke network business model strategies.

    Science.gov (United States)

    Fanale, Christopher V; Demaerschalk, Bart M

    2012-10-01

    Our objective is to summarize the evidence that supports the reliability of telemedicine for diagnosis and efficacy in acute stroke treatment, identify strategies for funding the development of a telestroke network, and to present issues with respect to economic sustainability, cost effectiveness, and the status of reimbursement for telestroke. Copyright © 2012 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  11. Model Predictive Control of Sewer Networks

    Science.gov (United States)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.

    2017-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.

  12. Performance modeling, stochastic networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi R

    2013-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan

  13. Network Modeling and Simulation A Practical Perspective

    CERN Document Server

    Guizani, Mohsen; Khan, Bilal

    2010-01-01

    Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strate

  14. Modeling acquaintance networks based on balance theory

    Directory of Open Access Journals (Sweden)

    Vukašinović Vida

    2014-09-01

    Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models

  15. Distributed Bayesian Networks for User Modeling

    DEFF Research Database (Denmark)

    Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang

    2006-01-01

    by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...... efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....

  16. Optimal transportation networks models and theory

    CERN Document Server

    Bernot, Marc; Morel, Jean-Michel

    2009-01-01

    The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.

  17. Modeling Emergence in Neuroprotective Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.; Stevens, S.L.; Stenzel-Poore, Mary

    2013-01-05

    The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatory networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.

  18. Flood routing modelling with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R. Peters

    2006-01-01

    Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

  19. International migration network: topology and modeling.

    Science.gov (United States)

    Fagiolo, Giorgio; Mastrorillo, Marina

    2013-07-01

    This paper studies international migration from a complex-network perspective. We define the international migration network (IMN) as the weighted-directed graph where nodes are world countries and links account for the stock of migrants originated in a given country and living in another country at a given point in time. We characterize the binary and weighted architecture of the network and its evolution over time in the period 1960-2000. We find that the IMN is organized around a modular structure with a small-world binary pattern displaying disassortativity and high clustering, with power-law distributed weighted-network statistics. We also show that a parsimonious gravity model of migration can account for most of observed IMN topological structure. Overall, our results suggest that socioeconomic, geographical, and political factors are more important than local-network properties in shaping the structure of the IMN.

  20. Persistence of cortical sensory processing during absence seizures in human and an animal model: evidence from EEG and intracellular recordings.

    Directory of Open Access Journals (Sweden)

    Mathilde Chipaux

    Full Text Available Absence seizures are caused by brief periods of abnormal synchronized oscillations in the thalamocortical loops, resulting in widespread spike-and-wave discharges (SWDs in the electroencephalogram (EEG. SWDs are concomitant with a complete or partial impairment of consciousness, notably expressed by an interruption of ongoing behaviour together with a lack of conscious perception of external stimuli. It is largely considered that the paroxysmal synchronizations during the epileptic episode transiently render the thalamocortical system incapable of transmitting primary sensory information to the cortex. Here, we examined in young patients and in the Genetic Absence Epilepsy Rats from Strasbourg (GAERS, a well-established genetic model of absence epilepsy, how sensory inputs are processed in the related cortical areas during SWDs. In epileptic patients, visual event-related potentials (ERPs were still present in the occipital EEG when the stimuli were delivered during seizures, with a significant increase in amplitude compared to interictal periods and a decrease in latency compared to that measured from non-epileptic subjects. Using simultaneous in vivo EEG and intracellular recordings from the primary somatosensory cortex of GAERS and non-epileptic rats, we found that ERPs and firing responses of related pyramidal neurons to whisker deflection were not significantly modified during SWDs. However, the intracellular subthreshold synaptic responses in somatosensory cortical neurons during seizures had larger amplitude compared to quiescent situations. These convergent findings from human patients and a rodent genetic model show the persistence of cortical responses to sensory stimulations during SWDs, indicating that the brain can still process external stimuli during absence seizures. They also demonstrate that the disruption of conscious perception during absences is not due to an obliteration of information transfer in the thalamocortical system

  1. Linking physics with physiology in TMS: a sphere field model to determine the cortical stimulation site in TMS.

    Science.gov (United States)

    Thielscher, Axel; Kammer, Thomas

    2002-11-01

    A fundamental problem of transcranial magnetic stimulation (TMS) is determining the site and size of the stimulated cortical area. In the motor system, the most common procedure for this is motor mapping. The obtained two-dimensional distribution of coil positions with associated muscle responses is used to calculate a center of gravity on the skull. However, even in motor mapping the exact stimulation site on the cortex is not known and only rough estimates of its size are possible. We report a new method which combines physiological measurements with a physical model used to predict the electric field induced by the TMS coil. In four subjects motor responses in a small hand muscle were mapped with 9-13 stimulation sites at the head perpendicular to the central sulcus in order to keep the induced current direction constant in a given cortical region of interest. Input-output functions from these head locations were used to determine stimulator intensities that elicit half-maximal muscle responses. Based on these stimulator intensities the field distribution on the individual cortical surface was calculated as rendered from anatomical MR data. The region on the cortical surface in which the different stimulation sites produced the same electric field strength (minimal variance, 4.2 +/- 0.8%.) was determined as the most likely stimulation site on the cortex. In all subjects, it was located at the lateral part of the hand knob in the motor cortex. Comparisons of model calculations with the solutions obtained in this manner reveal that the stimulated cortex area innervating the target muscle is substantially smaller than the size of the electric field induced by the coil. Our results help to resolve fundamental questions raised by motor mapping studies as well as motor threshold measurements.

  2. Evolving Models of Pavlovian Conditioning: Cerebellar Cortical Dynamics in Awake Behaving Mice

    NARCIS (Netherlands)

    M. tenBrinke (MichielM.); H.J. Boele (Henk-Jan); J.K. Spanke (Jochen); J.W. Potters (Jan Willem); K. Kornysheva (Katja); P. Wulff (Peer); A.C.H.G. IJpelaar (Anna C.H.G.); S.K.E. Koekkoek (Bas); C.I. DeZeeuw (Chris)

    2015-01-01

    textabstractThree decades of electrophysiological research on cerebellar cortical activity underlying Pavlovian conditioning have expanded our understanding of motor learning in the brain. Purkinje cell simple spike suppression is considered to be crucial in the expression of conditional blink respo

  3. Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data

    DEFF Research Database (Denmark)

    Greve, Douglas N; Svarer, Claus; Fisher, Patrick M

    2014-01-01

    intersubject variance than when volume smoothing was used. This translates into more than 4 times fewer subjects needed in a group analysis to achieve similarly powered statistical tests. Surface-based smoothing has less bias and variance because it respects cortical geometry by smoothing the PET data only...

  4. Cortical region-specific engraftment of embryonic stem cell-derived neural progenitor cells restores axonal sprouting to a subcortical target and achieves motor functional recovery in a mouse model of neonatal hypoxic-ischemic brain injury

    Directory of Open Access Journals (Sweden)

    Mizuya eShinoyama

    2013-08-01

    Full Text Available Hypoxic–ischemic encephalopathy (HIE at birth could cause cerebral palsy, mental retardation, and epilepsy, which last throughout the individual’s lifetime. However, few restorative treatments for ischemic tissue are currently available. Cell replacement therapy offers the potential to rescue brain damage caused by HI and to restore motor function. In the present study, we evaluated the ability of embryonic stem cell-derived neural progenitor cells (ES-NPCs to become cortical deep layer neurons, to restore the neural network, and to repair brain damage in an HIE mouse model. ES cells stably expressing the reporter gene GFP are induced to a neural precursor state by stromal cell co-culture. Forty-hours after the induction of HIE, animals were grafted with ES-NPCs targeting the deep layer of the motor cortex in the ischemic brain. Motor function was evaluated 3 weeks after transplantation. Immunohistochemistry and neuroanatomical tracing with GFP were used to analyze neuronal differentiation and axonal sprouting. ES-NPCs could differentiate to cortical neurons with pyramidal morphology and expressed the deep layer-specific marker, Ctip2. The graft showed good survival and an appropriate innervation pattern via axonal sprouting from engrafted cells in the ischemic brain. The motor functions of the transplanted HIE mice also improved significantly compared to the sham-transplanted group. These findings suggest that cortical region specific engraftment of preconditioned cortical precursor cells could support motor functional recovery in the HIE model. It is not clear whether this is a direct effect of the engrafted cells or due to neurotrophic factors produced by these cells. These results suggest that cortical region-specific NPC engraftment is a promising therapeutic approach for brain repair.

  5. Cortical region-specific engraftment of embryonic stem cell-derived neural progenitor cells restores axonal sprouting to a subcortical target and achieves motor functional recovery in a mouse model of neonatal hypoxic-ischemic brain injury.

    Science.gov (United States)

    Shinoyama, Mizuya; Ideguchi, Makoto; Kida, Hiroyuki; Kajiwara, Koji; Kagawa, Yoshiteru; Maeda, Yoshihiko; Nomura, Sadahiro; Suzuki, Michiyasu

    2013-01-01

    Hypoxic-ischemic encephalopathy (HIE) at birth could cause cerebral palsy (CP), mental retardation, and epilepsy, which last throughout the individual's lifetime. However, few restorative treatments for ischemic tissue are currently available. Cell replacement therapy offers the potential to rescue brain damage caused by HI and to restore motor function. In the present study, we evaluated the ability of embryonic stem cell-derived neural progenitor cells (ES-NPCs) to become cortical deep layer neurons, to restore the neural network, and to repair brain damage in an HIE mouse model. ES cells stably expressing the reporter gene GFP are induced to a neural precursor state by stromal cell co-culture. Forty-hours after the induction of HIE, animals were grafted with ES-NPCs targeting the deep layer of the motor cortex in the ischemic brain. Motor function was evaluated 3 weeks after transplantation. Immunohistochemistry and neuroanatomical tracing with GFP were used to analyze neuronal differentiation and axonal sprouting. ES-NPCs could differentiate to cortical neurons with pyramidal morphology and expressed the deep layer-specific marker, Ctip2. The graft showed good survival and an appropriate innervation pattern via axonal sprouting from engrafted cells in the ischemic brain. The motor functions of the transplanted HIE mice also improved significantly compared to the sham-transplanted group. These findings suggest that cortical region specific engraftment of preconditioned cortical precursor cells could support motor functional recovery in the HIE model. It is not clear whether this is a direct effect of the engrafted cells or due to neurotrophic factors produced by these cells. These results suggest that cortical region-specific NPC engraftment is a promising therapeutic approach for brain repair.

  6. Modeling, Optimization & Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat

    2014-01-01

    to check if the network is controllable. Afterward the pressure control problem in water supply systems is formulated as an optimal control problem. The goal is to minimize the power consumption in pumps and also to regulate the pressure drop at the end-users to a desired value. The formulated optimal...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...

  7. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    . Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...

  8. Enhanced Gravity Model of trade: reconciling macroeconomic and network models

    CERN Document Server

    Almog, Assaf; Garlaschelli, Diego

    2015-01-01

    The bilateral trade relations between world countries form a complex network, the International Trade Network (ITN), which is involved in an increasing number of worldwide economic processes, including globalization, integration, industrial production, and the propagation of shocks and instabilities. Characterizing the ITN via a simple yet accurate model is an open problem. The classical Gravity Model of trade successfully reproduces the volume of trade between two connected countries using known macroeconomic properties such as GDP and geographic distance. However, it generates a network with an unrealistically homogeneous topology, thus failing to reproduce the highly heterogeneous structure of the real ITN. On the other hand, network models successfully reproduce the complex topology of the ITN, but provide no information about trade volumes. Therefore macroeconomic and network models of trade suffer from complementary limitations but are still largely incompatible. Here, we make an important step forward ...

  9. Hybrid neural network models of transducers

    Science.gov (United States)

    Xie, Shilin; Zhang, Xinong; Chen, Shenglai; Zhu, Changchun

    2011-10-01

    A hybrid neural network (NN) approach is proposed and applied to modeling of transducers in the paper. The modeling procedures are also presented in detail. First, the simulated studies on the modeling of single input-single output and multi input-multi output transducers are conducted respectively by use of the developed hybrid NN scheme. Secondly, the hybrid NN modeling approach is utilized to characterize a six-axis force sensor prototype based on the measured data. The results show that the hybrid NN approach can significantly improve modeling precision in comparison with the conventional modeling method. In addition, the method is superior to NN black-box modeling because the former possesses smaller network scale, higher convergence speed, higher model precision and better generalization performance.

  10. Preferential urn model and nongrowing complex networks.

    Science.gov (United States)

    Ohkubo, Jun; Yasuda, Muneki; Tanaka, Kazuyuki

    2005-12-01

    A preferential urn model, which is based on the concept "the rich get richer," is proposed. From a relationship between a nongrowing model for complex networks and the preferential urn model in regard to degree distributions, it is revealed that a fitness parameter in the nongrowing model is interpreted as an inverse local temperature in the preferential urn model. Furthermore, it is clarified that the preferential urn model with randomness generates a fat-tailed occupation distribution; the concept of the local temperature enables us to understand the fat-tailed occupation distribution intuitively. Since the preferential urn model is a simple stochastic model, it can be applied to research on not only the nongrowing complex networks, but also many other fields such as econophysics and social sciences.

  11. A Network Model of Credit Risk Contagion

    Directory of Open Access Journals (Sweden)

    Ting-Qiang Chen

    2012-01-01

    Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.

  12. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...

  13. Grid architecture model of network centric warfare

    Institute of Scientific and Technical Information of China (English)

    Yan Tihua; Wang Baoshu

    2006-01-01

    NCW(network centric warfare) is an information warfare concentrating on network. A global network-centric warfare architecture with OGSA grid technology is put forward, which is a four levels system including the user level, the application level, the grid middleware layer and the resource level. In grid middleware layer, based on virtual hosting environment, a BEPL4WS grid service composition method is introduced. In addition, the NCW grid service model is built with the help of Eclipse-SDK-3.0.1 and Bpws4j.

  14. Decomposed Implicit Models of Piecewise - Linear Networks

    Directory of Open Access Journals (Sweden)

    J. Brzobohaty

    1992-05-01

    Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.

  15. Stochastic modeling and analysis of telecoms networks

    CERN Document Server

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  16. Model Reduction for Complex Hyperbolic Networks

    OpenAIRE

    Himpe, Christian; Ohlberger, Mario

    2013-01-01

    We recently introduced the joint gramian for combined state and parameter reduction [C. Himpe and M. Ohlberger. Cross-Gramian Based Combined State and Parameter Reduction for Large-Scale Control Systems. arXiv:1302.0634, 2013], which is applied in this work to reduce a parametrized linear time-varying control system modeling a hyperbolic network. The reduction encompasses the dimension of nodes and parameters of the underlying control system. Networks with a hyperbolic structure have many app...

  17. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo

    2017-04-10

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.

  18. Network Design Models for Container Shipping

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund

    This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...... is included in the calculation of the capacity and that a inhomogeneous fleet is modeled. The model also includes the cost of transshipment which is one of the major cost for the shipping companies. The concept of pseudo simple routes is introduced to expand the set of feasible routes. The linearization...

  19. Implementation of a network model of hysteresis

    Energy Technology Data Exchange (ETDEWEB)

    Gruosso, G. [Dipartimento Elettronica e Informazione, Politecnico di Milano, P.za Leonardo da Vinci 32, I-20133 Milan (Italy); Repetto, M. [Dipartimento Ingegneria Elettrica, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Turin (Italy)]. E-mail: maurizio.repetto@polito.it

    2006-02-01

    A network model of hysteresis based on elementary cells made up with piece-wise linear resistors and a linear capacitor has been presented in the literature and its theoretical properties have been investigated. This model allows to simulate hysteresis in a circuit solver without requiring any modification to its source code. Despite its appealing features, some cautions must be used for the treatment of the interface between the model and the rest of the circuit and for the handling of nonlinear resistors which can introduce some convergence problems in the network solution. These topics are investigated and some results on a simple test case are presented and discussed.

  20. Motor Cortex and Motor Cortical Interhemispheric Communication in Walking After Stroke: The Roles of Transcranial Magnetic Stimulation and Animal Models in Our Current and Future Understanding.

    Science.gov (United States)

    Charalambous, Charalambos C; Bowden, Mark G; Adkins, DeAnna L

    2016-01-01

    Despite the plethora of human neurophysiological research, the bilateral involvement of the leg motor cortical areas and their interhemispheric interaction during both normal and impaired human walking is poorly understood. Using transcranial magnetic stimulation (TMS), we have expanded our understanding of the role upper-extremity motor cortical areas play in normal movements and how stroke alters this role, and probed the efficacy of interventions to improve post-stroke arm function. However, similar investigations of the legs have lagged behind, in part, due to the anatomical difficulty in using TMS to stimulate the leg motor cortical areas. Additionally, leg movements are predominately bilaterally controlled and require interlimb coordination that may involve both hemispheres. The sensitive, but invasive, tools used in animal models of locomotion hold great potential for increasing our understanding of the bihemispheric motor cortical control of walking. In this review, we discuss 3 themes associated with the bihemispheric motor cortical control of walking after stroke: (a) what is known about the role of the bihemispheric motor cortical control in healthy and poststroke leg movements, (b) how the neural remodeling of the contralesional hemisphere can affect walking recovery after a stroke, and (c) what is the effect of behavioral rehabilitation training of walking on the neural remodeling of the motor cortical areas bilaterally. For each theme, we discuss how rodent models can enhance the present knowledge on human walking by testing hypotheses that cannot be investigated in humans, and how these findings can then be back-translated into the neurorehabilitation of poststroke walking.

  1. Non-Invasive Brain Stimulation to Enhance Upper Limb Motor Practice Poststroke: A Model for Selection of Cortical Site

    Directory of Open Access Journals (Sweden)

    Michelle L. Harris-Love

    2017-05-01

    Full Text Available Motor practice is an essential part of upper limb motor recovery following stroke. To be effective, it must be intensive with a high number of repetitions. Despite the time and effort required, gains made from practice alone are often relatively limited, and substantial residual impairment remains. Using non-invasive brain stimulation to modulate cortical excitability prior to practice could enhance the effects of practice and provide greater returns on the investment of time and effort. However, determining which cortical area to target is not trivial. The implications of relevant conceptual frameworks such as Interhemispheric Competition and Bimodal Balance Recovery are discussed. In addition, we introduce the STAC (Structural reserve, Task Attributes, Connectivity framework, which incorporates patient-, site-, and task-specific factors. An example is provided of how this framework can assist in selecting a cortical region to target for priming prior to reaching practice poststroke. We suggest that this expanded patient-, site-, and task-specific approach provides a useful model for guiding the development of more successful approaches to neuromodulation for enhancing motor recovery after stroke.

  2. Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function.

    Science.gov (United States)

    Spratling, M W

    2012-01-01

    A method is presented for learning the reciprocal feedforward and feedback connections required by the predictive coding model of cortical function. When this method is used, feedforward and feedback connections are learned simultaneously and independently in a biologically plausible manner. The performance of the proposed algorithm is evaluated by applying it to learning the elementary components of artificial and natural images. For artificial images, the bars problem is employed, and the proposed algorithm is shown to produce state-of-the-art performance on this task. For natural images, components resembling Gabor functions are learned in the first processing stage, and neurons responsive to corners are learned in the second processing stage. The properties of these learned representations are in good agreement with neurophysiological data from V1 and V2. The proposed algorithm demonstrates for the first time that a single computational theory can explain the formation of cortical RFs and also the response properties of cortical neurons once those RFs have been learned.

  3. Evaluation of EOR Processes Using Network Models

    DEFF Research Database (Denmark)

    Larsen, Jens Kjell; Krogsbøll, Anette

    1998-01-01

    The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels)...

  4. Spectral stability of unitary network models

    Science.gov (United States)

    Asch, Joachim; Bourget, Olivier; Joye, Alain

    2015-08-01

    We review various unitary network models used in quantum computing, spectral analysis or condensed matter physics and establish relationships between them. We show that symmetric one-dimensional quantum walks are universal, as are CMV matrices. We prove spectral stability and propagation properties for general asymptotically uniform models by means of unitary Mourre theory.

  5. Modelling cooperative agents in infrastructure networks

    NARCIS (Netherlands)

    Ligtvoet, A.; Chappin, E.J.L.; Stikkelman, R.M.

    2010-01-01

    This paper describes the translation of concepts of cooperation into an agent-based model of an industrial network. It first addresses the concept of cooperation and how this could be captured as heuristical rules within agents. Then it describes tests using these heuristics in an abstract model of

  6. Persistence and storage of activity patterns in spiking recurrent cortical networks:Modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

    Directory of Open Access Journals (Sweden)

    Jesse ePalma

    2012-06-01

    Full Text Available Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM. Theorems in the 1970’s showed how feedback signal function