A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.

Phase-disorder-induced firing activity in excitable neuronal networks with attractive and repulsive coupling

It has been revealed that the network of excitable neurons via attractive coupling can generate spikes under stimuli of subthreshold signals with disordered phases. In this paper, we explore the firing activity induced by phase disorder in excitable neuronal networks consisting of both attractive and repulsive coupling. By increasing the fraction of repulsive coupling, we find that, in the weak coupling strength case, the firing threshold of phase disorder is increased and the system response to subthreshold signals is decreased, indicating that the effect of inducing neuron firing by phase disorder is weakened with repulsive coupling. Interestingly, in the large coupling strength case, we see an opposite situation, where the coupled neurons show a rather large response to the subthreshold signals even with small phase disorder. The latter case implies that the effect of phase disorder is enhanced by repulsive coupling. A system of two-coupled excitable neurons is used to explain the role of repulsive coupling on phase-disorder-induced firing activity.

The influence of connectivity on the firing rate in a neuronal network with electrical and chemical synapses

We study the firing rate properties of a cellular automaton model for a neuronal network with chemical synapses. We propose a simple mechanism in which the nonlocal connections are included, through electrical and chemical synapses. In the latter case, we introduce a time delay which produces self-sustained activity. Nonlocal connections, or shortcuts, are randomly introduced according to a specified connection probability. There is a range of connection probabilities for which neuron firing occurs, as well as a critical probability for which the firing ceases in the absence of time delay. The critical probability for nonlocal shortcuts depends on the network size according to a power-law. We also compute the firing rate amplification factor by varying both the connection probability and the time delay for different network sizes. (C) 2011 Elsevier B.V. All rights reserved.

Schizophrenia, brain disease and meta-analyses: Integrating the pieces and testing Fusar-Poli`s hypothesis

This paper aims to discuss and test the hypothesis raised by Fusar-Poli [Fusar-Poli P. Can neuroimaging prove that schizophrenia is a brain disease? A radical hypothesis. Medical Hypotheses in press, corrected proof] that ""on the basis of the available imaging literature there is no consistent evidence to reject the radical and provocative hypothesis that schizophrenia is not a brain disease"". To achieve this goal, all meta-analyses on `fMRI and schizophrenia` published during the current decade and indexed in Pubmed were summarized, as much as some other useful information, e.g., meta-analyses on genetic risk factors. Our main conclusion is that the literature fully supports the hypothesis that schizophrenia is a syndrome (not a disease) associated with brain abnormalities, despite the fact that there is no singular and reductionist pathway from the nosographic entity (schizophrenia) to its causes. This irreducibility is due to the fact that the syndrome has more than one dimension (e.g., cognitive, psychotic and negative) and each of them is related to abnormalities in specific neuronal networks. A psychiatric diagnosis is a statistical procedure; these dimensions are not identically represented in each diagnosticated case and this explains the existence of more than one pattern of brain abnormalities related to schizophrenia. For example, chronification is associated with negativism while the first psychotic episode is not; in that sense, the same person living with schizophrenia may reveal different symptoms and fMRI patterns along the course of his life, and this is precisely what defines schizophrenia since the time when it was called Dementia Praecox (first by pick then by Kraepelin). It is notable that 100% of the collected meta-analyses on `fMRI and schizophrenia` reveal positive findings. Moreover, all meta-analyses that found positive associations between schizophrenia and genetic risk factors have to do with genes (SNPs) especially activated in neuronal tissue of the central nervous system (CNS), suggesting that, to the extent these polymorphisms are related to schizophrenia`s etiology, they are also related to abnormal brain activity. (C) 2009 Elsevier Ltd. All rights reserved.

Morphological Homogeneity of Neurons: Searching for Outlier Neuronal Cells

We report a morphology-based approach for the automatic identification of outlier neurons, as well as its application to the NeuroMorpho.org database, with more than 5,000 neurons. Each neuron in a given analysis is represented by a feature vector composed of 20 measurements, which are then projected into a two-dimensional space by applying principal component analysis. Bivariate kernel density estimation is then used to obtain the probability distribution for the group of cells, so that the cells with highest probabilities are understood as archetypes while those with the smallest probabilities are classified as outliers. The potential of the methodology is illustrated in several cases involving uniform cell types as well as cell types for specific animal species. The results provide insights regarding the distribution of cells, yielding single and multi-variate clusters, and they suggest that outlier cells tend to be more planar and tortuous. The proposed methodology can be used in several situations involving one or more categories of cells, as well as for detection of new categories and possible artifacts.

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

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.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.