Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.     

Topographic Hub Maps of the Human Structural Neocortical Network

Hubs within the neocortical structural network determined by graph theoretical analysis play a crucial role in brain function. We mapped neocortical hubs topographically, using a sample population of 63 young adults. Subjects were imaged with high resolution structural and diffusion weighted magnetic resonance imaging techniques. Multiple network configurations were then constructed per subject, using random parcellations to define the nodes and using fibre tractography to determine the connectivity between the nodes. The networks were analysed with graph theoretical measures. Our results give reference maps of hub distribution measured with betweenness centrality and node degree. The loci of the hubs correspond with key areas from known overlapping cognitive networks. Several hubs were asymmetrically organized across hemispheres. Furthermore, females have hubs with higher betweenness centrality and males have hubs with higher node degree. Female networks have higher small-world indices.     

Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.     

Undirected graphs of frequency-dependent functional connectivity in whole brain networks

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information phi, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.     

Designing connected marine reserves in the face of global warming

Marine reserves are widely used to protect species important for conservation and fisheries and to help maintain ecological processes that sustain their populations, including recruitment and dispersal. Achieving these goals requires well‐connected networks of marine reserves that maximize larval connectivity, thus allowing exchanges between populations and recolonization after local disturbances. However, global warming can disrupt connectivity by shortening potential dispersal pathways through changes in larval physiology. These changes can compromise the performance of marine reserve networks, thus requiring adjusting their design to account for ocean warming. To date, empirical approaches to marine prioritization have not considered larval connectivity as affected by global warming. Here, we develop a framework for designing marine reserve networks that integrates graph theory and changes in larval connectivity due to potential reductions in planktonic larval duration (PLD) associated with ocean warming, given current socioeconomic constraints. Using the Gulf of California as case study, we assess the benefits and costs of adjusting networks to account for connectivity, with and without ocean warming. We compare reserve networks designed to achieve representation of species and ecosystems with networks designed to also maximize connectivity under current and future ocean‐warming scenarios. Our results indicate that current larval connectivity could be reduced significantly under ocean warming because of shortened PLDs. Given the potential changes in connectivity, we show that our graph‐theoretical approach based on centrality (eigenvector and distance‐weighted fragmentation) of habitat patches can help design better‐connected marine reserve networks for the future with equivalent costs. We found that maintaining dispersal connectivity incidentally through representation‐only reserve design is unlikely, particularly in regions with strong asymmetric patterns of dispersal connectivity. Our results support previous studies suggesting that, given potential reductions in PLD due to ocean warming, future marine reserve networks would require more and/or larger reserves in closer proximity to maintain larval connectivity.     

Modulation of Functional EEG Networks by the NMDA Antagonist Nitrous Oxide

Parietal networks are hypothesised to play a central role in the cortical information synthesis that supports conscious experience and behavior. Significant reductions in parietal level functional connectivity have been shown to occur during general anesthesia with propofol and a range of other GABAergic general anesthetic agents. Using two analysis approaches (1) a graph theoretic analysis based on surrogate-corrected zero-lag correlations of scalp EEG, and (2) a global coherence analysis based on the EEG cross-spectrum, we reveal that sedation with the NMDA receptor antagonist nitrous oxide (N₂O), an agent that has quite different electroencephalographic effects compared to the inductive general anesthetics, also causes significant alterations in parietal level functional networks, as well as changes in full brain and frontal level networks. A total of 20 subjects underwent N₂O inhalation at either 20%, 40% or 60% peak N₂O/O₂ gas concentration levels. N₂O-induced reductions in parietal network level functional connectivity (on the order of 50%) were exclusively detected by utilising a surface Laplacian derivation, suggesting that superficial, smaller spatial scale, cortical networks were most affected. In contrast reductions in frontal network functional connectivity were optimally discriminated using a common-reference derivation (reductions on the order of 10%), indicating that the NMDA antagonist N₂O induces spatially coherent and widespread perturbations in frontal activity. Our findings not only give important weight to the idea of agent invariant final network changes underlying drug-induced reductions in consciousness, but also provide significant impetus for the application and development of multiscale functional analyses to systematically characterise the network level cortical effects of NMDA receptor related hypofunction. Future work at the source space level will be needed to verify the consistency between cortical network changes seen at the source level and those presented here at the EEG sensor space level.     

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

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 and economical connectivity in larger brains.     

Graph models of habitat mosaics

Graph theory is a body of mathematics dealing with problems of connectivity, flow, and routing in networks ranging from social groups to computer networks. Recently, network applications have erupted in many fields, and graph models are now being applied in landscape ecology and conservation biology, particularly for applications couched in metapopulation theory. In these applications, graph nodes represent habitat patches or local populations and links indicate functional connections among populations (i.e. via dispersal). Graphs are models of more complicated real systems, and so it is appropriate to review these applications from the perspective of modelling in general. Here we review recent applications of network theory to habitat patches in landscape mosaics. We consider (1) the conceptual model underlying these applications; (2) formalization and implementation of the graph model; (3) model parameterization; (4) model testing, insights, and predictions available through graph analyses; and (5) potential implications for conservation biology and related applications. In general, and for a variety of ecological systems, we find the graph model a remarkably robust framework for applications concerned with habitat connectivity. We close with suggestions for further work on the parameterization and validation of graph models, and point to some promising analytic insights.     

A Neural Population Model Incorporating Dopaminergic Neurotransmission during Complex Voluntary Behaviors

Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number of extensions of the proposed methodology building upon the current model.     

Network analysis to assess landscape connectivity trends: Application to European forests (1990–2000)

Landscape networks and ecosystems worldwide are undergoing changes that may impact in different ways relevant ecological processes such as gene flow, pollination, or wildlife dispersal. A myriad of indices have been developed to characterize landscape patterns, but not all of them are equally suited to evaluate temporal changes in landscape connectivity as is increasingly needed for biodiversity monitoring and operational indicator delivery. Relevant advancements in this direction have been recently proposed based on graph theoretical methods to analyze landscape network connectivity and on the measurement of habitat availability at the landscape scale. Building from these developments, we modify a recent index and present the equivalent connected area (ECA) index, defined as the size of a single patch (maximally connected) that would provide the same probability of connectivity than the actual habitat pattern in the landscape. The temporal changes in ECA can be directly compared with the changes in total habitat area. This allows for additional and straightforward insights on the degree to which the gains or losses in habitat amount can be beneficial or deleterious by affecting landscape elements that uphold connectivity in a wider landscape context. We provide a demonstrative example of application and interpretation of this index and approach to monitor changes in functional landscape connectivity. We focus on the trends in European forests at the province level in the period 1990–2000 from Corine land cover data, considering both changes in the forest spatial pattern and in the average permeability of the landscape matrix. The degree of connectivity was rather stable over most of the study area, with a slight overall increase in forest connectivity in Europe. However, a few countries and regions concentrated remarkably high changes in the analyzed period, particularly those with a low forest cover. The species traits also affected the responses to landscape pattern changes, which were more prominent for those species with limited dispersal abilities. We conclude discussing the potential of this approach for consistent indicator delivery, as well as the limitations and possibilities of application to a variety of situations, for which the required quantitative tools are freely available as open source projects.     

The Effect of Slow Electrical Stimuli to Achieve Learning in Cultured Networks of Rat Cortical Neurons

Learning, or more generally, plasticity may be studied using cultured networks of rat cortical neurons on multi electrode arrays. Several protocols have been proposed to affect connectivity in such networks. One of these protocols, proposed by Shahaf and Marom, aimed to train the input-output relationship of a selected connection in a network using slow electrical stimuli. Although the results were quite promising, the experiments appeared difficult to repeat and the training protocol did not serve as a basis for wider investigation yet. Here, we repeated their protocol, and compared our ‘learning curves’ to the original results. Although in some experiments the protocol did not seem to work, we found that on average, the protocol showed a significantly improved stimulus response indeed. Furthermore, the protocol always induced functional connectivity changes that were much larger than changes that occurred after a comparable period of random or no stimulation. Finally, our data shows that stimulation at a fixed electrode induces functional connectivity changes of similar magnitude as stimulation through randomly varied sites; both larger than spontaneous connectivity fluctuations. We concluded that slow electrical stimulation always induced functional connectivity changes, although uncontrolled. The magnitude of change increased when we applied the adaptive (closed-loop) training protocol. We hypothesize that networks develop an equilibrium between connectivity and activity. Induced connectivity changes depend on the combination of applied stimulus and initial connectivity. Plain stimuli may drive networks to the nearest equilibrium that accommodates this input, whereas adaptive stimulation may direct the space for exploration and force networks to a new balance, at a larger distance from the initial state.     

Why everything is connected to everything else

In Earth surface systems (ESS), everything is connected to everything else, an aphorism often called the First Law of Ecology and of geography. Such linkages are not always direct and unmediated, but many ESS, represented as networks of interacting components, attain or approach full, direct connectivity among components. The question is how and why this happens at the system or network scale. The crowded landscape concept dictates that linkages and connections among ESS components are inevitable. The connection selection concept holds that the linkages among components are (often) advantageous to the network and are selected for, and thereby preserved and enhanced. These network advantages are illustrated via algebraic graph theory. For a given number of components in an ESS, as the number of links or connections increases, spectral radius, graph energy, and algebraic connectivity increase. While the advantages (if any) of increased complexity are unclear, higher spectral radii are directly correlated with higher graph energy. The greater graph energy is associated with more intense feedback in the system, and tighter coupling among components. This in turn reflects advantageous properties of more intense cycling of water, nutrients, and minerals, as well as multiple potential degrees of freedom for individual components to respond to changes. The increase of algebraic connectivity reflects a greater ability or tendency for the network to respond to changes in concert.     

A network diffusion model of disease progression in dementia

Patterns of dementia are known to fall into dissociated but dispersed brain networks, suggesting that the disease is transmitted along neuronal pathways rather than by proximity. This view is supported by neuropathological evidence for "prion-like" transsynaptic transmission of disease agents like misfolded tau and beta amyloid. We mathematically model this transmission by a diffusive mechanism mediated by?the brain's connectivity network obtained from tractography of 14 healthy-brain MRIs. Subsequent graph theoretic analysis provides a fully quantitative, testable, predictive model of dementia. Specifically, we predict spatially distinct "persistent modes," which, we found, recapitulate known patterns of dementia and match recent reports of selectively vulnerable dissociated brain networks. Model predictions also closely match T1-weighted MRI volumetrics of 18 Alzheimer's and 18 frontotemporal dementia subjects. Prevalence rates predicted by the model strongly agree with published data. This work has many important implications, including dimensionality reduction, differential diagnosis, and especially prediction of future atrophy using baseline MRI morphometrics.     

Intrinsic Functional Brain Architecture Derived from Graph Theoretical Analysis in the Human Fetus

The human brain undergoes dramatic maturational changes during late stages of fetal and early postnatal life. The importance of this period to the establishment of healthy neural connectivity is apparent in the high incidence of neural injury in preterm infants, in whom untimely exposure to ex-uterine factors interrupts neural connectivity. Though the relevance of this period to human neuroscience is apparent, little is known about functional neural networks in human fetal life. Here, we apply graph theoretical analysis to examine human fetal brain connectivity. Utilizing resting state functional magnetic resonance imaging (fMRI) data from 33 healthy human fetuses, 19 to 39 weeks gestational age (GA), our analyses reveal that the human fetal brain has modular organization and modules overlap functional systems observed postnatally. Age-related differences between younger (GA <31 weeks) and older (GA≥31 weeks) fetuses demonstrate that brain modularity decreases, and connectivity of the posterior cingulate to other brain networks becomes more negative, with advancing GA. By mimicking functional principles observed postnatally, these results support early emerging capacity for information processing in the human fetal brain. Current technical limitations, as well as the potential for fetal fMRI to one day produce major discoveries about fetal origins or antecedents of neural injury or disease are discussed.     

Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph

MOTIVATION: Metabolic networks are organized in a modular, hierarchical manner. Methods for a rational decomposition of the metabolic network into relatively independent functional subsets are essential to better understand the modularity and organization principle of a large-scale, genome-wide network. Network decomposition is also necessary for functional analysis of metabolism by pathway analysis methods that are often hampered by the problem of combinatorial explosion due to the complexity of metabolic network. Decomposition methods proposed in literature are mainly based on the connection degree of metabolites. To obtain a more reasonable decomposition, the global connectivity structure of metabolic networks should be taken into account. RESULTS: In this work, we use a reaction graph representation of a metabolic network for the identification of its global connectivity structure and for decomposition. A bow-tie connectivity structure similar to that previously discovered for metabolite graph is found also to exist in the reaction graph. Based on this bow-tie structure, a new decomposition method is proposed, which uses a distance definition derived from the path length between two reactions. An hierarchical classification tree is first constructed from the distance matrix among the reactions in the giant strong component of the bow-tie structure. These reactions are then grouped into different subsets based on the hierarchical tree. Reactions in the IN and OUT subsets of the bow-tie structure are subsequently placed in the corresponding subsets according to a 'majority rule'. Compared with the decomposition methods proposed in literature, ours is based on combined properties of the global network structure and local reaction connectivity rather than, primarily, on the connection degree of metabolites. The method is applied to decompose the metabolic network of Escherichia coli. Eleven subsets are obtained. More detailed investigations of the subsets show that reactions in the same subset are really functionally related. The rational decomposition of metabolic networks, and subsequent studies of the subsets, make it more amenable to understand the inherent organization and functionality of metabolic networks at the modular level. SUPPLEMENTARY INFORMATION: http://genome.gbf.de/bioinformatics/     

Abnormal reorganization of functional cortical small-world networks in focal hand dystonia

We investigated the large-scale functional cortical connectivity network in focal hand dystonia (FHD) patients using graph theoretic measures to assess efficiency. High-resolution EEGs were recorded in 15 FHD patients and 15 healthy volunteers at rest and during a simple sequential finger tapping task. Mutual information (MI) values of wavelet coefficients were estimated to create an association matrix between EEG electrodes, and to produce a series of adjacency matrices or graphs, G, by thresholding with network cost. Efficiency measures of small-world networks were assessed. As a result, we found that FHD patients have economical small-world properties in their brain functional networks in the alpha and beta bands. During a motor task, in the beta band network, FHD patients have decreased efficiency of small-world networks, whereas healthy volunteers increase efficiency. Reduced efficient beta band network in FHD patients during the task was consistently observed in global efficiency, cost-efficiency, and maximum cost-efficiency. This suggests that the beta band functional cortical network of FHD patients is reorganized even during a task that does not induce dystonic symptoms, representing a loss of long-range communication and abnormal functional integration in large-scale brain functional cortical networks. Moreover, negative correlations between efficiency measures and duration of disease were found, indicating that the longer duration of disease, the less efficient the beta band network in FHD patients. In regional efficiency analysis, FHD patients at rest have high regional efficiency at supplementary motor cortex (SMA) compared with healthy volunteers; however, it is diminished during the motor task, possibly reflecting abnormal inhibition in FHD patients. The present study provides the first evidence with graph theory for abnormal reconfiguration of brain functional networks in FHD during motor task.     

Landscape metrics as indicators: Quantifying habitat network changes of a bush-cricket Pholidoptera transsylvanica in Hungary

Fragmentation of habitats is a serious problem for many endangered species; a possible solution is the maintenance of landscape connectivity. Due to scarce sources, in managing and planning landscapes exact and quantitative priorities must be set. Application of mathematical tools, such as network analysis, can be useful help in these decisions. We illustrate the possibilities and results of this approach with a case study of endangered Pholidoptera transsylvanica bush-cricket population in the Aggtelek Karst, Northeast-Hungary, which inhabits 39 habitat patches connected with ecological corridors. A key issue in the long-term survival of this metapopulation is the maintenance of gene flow (by preserving the connectivity of the habitat network). We evaluated the landscape graph and our results are compared to earlier ones based on older methods. During the comparison, we used several network indices to set quantitative conservation preferences. In addition, we would like to draw attention to the need for constant monitoring (and possible treatments), because several changes (like secondary succession) have occurred during the years between the two studies, threatening landscape connectivity and long-term survival of certain species. A potential solution for preventing fragmentation is establishing new corridors or improving the existing ones: we estimated the possible effects of these changes. New corridors did not have major effect on the system; maintaining already functioning corridors is more effective.     

Sex‐specific graphs: Relating group‐specific topology to demographic and landscape data

Sex‐specific genetic structure is a commonly observed pattern among vertebrate species. Facing differential selective pressures, individuals may adopt sex‐specific life history traits that ultimately shape genetic variation among populations. Although differential dispersal dynamics are commonly detected in the literature, few studies have used genetic structure to investigate sex‐specific functional connectivity. The recent use of graph theoretic approaches in landscape genetics has demonstrated network capacities to describe complex system behaviours where network topology represents genetic interaction among subunits. Here, we partition the overall genetic structure into sex‐specific graphs, revealing different male and female dispersal dynamics of a fisher (Pekania [Martes] pennanti) metapopulation in southern Ontario. Our analyses based on network topologies supported the hypothesis of male‐biased dispersal. Furthermore, we demonstrated that the effect of the landscape, identified at the population level, could be partitioned among sex‐specific strata. We found that female connectivity was negatively correlated with snow depth, whereas connectivity among males was not. Our findings underscore the potential of conducting sex‐specific analysis by identifying landscape elements or configuration that differentially promotes or impedes functional connectivity between sexes, revealing processes that may otherwise remain cryptic. We propose that the sex‐specific graph approach would be applicable to other vagile species where differential sex‐specific processes are expected to occur.     

Decoding the different states of visual attention using functional and effective connectivity features in fMRI data

The present paper concentrates on the impact of visual attention task on structure of the brain functional and effective connectivity networks using coherence and Granger causality methods. Since most studies used correlation method and resting-state functional connectivity, the task-based approach was selected for this experiment to boost our knowledge of spatial and feature-based attention. In the present study, the whole brain was divided into 82 sub-regions based on Brodmann areas. The coherence and Granger causality were applied to construct functional and effective connectivity matrices. These matrices were converted into graphs using a threshold, and the graph theory measures were calculated from it including degree and characteristic path length. Visual attention was found to reveal more information during the spatial-based task. The degree was higher while performing a spatial-based task, whereas characteristic path length was lower in the spatial-based task in both functional and effective connectivity. Primary and secondary visual cortex (17 and 18 Brodmann areas) were highly connected to parietal and prefrontal cortex while doing visual attention task. Whole brain connectivity was also calculated in both functional and effective connectivity. Our results reveal that Brodmann areas of 17, 18, 19, 46, 3 and 4 had a significant role proving that somatosensory, parietal and prefrontal regions along with visual cortex were highly connected to other parts of the cortex during the visual attention task. Characteristic path length results indicated an increase in functional connectivity and more functional integration in spatial-based attention compared with feature-based attention. The results of this work can provide useful information about the mechanism of visual attention at the network level.     

Uterine Synchronization Analysis During Pregnancy and Labor Using Graph Theory,Classification Based on Neural Network and Deep Learning

1) ObjectivesPreterm birth caused by preterm labor is one of the major health problems in the world. In this article, we present a new framework for dealing with this problem through the processing of electrohysterographic signals (EHG) that are recorded during labor and pregnancy. The objective in this research is to improve the classification between labor and pregnancy contractions by using a new approach that focuses on the connectivity analysis based on graph parameters, representative of uterine synchronization, and comparing neural network and machine learning methods in order to classify between labor and pregnancy.2) Material and methodsafter denoising of the 16 EHG signals recorded from pregnant women abdomen, we applied different connectivity methods to obtain connectivity matrices; then by using the graph theory, we extracted some graph parameters from the connectivity matrices; finally, we tested different neural network and machine learning methods on the features obtained from both graph and connectivity methods in order to classify between labor and pregnancy.3) ResultsThe best results were obtained by using the logistic regression method. We also evidence the power of graph parameters extracted from the connectivity matrices to improve the classification results.4) ConclusionThe use of graph analysis associated with machine learning methods can be a powerful tool to improve labor and pregnancy classification based on the analysis of EHG signals.