DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

Energy Technology Data Exchange (ETDEWEB)

MASLOV,S.SNEPPEN,K.

2003-11-17

interesting property of many biological networks that was recently brought to attention of the scientific community [3, 4, 5] is an extremely broad distribution of node connectivities defined as the number of immediate neighbors of a given node in the network. While the majority of nodes have just a few edges connecting them to other nodes in the network, there exist some nodes, that we will refer to as ''hubs'', with an unusually large number of neighbors. The connectivity of the most connected hub in such a network is typically several orders of magnitude larger than the average connectivity in the network. Often the distribution of connectivities of individual nodes can be approximated by a scale-free power law form [3] in which case the network is referred to as scale-free. Among biological networks distributions of node connectivities in metabolic [4], protein interaction [5], and brain functional [6] networks can be reasonably approximated by a power law extending for several orders of magnitude. The set of connectivities of individual nodes is an example of a low-level (single-node) topological property of a network. While it answers the question about how many neighbors a given node has, it gives no information about the identity of those neighbors. It is clear that most functional properties of networks are defined at a higher topological level in the exact pattern of connections of nodes to each other. However, such multi-node connectivity patterns are rather difficult to quantify and compare between networks. In this work we concentrate on multi-node topological properties of protein networks. These networks (as any other biological networks) lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as mutations within individual genes, and gene duplications. As a result their connections are characterized by a large degree of randomness. One may wonder which

Social network models predict movement and connectivity in ecological landscapes

Science.gov (United States)

Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.

2011-01-01

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Social network models predict movement and connectivity in ecological landscapes.

Science.gov (United States)

Fletcher, Robert J; Acevedo, Miguel A; Reichert, Brian E; Pias, Kyle E; Kitchens, Wiley M

2011-11-29

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Persistence of self-recruitment and patterns of larval connectivity in a marine protected area network

KAUST Repository

Berumen, Michael L.

2012-02-01

The use of marine protected area (MPA) networks to sustain fisheries and conserve biodiversity is predicated on two critical yet rarely tested assumptions. Individual MPAs must produce sufficient larvae that settle within that reserve\\'s boundaries to maintain local populations while simultaneously supplying larvae to other MPA nodes in the network that might otherwise suffer local extinction. Here, we use genetic parentage analysis to demonstrate that patterns of self-recruitment of two reef fishes (Amphiprion percula and Chaetodon vagabundus) in an MPA in Kimbe Bay, Papua New Guinea, were remarkably consistent over several years. However, dispersal from this reserve to two other nodes in an MPA network varied between species and through time. The stability of our estimates of self-recruitment suggests that even small MPAs may be self-sustaining. However, our results caution against applying optimization strategies to MPA network design without accounting for variable connectivity among species and over time. 2012 The Authors.

Connecting Network Properties of Rapidly Disseminating Epizoonotics

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Rivas, Ariel L.; Fasina, Folorunso O.; Hoogesteyn, Almira L.; Konah, Steven N.; Febles, José L.; Perkins, Douglas J.; Hyman, James M.; Fair, Jeanne M.; Hittner, James B.; Smith, Steven D.

2012-01-01

Background To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure. Methods Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) ‘connectivity’, a model that integrated bio-physical concepts (the agent’s transmission cycle, road topology) into indicators designed to measure networks (‘nodes’ or infected sites with short- and long-range links), and 2) ‘contacts’, which focused on infected individuals but did not assess connectivity. Results The connectivity model showed five network properties: 1) spatial aggregation of cases (disease clusters), 2) links among similar ‘nodes’ (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a “20∶80″ pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads. Conclusions Geo-temporal constructs of Network Theory’s nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished

Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study.

Science.gov (United States)

Wang, X; Jiao, Y; Tang, T; Wang, H; Lu, Z

2013-12-19

Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Minimum cost connection networks

DEFF Research Database (Denmark)

Hougaard, Jens Leth; Tvede, Mich

In the present paper we consider the allocation of cost in connection networks. Agents have connection demands in form of pairs of locations they want to be connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection demands...

How has climate change altered network connectivity in a mountain stream network?

Science.gov (United States)

Ward, A. S.; Schmadel, N.; Wondzell, S. M.; Johnson, S.

2017-12-01

Connectivity along river networks is broadly recognized as dynamic, with seasonal and event-based expansion and contraction of the network extent. Intermittently flowing streams are particularly important as they define a crucial threshold for continuously connected waters that enable migration by aquatic species. In the Pacific northwestern U.S., changes in atmospheric circulation have been found to alter rainfall patterns and result in decreased summer low-flows in the region. However, the impact of this climate dynamic on network connectivity is heretofore unstudied. Thus, we ask: How has connectivity in the riparian corridor changed in response to observed changes in climate? In this study we take the well-studied H.J. Andrews Experimental Forest as representative of mountain river networks in the Pacific northwestern U.S. First, we analyze 63 years of stream gauge information from a network of 11 gauges to document observed changes in timing and magnitude of stream discharge. We found declining magnitudes of seasonal low-flows and shifting seasonality of water export from the catchment, both of which we attribute to changes in precipitation timing and storage as snow vs. rainfall. Next, we use these discharge data to drive a reduced-complexity model of the river network to simulate network connectivity over 63 years. Model results show that network contraction (i.e., minimum network extent) has decreased over the past 63 years. Unexpectedly, the increasing winter peak flows did not correspond with increasing network expansion, suggesting a geologic control on maximum flowing network extent. We find dynamic expansion and contraction of the network primarily occurs during period of catchment discharge less than about 1 m3/s at the outlet, whereas the network extent is generally constant for discharges from 1 to 300 m3/s. Results of our study are of interest to scientists focused on connectivity as a control on ecological processes both directly (e.g., fish

Flexible modulation of network connectivity related to cognition in Alzheimer’s disease

Science.gov (United States)

McLaren, Donald G.; Sperling, Reisa A.; Atria, Alireza

2014-01-01

Functional neuroimaging tools, such as fMRI methods, may elucidate the neural correlates of clinical, behavioral, and cognitive performance. Most functional imaging studies focus on regional task-related activity or resting state connectivity rather than how changes in functional connectivity across conditions and tasks are related to cognitive and behavioral performance. To investigate the promise of characterizing context-dependent connectivity-behavior relationships, this study applies the method of generalized psychophysiological interactions (gPPI) to assess the patterns of associative-memory-related fMRI hippocampal functional connectivity in Alzheimer’s disease (AD) associated with performance on memory and other cognitively demanding neuropsychological tests and clinical measures. Twenty-four subjects with mild AD dementia (ages 54–82, nine females) participated in a face-name paired-associate encoding memory study. Generalized PPI analysis was used to estimate the connectivity between the hippocampus and the whole brain during encoding. The difference in hippocampal-whole brain connectivity between encoding novel and repeated face-name pairs was used in multiple-regression analyses as an independent predictor for 10 behavioral, neuropsychological and clinical tests. The analysis revealed connectivity-behavior relationships that were distributed, dynamically overlapping, and task-specific within and across intrinsic networks; hippocampal-whole brain connectivity-behavior relationships were not isolated to single networks, but spanned multiple brain networks. Importantly, these spatially distributed performance patterns were unique for each measure. In general, out-of-network behavioral associations with encoding novel greater than repeated face-name pairs hippocampal-connectivity were observed in the default-mode network, while correlations with encoding repeated greater than novel face-name pairs hippocampal-connectivity were observed in the executive

Sensitivity of marine protected area network connectivity to atmospheric variability.

Science.gov (United States)

Fox, Alan D; Henry, Lea-Anne; Corne, David W; Roberts, J Murray

2016-11-01

International efforts are underway to establish well-connected systems of marine protected areas (MPAs) covering at least 10% of the ocean by 2020. But the nature and dynamics of ocean ecosystem connectivity are poorly understood, with unresolved effects of climate variability. We used 40-year runs of a particle tracking model to examine the sensitivity of an MPA network for habitat-forming cold-water corals in the northeast Atlantic to changes in larval dispersal driven by atmospheric cycles and larval behaviour. Trajectories of Lophelia pertusa larvae were strongly correlated to the North Atlantic Oscillation (NAO), the dominant pattern of interannual atmospheric circulation variability over the northeast Atlantic. Variability in trajectories significantly altered network connectivity and source-sink dynamics, with positive phase NAO conditions producing a well-connected but asymmetrical network connected from west to east. Negative phase NAO produced reduced connectivity, but notably some larvae tracked westward-flowing currents towards coral populations on the mid-Atlantic ridge. Graph theoretical metrics demonstrate critical roles played by seamounts and offshore banks in larval supply and maintaining connectivity across the network. Larval longevity and behaviour mediated dispersal and connectivity, with shorter lived and passive larvae associated with reduced connectivity. We conclude that the existing MPA network is vulnerable to atmospheric-driven changes in ocean circulation.

Intrinsic network connectivity and own body perception in gender dysphoria.

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Feusner, Jamie D; Lidström, Andreas; Moody, Teena D; Dhejne, Cecilia; Bookheimer, Susan Y; Savic, Ivanka

2017-08-01

Gender dysphoria (GD) is characterized by incongruence between one's identity and gender assigned at birth. The biological mechanisms of GD are unclear. We investigated brain network connectivity patterns involved in own body perception in the context of self in GD. Twenty-seven female-to-male (FtM) individuals with GD, 27 male controls, and 27 female controls underwent resting state fMRI. We compared functional connections within intrinsic connectivity networks involved in self-referential processes and own body perception -default mode network (DMN) and salience network - and visual networks, using independent components analyses. Behavioral correlates of network connectivity were also tested using self-perception ratings while viewing own body images morphed to their sex assigned at birth, and to the sex of their gender identity. FtM exhibited decreased connectivity of anterior and posterior cingulate and precuneus within the DMN compared with controls. In FtM, higher "self" ratings for bodies morphed towards the sex of their gender identity were associated with greater connectivity of the anterior cingulate within the DMN, during long viewing times. In controls, higher ratings for bodies morphed towards their gender assigned at birth were associated with right insula connectivity within the salience network, during short viewing times. Within visual networks FtM showed weaker connectivity in occipital and temporal regions. Results suggest disconnectivity within networks involved in own body perception in the context of self in GD. Moreover, perception of bodies in relation to self may be reflective rather than reflexive, as a function of mesial prefrontal processes. These may represent neurobiological correlates to the subjective disconnection between perception of body and self-identification.

Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

Science.gov (United States)

Shou, Guofa; Mosconi, Matthew W.; Wang, Jun; Ethridge, Lauren E.; Sweeney, John A.; Ding, Lei

2017-08-01

Objective. Abnormal local and long-range brain connectivity have been widely reported in autism spectrum disorder (ASD), yet the nature of these abnormalities and their functional relevance at distinct cortical rhythms remains unknown. Investigations of intrinsic connectivity networks (ICNs) and their coherence across whole brain networks hold promise for determining whether patterns of functional connectivity abnormalities vary across frequencies and networks in ASD. In the present study, we aimed to probe atypical intrinsic brain connectivity networks in ASD from resting-state electroencephalography (EEG) data via characterizing the whole brain network. Approach. Connectivity within individual ICNs (measured by spectral power) and between ICNs (measured by coherence) were examined at four canonical frequency bands via a time-frequency independent component analysis on high-density EEG, which were recorded from 20 ASD and 20 typical developing (TD) subjects during an eyes-closed resting state. Main results. Among twelve identified electrophysiological ICNs, individuals with ASD showed hyper-connectivity in individual ICNs and hypo-connectivity between ICNs. Functional connectivity alterations in ASD were more severe in the frontal lobe and the default mode network (DMN) and at low frequency bands. These functional connectivity measures also showed abnormal age-related associations in ICNs related to frontal, temporal and motor regions in ASD. Significance. Our findings suggest that ASD is characterized by the opposite directions of abnormalities (i.e. hypo- and hyper-connectivity) in the hierarchical structure of the whole brain network, with more impairments in the frontal lobe and the DMN at low frequency bands, which are critical for top-down control of sensory systems, as well as for both cognition and social skills.

Switch-connected HyperX network

Science.gov (United States)

Chen, Dong; Heidelberger, Philip

2018-02-13

A network system includes a plurality of sub-network planes and global switches. The sub-network planes have a same network topology as each other. Each of the sub-network planes includes edge switches. Each of the edge switches has N ports. Each of the global switches is configured to connect a group of edge switches at a same location in the sub-network planes. In each of the sub-network planes, some of the N ports of each of the edge switches are connected to end nodes, and others of the N ports are connected to other edge switches in the same sub-network plane, other of the N ports are connected to at least one of the global switches.

Functional connectivity pattern during rest within the episodic memory network in association with episodic memory performance in bipolar disorder.

Science.gov (United States)

Oertel-Knöchel, Viola; Reinke, Britta; Matura, Silke; Prvulovic, David; Linden, David E J; van de Ven, Vincent

2015-02-28

In this study, we sought to examine the intrinsic functional organization of the episodic memory network during rest in bipolar disorder (BD). The previous work suggests that deficits in intrinsic functional connectivity may account for impaired memory performance. We hypothesized that regions involved in episodic memory processing would reveal aberrant functional connectivity in patients with bipolar disorder. We examined 21 patients with BD and 21 healthy matched controls who underwent functional magnetic resonance imaging (fMRI) during a resting condition. We did a seed-based functional connectivity analysis (SBA), using the regions of the episodic memory network that showed a significantly different activation pattern during task-related fMRI as seeds. The functional connectivity scores (FC) were further correlated with episodic memory task performance. Our results revealed decreased FC scores within frontal areas and between frontal and temporal/hippocampal/limbic regions in BD patients in comparison with controls. We observed higher FC in BD patients compared with controls between frontal and limbic regions. The decrease in fronto-frontal functional connectivity in BD patients showed a significant positive association with episodic memory performance. The association between task-independent dysfunctional frontal-limbic FC and episodic memory performance may be relevant for current pathophysiological models of the disease. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Detecting altered connectivity patterns in HIV associated neurocognitive impairment using mutual connectivity analysis

Science.gov (United States)

Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

The use of functional Magnetic Resonance Imaging (fMRI) has provided interesting insights into our understanding of the brain. In clinical setups these scans have been used to detect and study changes in the brain network properties in various neurological disorders. A large percentage of subjects infected with HIV present cognitive deficits, which are known as HIV associated neurocognitive disorder (HAND). In this study we propose to use our novel technique named Mutual Connectivity Analysis (MCA) to detect differences in brain networks in subjects with and without HIV infection. Resting state functional MRI scans acquired from 10 subjects (5 HIV+ and 5 HIV-) were subject to standard preprocessing routines. Subsequently, the average time-series for each brain region of the Automated Anatomic Labeling (AAL) atlas are extracted and used with the MCA framework to obtain a graph characterizing the interactions between them. The network graphs obtained for different subjects are then compared using Network-Based Statistics (NBS), which is an approach to detect differences between graphs edges while controlling for the family-wise error rate when mass univariate testing is performed. Applying this approach on the graphs obtained yields a single network encompassing 42 nodes and 65 edges, which is significantly different between the two subject groups. Specifically connections to the regions in and around the basal ganglia are significantly decreased. Also some nodes corresponding to the posterior cingulate cortex are affected. These results are inline with our current understanding of pathophysiological mechanisms of HIV associated neurocognitive disease (HAND) and other HIV based fMRI connectivity studies. Hence, we illustrate the applicability of our novel approach with network-based statistics in a clinical case-control study to detect differences connectivity patterns.

Handbook of networking & connectivity

CERN Document Server

McClain, Gary R

1994-01-01

Handbook of Networking & Connectivity focuses on connectivity standards in use, including hardware and software options. The book serves as a guide for solving specific problems that arise in designing and maintaining organizational networks.The selection first tackles open systems interconnection, guide to digital communications, and implementing TCP/IP in an SNA environment. Discussions focus on elimination of the SNA backbone, routing SNA over internets, connectionless versus connection-oriented networks, internet concepts, application program interfaces, basic principles of layering, proto

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity.

Science.gov (United States)

Finn, Emily S; Shen, Xilin; Scheinost, Dustin; Rosenberg, Monica D; Huang, Jessica; Chun, Marvin M; Papademetris, Xenophon; Constable, R Todd

2015-11-01

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

Connectivity and dynamics of neuronal networks as defined by the shape of individual neurons

International Nuclear Information System (INIS)

Ahnert, Sebastian E; A N Travencolo, Bruno; Costa, Luciano da Fontoura

2009-01-01

Biological neuronal networks constitute a special class of dynamical systems, as they are formed by individual geometrical components, namely the neurons. In the existing literature, relatively little attention has been given to the influence of neuron shape on the overall connectivity and dynamics of the emerging networks. The current work addresses this issue by considering simplified neuronal shapes consisting of circular regions (soma/axons) with spokes (dendrites). Networks are grown by placing these patterns randomly in the two-dimensional (2D) plane and establishing connections whenever a piece of dendrite falls inside an axon. Several topological and dynamical properties of the resulting graph are measured, including the degree distribution, clustering coefficients, symmetry of connections, size of the largest connected component, as well as three hierarchical measurements of the local topology. By varying the number of processes of the individual basic patterns, we can quantify relationships between the individual neuronal shape and the topological and dynamical features of the networks. Integrate-and-fire dynamics on these networks is also investigated with respect to transient activation from a source node, indicating that long-range connections play an important role in the propagation of avalanches.

Repositioning through Culture: Testing Change in Connectivity Patterns

Directory of Open Access Journals (Sweden)

Beatriz Plaza

2016-12-01

Full Text Available Symbolic knowledge-driven innovations can play an important role in the economic development of cities and regions. Cultural events and infrastructures can act as powerful connectivity engines, generating new connections, rewiring links, and repositioning institutions/cities/regions on the Internet map. Within this framework, this paper aims to contribute to the analytical understanding of culture-led repositioning. For this purpose we perform regression analysis with cultural networks (observational cross-sectional network data from digital media for a specific cultural case study: the Basque Culinary Center (BCC, a higher education faculty of haute cuisine promoted by the University of Mondragon along with a group of Michelin-starred chefs. Results show that a cultural sector, such as haute cuisine, can contribute to structural changes in connectivity patterns, putting an institution/city/region on the media map. It is the connection (in the online press of the BCC to the influential Michelin-starred chefs that can fuel the accumulation of press articles (media items on the BCC; and it is precisely this accumulation of press articles that can impact BCC revenues. Put differently, the co-branding between the influential Michelin chefs and the BCC may have put the BCC on the press map, promoting new student registrations and fostering Basque haute cuisine. The main contribution of this article is a prototype of regression analysis to test repositioning with network data.

Pattern Recognition for Reliability Assessment of Water Distribution Networks

NARCIS (Netherlands)

Trifunovi?, N.

2012-01-01

The study presented in this manuscript investigates the patterns that describe reliability of water distribution networks focusing to the node connectivity, energy balance, and economics of construction, operation and maintenance. A number of measures to evaluate the network resilience has been

Network structure shapes spontaneous functional connectivity dynamics.

Science.gov (United States)

Shen, Kelly; Hutchison, R Matthew; Bezgin, Gleb; Everling, Stefan; McIntosh, Anthony R

2015-04-08

The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture. Copyright © 2015 the authors 0270-6474/15/355579-10$15.00/0.

Intrinsic connectivity networks within cerebellum and beyond in eating disorders.

Science.gov (United States)

Amianto, F; D'Agata, F; Lavagnino, L; Caroppo, P; Abbate-Daga, G; Righi, D; Scarone, S; Bergui, M; Mortara, P; Fassino, S

2013-10-01

Cerebellum seems to have a role both in feeding behavior and emotion regulation; therefore, it is a region that warrants further neuroimaging studies in eating disorders, severe conditions that determine a significant impairment in the physical and psychological domain. The aim of this study was to examine the cerebellum intrinsic connectivity during functional magnetic resonance imaging resting state in anorexia nervosa (AN), bulimia nervosa (BN), and healthy controls (CN). Resting state brain activity was decomposed into intrinsic connectivity networks (ICNs) using group spatial independent component analysis on the resting blood oxygenation level dependent time courses of 12 AN, 12 BN, and 10 CN. We extracted the cerebellar ICN and compared it between groups. Intrinsic connectivity within the cerebellar network showed some common alterations in eating disordered compared to healthy subjects (e.g., a greater connectivity with insulae, vermis, and paravermis and a lesser connectivity with parietal lobe); AN and BN patients were characterized by some peculiar alterations in connectivity patterns (e.g., greater connectivity with the insulae in AN compared to BN, greater connectivity with anterior cingulate cortex in BN compared to AN). Our data are consistent with the presence of different alterations in the cerebellar network in AN and BN patients that could be related to psychopathologic dimensions of eating disorders.

Global terrestrial water storage connectivity revealed using complex climate network analyses

Science.gov (United States)

Sun, A. Y.; Chen, J.; Donges, J.

2015-07-01

Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

Age Differences in the Intrinsic Functional Connectivity of Default Network Subsystems

Directory of Open Access Journals (Sweden)

Karen eCampbell

2013-11-01

Full Text Available Recent work suggests that the default mode network (DMN includes two core regions, the ventromedial prefrontal cortex (vmPFC and posterior cingulate cortex (PCC, and several unique subsystems that are functionally distinct. These include a medial temporal lobe (MTL subsystem, active during remembering and future projection, and a dorsomedial PFC (dmPFC subsystem, active during self-reference. The PCC has been further subdivided into ventral (vPCC and dorsal (dPCC regions that are more strongly connected with the DMN and cognitive control networks, respectively. The goal of this study was to examine age differences in resting state functional connectivity within these subsystems. After applying a rigorous procedure to reduce the effects of head motion, we used a multivariate technique to identify both common and unique patterns of functional connectivity in the MTL vs. the dmPFC, and in vPCC vs. dPCC. All four areas had robust functional connectivity with other DMN regions, and each also showed distinct connectivity patterns in both age groups. Young and older adults had equivalent functional connectivity in the MTL subsystem. Older adults showed weaker connectivity in the vPCC and dmPFC subsystems, particularly with other DMN areas, but stronger connectivity than younger adults in the dPCC subsystem, which included areas involved in cognitive control. Our data provide evidence for distinct subsystems involving DMN nodes, which are maintained with age. Nevertheless, there are age differences in the strength of functional connectivity within these subsystems, supporting prior evidence that DMN connectivity is particularly vulnerable to age, whereas connectivity involving cognitive control regions is relatively maintained. These results suggest an age difference in the integrated activity among brain networks that can have implications for cognition in older adults.

Patterns of brain structural connectivity differentiate normal weight from overweight subjects.

Science.gov (United States)

Gupta, Arpana; Mayer, Emeran A; Sanmiguel, Claudia P; Van Horn, John D; Woodworth, Davis; Ellingson, Benjamin M; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S

2015-01-01

Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69

Patterns of brain structural connectivity differentiate normal weight from overweight subjects

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Gupta, Arpana; Mayer, Emeran A.; Sanmiguel, Claudia P.; Van Horn, John D.; Woodworth, Davis; Ellingson, Benjamin M.; Fling, Connor; Love, Aubrey; Tillisch, Kirsten; Labus, Jennifer S.

2015-01-01

Background Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. Aim To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. Methods Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. Results 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42

An approach to evaluate the topological significance of motifs and other patterns in regulatory networks

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Wingender Edgar

2009-05-01

Full Text Available Abstract Background The identification of network motifs as statistically over-represented topological patterns has become one of the most promising topics in the analysis of complex networks. The main focus is commonly made on how they operate by means of their internal organization. Yet, their contribution to a network's global architecture is poorly understood. However, this requires switching from the abstract view of a topological pattern to the level of its instances. Here, we show how a recently proposed metric, the pairwise disconnectivity index, can be adapted to survey if and which kind of topological patterns and their instances are most important for sustaining the connectivity within a network. Results The pairwise disconnectivity index of a pattern instance quantifies the dependency of the pairwise connections between vertices in a network on the presence of this pattern instance. Thereby, it particularly considers how the coherence between the unique constituents of a pattern instance relates to the rest of a network. We have applied the method exemplarily to the analysis of 3-vertex topological pattern instances in the transcription networks of a bacteria (E. coli, a unicellular eukaryote (S. cerevisiae and higher eukaryotes (human, mouse, rat. We found that in these networks only very few pattern instances break lots of the pairwise connections between vertices upon the removal of an instance. Among them network motifs do not prevail. Rather, those patterns that are shared by the three networks exhibit a conspicuously enhanced pairwise disconnectivity index. Additionally, these are often located in close vicinity to each other or are even overlapping, since only a small number of genes are repeatedly present in most of them. Moreover, evidence has gathered that the importance of these pattern instances is due to synergistic rather than merely additive effects between their constituents. Conclusion A new method has been proposed

Changes of functional connectivity in the left frontoparietal network following aphasic stroke

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Dan eZhu

2014-05-01

Full Text Available Language is an essential higher cognitive function supported by large-scale brain networks. In this study, we investigated functional connectivity changes in the left frontoparietal network (LFPN, a language-cognition related brain network in aphasic patients. We enrolled thirteen aphasic patients who had undergone a stroke in the left hemisphere and age-, gender-, educational level-matched controls and analyzed the data by integrating independent component analysis (ICA with a network connectivity analysis method. Resting state functional magnetic resonance imaging (fMRI and clinical evaluation of language function were assessed at two stages: one and two months after stroke onset. We found reduced functional connectivity between the LFPN and the right middle frontal cortex, medial frontal cortex and right inferior frontal cortex in aphasic patients as compared to controls. Correlation analysis showed that stronger functional connectivity between the LFPN and the right middle frontal cortex and medial frontal cortex coincided with more preserved language comprehension ability after stroke. Network connectivity analysis showed reduced LFPN connectivity as indicated by the mean network connectivity index of key regions in the LFPN of aphasic patients. The decreased LFPN connectivity in stroke patients was significantly associated with the impairment of language function in their comprehension ability. We also found significant association between recovery of comprehension ability and the mean changes in intrinsic LFPN connectivity. Our findings suggest that brain lesions may influence language comprehension by altering functional connectivity between regions and that the patterns of abnormal functional connectivity may contribute to the recovery of language deficits.

A host-endoparasite network of Neotropical marine fish: are there organizational patterns?

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Bellay, Sybelle; Lima, Dilermando P; Takemoto, Ricardo M; Luque, José L

2011-12-01

Properties of ecological networks facilitate the understanding of interaction patterns in host-parasite systems as well as the importance of each species in the interaction structure of a community. The present study evaluates the network structure, functional role of all species and patterns of parasite co-occurrence in a host-parasite network to determine the organization level of a host-parasite system consisting of 170 taxa of gastrointestinal metazoans of 39 marine fish species on the coast of Brazil. The network proved to be nested and modular, with a low degree of connectance. Host-parasite interactions were influenced by host phylogeny. Randomness in parasite co-occurrence was observed in most modules and component communities, although species segregation patterns were also observed. The low degree of connectance in the network may be the cause of properties such as nestedness and modularity, which indicate the presence of a high number of peripheral species. Segregation patterns among parasite species in modules underscore the role of host specificity. Knowledge of ecological networks allows detection of keystone species for the maintenance of biodiversity and the conduction of further studies on the stability of networks in relation to frequent environmental changes.

Multimodal Hyper-connectivity Networks for MCI Classification.

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Li, Yang; Gao, Xinqiang; Jie, Biao; Yap, Pew-Thian; Kim, Min-Jeong; Wee, Chong-Yaw; Shen, Dinggang

2017-09-01

Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions. In this paper, a novel multimodal hyper-network modelling method was proposed for improving the diagnostic accuracy of mild cognitive impairment (MCI). Specifically, we first constructed a multimodal hyper-connectivity network by simultaneously considering information from diffusion tensor imaging and resting-state functional magnetic resonance imaging data. We then extracted different types of network features from the hyper-connectivity network, and further exploited a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Our proposed multimodal hyper-connectivity network demonstrated a better MCI classification performance than the conventional single modality based hyper-connectivity networks.

Resting-state functional connectivity of orthographic networks in acquired dysgraphia

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Gali Ellenblum

2015-05-01

The NTA findings indicate that the relationship between orthographic and default-mode networks is characterized by greater within- vs. across-network connectivity. Furthermore, we show for the first time a pattern of increasing within/across network “coherence normalization” following spelling rehabilitation. Additional dysgraphic participants and other networks (language, sensory-motor, etc. will be analyzed to develop a better understanding of the RS orthographic network and its response to damage and recovery. Acknowledgements. The work is part of a multi-site, NIDCD-supported project examining language recovery neurobiology in aphasia (DC006740. We thank Melissa Greenberger and Xiao-Wei Song.

Synchronization from Second Order Network Connectivity Statistics

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Zhao, Liqiong; Beverlin, Bryce; Netoff, Theoden; Nykamp, Duane Q.

2011-01-01

We investigate how network structure can influence the tendency for a neuronal network to synchronize, or its synchronizability, independent of the dynamical model for each neuron. The synchrony analysis takes advantage of the framework of second order networks, which defines four second order connectivity statistics based on the relative frequency of two-connection network motifs. The analysis identifies two of these statistics, convergent connections, and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections. These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks. Surprisingly, divergent connections, which determine the fraction of shared inputs, do not strongly influence the synchrony. The critical role of chains, rather than divergent connections, in influencing synchrony can be explained by their increasing the effective coupling strength. The decrease of synchrony with convergent connections is primarily due to the resulting heterogeneity in firing rates. PMID:21779239

Synchronization from second order network connectivity statistics

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Liqiong eZhao

2011-07-01

Full Text Available We investigate how network structure can influence the tendency for a neuronal network to synchronize, or its synchronizability, independent of the dynamical model for each neuron. The synchrony analysis takes advantage of the framework of second order networks (SONETs, which defines four second order connectivity statistics based on the relative frequency of two-connection network motifs. The analysis identifies two of these statistics, convergent connections and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections. These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks. Surprisingly, divergent connections, which determine the fraction of shared inputs, do not strongly influence the synchrony. The critical role of chains, rather than divergent connections, in influencing synchrony can be explained by a pool and redistribute mechanism. The pooling of many inputs averages out independent fluctuations, amplifying weak correlations in the inputs. With increased chain connections, neurons with many inputs tend to have many outputs. Hence, chains ensure that the amplified correlations in the neurons with many inputs are redistributed throughout the network, enhancing the development of synchrony across the network.

A signaling network for patterning of neuronal connectivity in the Drosophila brain.

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Mohammed Srahna

2006-10-01

Full Text Available The precise number and pattern of axonal connections generated during brain development regulates animal behavior. Therefore, understanding how developmental signals interact to regulate axonal extension and retraction to achieve precise neuronal connectivity is a fundamental goal of neurobiology. We investigated this question in the developing adult brain of Drosophila and find that it is regulated by crosstalk between Wnt, fibroblast growth factor (FGF receptor, and Jun N-terminal kinase (JNK signaling, but independent of neuronal activity. The Rac1 GTPase integrates a Wnt-Frizzled-Disheveled axon-stabilizing signal and a Branchless (FGF-Breathless (FGF receptor axon-retracting signal to modulate JNK activity. JNK activity is necessary and sufficient for axon extension, whereas the antagonistic Wnt and FGF signals act to balance the extension and retraction required for the generation of the precise wiring pattern.

Aberrant functional connectivity of resting state networks in transient ischemic attack.

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Rong Li

Full Text Available BACKGROUND: Transient ischemic attack (TIA is usually defined as a neurologic ischemic disorder without permanent cerebral infarction. Studies have showed that patients with TIA can have lasting cognitive functional impairment. Inherent brain activity in the resting state is spatially organized in a set of specific coherent patterns named resting state networks (RSNs, which epitomize the functional architecture of memory, language, attention, visual, auditory and somato-motor networks. Here, we aimed to detect differences in RSNs between TIA patients and healthy controls (HCs. METHODS: Twenty one TIA patients suffered an ischemic event and 21 matched HCs were enrolled in the study. All subjects were investigated using cognitive tests, psychiatric tests and functional magnetic resonance imaging (fMRI. Independent component analysis (ICA was adopted to acquire the eight brain RSNs. Then one-sample t-tests were calculated in each group to gather the spatial maps of each RSNs, followed by second level analysis to investigate statistical differences on RSNs between twenty one TIA patients and 21 controls. Furthermore, a correlation analysis was performed to explore the relationship between functional connectivity (FC and cognitive and psychiatric scales in TIA group. RESULTS: Compared with the controls, TIA patients exhibited both decreased and increased functional connectivity in default mode network (DMN and self-referential network (SRN, and decreased functional connectivity in dorsal attention network (DAN, central-executive network (CEN, core network (CN, somato-motor network (SMN, visual network (VN and auditory network (AN. There was no correlation between neuropsychological scores and functional connectivity in regions of RSNs. CONCLUSIONS: We observed selective impairments of RSN intrinsic FC in TIA patients, whose all eight RSNs had aberrant functional connectivity. These changes indicate that TIA is a disease with widely abnormal brain

Reef-fish larval dispersal patterns validate no-take marine reserve network connectivity that links human communities

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Abesamis, Rene A.; Saenz-Agudelo, Pablo; Berumen, Michael L.; Bode, Michael; Jadloc, Claro Renato L.; Solera, Leilani A.; Villanoy, Cesar L.; Bernardo, Lawrence Patrick C.; Alcala, Angel C.; Russ, Garry R.

2017-09-01

Networks of no-take marine reserves (NTMRs) are a widely advocated strategy for managing coral reefs. However, uncertainty about the strength of population connectivity between individual reefs and NTMRs through larval dispersal remains a major obstacle to effective network design. In this study, larval dispersal among NTMRs and fishing grounds in the Philippines was inferred by conducting genetic parentage analysis on a coral-reef fish ( Chaetodon vagabundus). Adult and juvenile fish were sampled intensively in an area encompassing approximately 90 km of coastline. Thirty-seven true parent-offspring pairs were accepted after screening 1978 juveniles against 1387 adults. The data showed all types of dispersal connections that may occur in NTMR networks, with assignments suggesting connectivity among NTMRs and fishing grounds ( n = 35) far outnumbering those indicating self-recruitment ( n = 2). Critically, half (51%) of the inferred occurrences of larval dispersal linked reefs managed by separate, independent municipalities and constituent villages, emphasising the need for nested collaborative management arrangements across management units to sustain NTMR networks. Larval dispersal appeared to be influenced by wind-driven seasonal reversals in the direction of surface currents. The best-fit larval dispersal kernel estimated from the parentage data predicted that 50% of larvae originating from a population would attempt to settle within 33 km, and 95% within 83 km. Mean larval dispersal distance was estimated to be 36.5 km. These results suggest that creating a network of closely spaced (less than a few tens of km apart) NTMRs can enhance recruitment for protected and fished populations throughout the NTMR network. The findings underscore major challenges for regional coral-reef management initiatives that must be addressed with priority: (1) strengthening management of NTMR networks across political or customary boundaries; and (2) achieving adequate population

Reef-fish larval dispersal patterns validate no-take marine reserve network connectivity that links human communities

KAUST Repository

Abesamis, Rene A.

2017-03-24

Networks of no-take marine reserves (NTMRs) are a widely advocated strategy for managing coral reefs. However, uncertainty about the strength of population connectivity between individual reefs and NTMRs through larval dispersal remains a major obstacle to effective network design. In this study, larval dispersal among NTMRs and fishing grounds in the Philippines was inferred by conducting genetic parentage analysis on a coral-reef fish (Chaetodon vagabundus). Adult and juvenile fish were sampled intensively in an area encompassing approximately 90 km of coastline. Thirty-seven true parent-offspring pairs were accepted after screening 1978 juveniles against 1387 adults. The data showed all types of dispersal connections that may occur in NTMR networks, with assignments suggesting connectivity among NTMRs and fishing grounds (n = 35) far outnumbering those indicating self-recruitment (n = 2). Critically, half (51%) of the inferred occurrences of larval dispersal linked reefs managed by separate, independent municipalities and constituent villages, emphasising the need for nested collaborative management arrangements across management units to sustain NTMR networks. Larval dispersal appeared to be influenced by wind-driven seasonal reversals in the direction of surface currents. The best-fit larval dispersal kernel estimated from the parentage data predicted that 50% of larvae originating from a population would attempt to settle within 33 km, and 95% within 83 km. Mean larval dispersal distance was estimated to be 36.5 km. These results suggest that creating a network of closely spaced (less than a few tens of km apart) NTMRs can enhance recruitment for protected and fished populations throughout the NTMR network. The findings underscore major challenges for regional coral-reef management initiatives that must be addressed with priority: (1) strengthening management of NTMR networks across political or customary boundaries; and (2) achieving adequate population

Network based approaches reveal clustering in protein point patterns

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Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

2014-03-01

Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

Brain network connectivity in individuals with schizophrenia and their siblings.

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Repovs, Grega; Csernansky, John G; Barch, Deanna M

2011-05-15

Research on brain activity in schizophrenia has shown that changes in the function of any single region cannot explain the range of cognitive and affective impairments in this illness. Rather, neural circuits that support sensory, cognitive, and emotional processes are now being investigated as substrates for cognitive and affective impairments in schizophrenia, a shift in focus consistent with long-standing hypotheses about schizophrenia as a disconnection syndrome. Our goal was to further examine alterations in functional connectivity within and between the default mode network and three cognitive control networks (frontal-parietal, cingulo-opercular, and cerebellar) as a basis for such impairments. Resting state functional magnetic resonance imaging was collected from 40 individuals with DSM-IV-TR schizophrenia, 31 siblings of individuals with schizophrenia, 15 healthy control subjects, and 18 siblings of healthy control subjects while they rested quietly with their eyes closed. Connectivity metrics were compared between patients and control subjects for both within- and between-network connections and were used to predict clinical symptoms and cognitive function. Individuals with schizophrenia showed reduced distal and somewhat enhanced local connectivity between the cognitive control networks compared with control subjects. Additionally, greater connectivity between the frontal-parietal and cerebellar regions was robustly predictive of better cognitive performance across groups and predictive of fewer disorganization symptoms among patients. These results are consistent with the hypothesis that impairments of executive function and cognitive control result from disruption in the coordination of activity across brain networks and additionally suggest that these might reflect impairments in normal pattern of brain connectivity development. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Chimera states in brain networks: Empirical neural vs. modular fractal connectivity

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Chouzouris, Teresa; Omelchenko, Iryna; Zakharova, Anna; Hlinka, Jaroslav; Jiruska, Premysl; Schöll, Eckehard

2018-04-01

Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.

Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?

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Li, Dong; Zhou, Changsong

2011-01-01

Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576

Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

International Nuclear Information System (INIS)

Wang, Xun-Heng; Li, Lihua

2015-01-01

Highlights: • Temporal patterns within ICNs provide new way to investigate ADHD brains. • ADHD exhibits enhanced temporal activities within and between ICNs. • Network-wise ALFF influences functional connectivity between ICNs. • Univariate patterns within ICNs are correlated to behavior scores. - Abstract: Purpose: Investigating the altered temporal features within and between intrinsic connectivity networks (ICNs) for boys with attention-deficit/hyperactivity disorder (ADHD); and analyzing the relationships between altered temporal features within ICNs and behavior scores. Materials and methods: A cohort of boys with combined type of ADHD and a cohort of age-matched healthy boys were recruited from ADHD-200 Consortium. All resting-state fMRI datasets were preprocessed and normalized into standard brain space. Using general linear regression, 20 ICNs were taken as spatial templates to analyze the time-courses of ICNs for each subject. Amplitude of low frequency fluctuations (ALFFs) were computed as univariate temporal features within ICNs. Pearson correlation coefficients and node strengths were computed as bivariate temporal features between ICNs. Additional correlation analysis was performed between temporal features of ICNs and behavior scores. Results: ADHD exhibited more activated network-wise ALFF than normal controls in attention and default mode-related network. Enhanced functional connectivities between ICNs were found in ADHD. The network-wise ALFF within ICNs might influence the functional connectivity between ICNs. The temporal pattern within posterior default mode network (pDMN) was positively correlated to inattentive scores. The subcortical network, fusiform-related DMN and attention-related networks were negatively correlated to Intelligence Quotient (IQ) scores. Conclusion: The temporal low frequency oscillations of ICNs in boys with ADHD were more activated than normal controls during resting state; the temporal features within ICNs could

Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

Energy Technology Data Exchange (ETDEWEB)

Wang, Xun-Heng, E-mail: xhwang@hdu.edu.cn [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China); School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096 (China); Li, Lihua [College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018 (China)

2015-05-15

Highlights: • Temporal patterns within ICNs provide new way to investigate ADHD brains. • ADHD exhibits enhanced temporal activities within and between ICNs. • Network-wise ALFF influences functional connectivity between ICNs. • Univariate patterns within ICNs are correlated to behavior scores. - Abstract: Purpose: Investigating the altered temporal features within and between intrinsic connectivity networks (ICNs) for boys with attention-deficit/hyperactivity disorder (ADHD); and analyzing the relationships between altered temporal features within ICNs and behavior scores. Materials and methods: A cohort of boys with combined type of ADHD and a cohort of age-matched healthy boys were recruited from ADHD-200 Consortium. All resting-state fMRI datasets were preprocessed and normalized into standard brain space. Using general linear regression, 20 ICNs were taken as spatial templates to analyze the time-courses of ICNs for each subject. Amplitude of low frequency fluctuations (ALFFs) were computed as univariate temporal features within ICNs. Pearson correlation coefficients and node strengths were computed as bivariate temporal features between ICNs. Additional correlation analysis was performed between temporal features of ICNs and behavior scores. Results: ADHD exhibited more activated network-wise ALFF than normal controls in attention and default mode-related network. Enhanced functional connectivities between ICNs were found in ADHD. The network-wise ALFF within ICNs might influence the functional connectivity between ICNs. The temporal pattern within posterior default mode network (pDMN) was positively correlated to inattentive scores. The subcortical network, fusiform-related DMN and attention-related networks were negatively correlated to Intelligence Quotient (IQ) scores. Conclusion: The temporal low frequency oscillations of ICNs in boys with ADHD were more activated than normal controls during resting state; the temporal features within ICNs could

Default network connectivity in medial temporal lobe amnesia.

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Hayes, Scott M; Salat, David H; Verfaellie, Mieke

2012-10-17

There is substantial overlap between the brain regions supporting episodic memory and the default network. However, in humans, the impact of bilateral medial temporal lobe (MTL) damage on a large-scale neural network such as the default mode network is unknown. To examine this issue, resting fMRI was performed with amnesic patients and control participants. Seed-based functional connectivity analyses revealed robust default network connectivity in amnesia in cortical default network regions such as medial prefrontal cortex, posterior medial cortex, and lateral parietal cortex, as well as evidence of connectivity to residual MTL tissue. Relative to control participants, decreased posterior cingulate cortex connectivity to MTL and increased connectivity to cortical default network regions including lateral parietal and medial prefrontal cortex were observed in amnesic patients. In contrast, somatomotor network connectivity was intact in amnesic patients, indicating that bilateral MTL lesions may selectively impact the default network. Changes in default network connectivity in amnesia were largely restricted to the MTL subsystem, providing preliminary support from MTL amnesic patients that the default network can be fractionated into functionally and structurally distinct components. To our knowledge, this is the first examination of the default network in amnesia.

Nonrandom network connectivity comes in pairs

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Felix Z. Hoffmann

2017-02-01

Full Text Available Overrepresentation of bidirectional connections in local cortical networks has been repeatedly reported and is a focus of the ongoing discussion of nonrandom connectivity. Here we show in a brief mathematical analysis that in a network in which connection probabilities are symmetric in pairs, Pij = Pji, the occurrences of bidirectional connections and nonrandom structures are inherently linked; an overabundance of reciprocally connected pairs emerges necessarily when some pairs of neurons are more likely to be connected than others. Our numerical results imply that such overrepresentation can also be sustained when connection probabilities are only approximately symmetric.

Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment Patients.

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Sang, Linqiong; Chen, Lin; Wang, Li; Zhang, Jingna; Zhang, Ye; Li, Pengyue; Li, Chuanming; Qiu, Mingguo

2018-01-01

Cognitive impairment caused by subcortical ischemic vascular disease (SIVD) has been elucidated by many neuroimaging studies. However, little is known regarding the changes in brain functional connectivity networks in relation to the severity of cognitive impairment in SIVD. In the present study, 20 subcortical ischemic vascular cognitive impairment no dementia patients (SIVCIND) and 20 dementia patients (SIVaD) were enrolled; additionally, 19 normal controls were recruited. Each participant underwent a resting-state functional MRI scan. Whole-brain functional networks were analyzed with graph theory and network-based statistics (NBS) to study the functional organization of networks and find alterations in functional connectivity among brain regions. After adjustments for age, gender, and duration of formal education, there were significant group differences for two network functional organization indices, global efficiency and local efficiency, which decreased (NC > SIVCIND > SIVaD) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, p  impairment worsened, with an increased number of decreased connections between brain regions. We also observed more reductions in nodal efficiency in the prefrontal and temporal cortices for SIVaD than for SIVCIND. These findings indicated a progressively disrupted pattern of the brain functional connectivity network with increased cognitive impairment and showed promise for the development of reliable biomarkers of network metric changes related to cognitive impairment caused by SIVD.

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

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Wang, Xun-Heng; Jiao, Yun; Li, Lihua

2017-10-24

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken asan intra-ICN feature, and phase synchronization (PS) was used asan inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r=0.79 (p<10 -8 ), and the performance of the predictive model for impulsivity is r=0.48 (p<10 -8 ). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Forced phase-locked states and information retrieval in a two-layer network of oscillatory neurons with directional connectivity

International Nuclear Information System (INIS)

Kazantsev, Victor; Pimashkin, Alexey

2007-01-01

We propose two-layer architecture of associative memory oscillatory network with directional interlayer connectivity. The network is capable to store information in the form of phase-locked (in-phase and antiphase) oscillatory patterns. The first (input) layer takes an input pattern to be recognized and their units are unidirectionally connected with all units of the second (control) layer. The connection strengths are weighted using the Hebbian rule. The output (retrieved) patterns appear as forced-phase locked states of the control layer. The conditions are found and analytically expressed for pattern retrieval in response on incoming stimulus. It is shown that the system is capable to recover patterns with a certain level of distortions or noises in their profiles. The architecture is implemented with the Kuramoto phase model and using synaptically coupled neural oscillators with spikes. It is found that the spiking model is capable to retrieve patterns using the spiking phase that translates memorized patterns into the spiking phase shifts at different time scales

Connectivity and Nestedness in Bipartite Networks from Community Ecology

International Nuclear Information System (INIS)

Corso, Gilberto; De Araujo, A I Levartoski; De Almeida, Adriana M

2011-01-01

Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index ν. The index ν is found using the relation ν = 1 - τ where τ is the temperature of the adjacency matrix of the bipartite network. By its turn τ is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index ν is a function of the connectivities of the bipartite network. In addition we find a concise way to find ν which avoid cumbersome algorithm manupulation of the adjacency matrix.

Connectivity and Nestedness in Bipartite Networks from Community Ecology

Energy Technology Data Exchange (ETDEWEB)

Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, UFRN - Campus Universitario, Lagoa Nova, CEP 59078 972, Natal, RN (Brazil); De Araujo, A I Levartoski [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara Av. Treze de Maio, 2081 - Benfica CEP 60040-531 - Fortaleza, CE (Brazil); De Almeida, Adriana M, E-mail: corso@cb.ufrn.br [Departamento de Botanica, Ecologia e Zoologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, UFRN - Campus Universitario, Lagoa Nova, CEP 59078 972, Natal, RN (Brazil)

2011-03-01

Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index {nu}. The index {nu} is found using the relation {nu} = 1 - {tau} where {tau} is the temperature of the adjacency matrix of the bipartite network. By its turn {tau} is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index {nu} is a function of the connectivities of the bipartite network. In addition we find a concise way to find {nu} which avoid cumbersome algorithm manupulation of the adjacency matrix.

Optimizing network connectivity for mobile health technologies in sub-Saharan Africa.

Science.gov (United States)

Siedner, Mark J; Lankowski, Alexander; Musinga, Derrick; Jackson, Jonathon; Muzoora, Conrad; Hunt, Peter W; Martin, Jeffrey N; Bangsberg, David R; Haberer, Jessica E

2012-01-01

Mobile health (mHealth) technologies hold incredible promise to improve healthcare delivery in resource-limited settings. Network reliability across large catchment areas can be a major challenge. We performed an analysis of network failure frequency as part of a study of real-time adherence monitoring in rural Uganda. We hypothesized that the addition of short messaging service (SMS+GPRS) to the standard cellular network modality (GPRS) would reduce network disruptions and improve transmission of data. Participants were enrolled in a study of real-time adherence monitoring in southwest Uganda. In June 2011, we began using Wisepill devices that transmit data each time the pill bottle is opened. We defined network failures as medication interruptions of >48 hours duration that were transmitted when network connectivity was re-established. During the course of the study, we upgraded devices from GPRS to GPRS+SMS compatibility. We compared network failure rates between GPRS and GPRS+SMS periods and created geospatial maps to graphically demonstrate patterns of connectivity. One hundred fifty-seven participants met inclusion criteria of seven days of SMS and seven days of SMS+GPRS observation time. Seventy-three percent were female, median age was 40 years (IQR 33-46), 39% reported >1-hour travel time to clinic and 17% had home electricity. One hundred one had GPS coordinates recorded and were included in the geospatial maps. The median number of network failures per person-month for the GPRS and GPRS+SMS modalities were 1.5 (IQR 1.0-2.2) and 0.3 (IQR 0-0.9) respectively, (mean difference 1.2, 95%CI 1.0-1.3, p-valueImprovements in network connectivity were notable throughout the region. Study costs increased by approximately $1USD per person-month. Addition of SMS to standard GPRS cellular network connectivity can significantly reduce network connection failures for mobile health applications in remote areas. Projects depending on mobile health data in resource

Network topology and functional connectivity disturbances precede the onset of Huntington's disease.

Science.gov (United States)

Harrington, Deborah L; Rubinov, Mikail; Durgerian, Sally; Mourany, Lyla; Reece, Christine; Koenig, Katherine; Bullmore, Ed; Long, Jeffrey D; Paulsen, Jane S; Rao, Stephen M

2015-08-01

Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease

Connectivity diagnostics in the Mediterranean obtained from Lagrangian Flow Networks; global patterns, sensitivity and robustness

Science.gov (United States)

Monroy, Pedro; Rossi, Vincent; Ser-Giacomi, Enrico; López, Cristóbal; Hernández-García, Emilio

2017-04-01

Lagrangian Flow Network (LFN) is a modeling framework in which geographical sub-areas of the ocean are represented as nodes in a network and are interconnected by links representing the transport of water, substances or propagules (eggs and larvae) by currents. Here we compute for the surface of the whole Mediterranean basin four connectivity metrics derived from LFN that measure retention and exchange processes, thus providing a systematic characterization of propagule dispersal driven by the ocean circulation. Then we assess the sensitivity and robustness of the results with respect to the most relevant parameters: the density of released particles, the node size (spatial-scales of discretization), the Pelagic Larval Duration (PLD) and the modality of spawning. We find a threshold for the number of particles per node that guarantees reliable values for most of the metrics examined, independently of node size. For our setup, this threshold is 100 particles per node. We also find that the size of network nodes has a non-trivial influence on the spatial variability of both exchange and retention metrics. Although the spatio-temporal fluctuations of the circulation affect larval transport in a complex and unpredictable manner, our analyses evidence how specific biological parametrization impact the robustness of connectivity diagnostics. Connectivity estimates for long PLDs are more robust against biological uncertainties (PLD and spawning date) than for short PLDs. Furthermore, our model suggests that for mass-spawners that release propagules over short periods (≃ 2 to 10 days), daily release must be simulated to properly consider connectivity fluctuations. In contrast, average connectivity estimates for species that spawn repeatedly over longer duration (a few weeks to a few months) remain robust even using longer periodicity (5 to 10 days). Our results give a global view of the surface connectivity of the Mediterranean Sea and have implications for the design of

Spatio-Temporal Patterns of the International Merger and Acquisition Network.

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Dueñas, Marco; Mastrandrea, Rossana; Barigozzi, Matteo; Fagiolo, Giorgio

2017-09-07

This paper analyses the world web of mergers and acquisitions (M&As) using a complex network approach. We use data of M&As to build a temporal sequence of binary and weighted-directed networks for the period 1995-2010 and 224 countries (nodes) connected according to their M&As flows (links). We study different geographical and temporal aspects of the international M&A network (IMAN), building sequences of filtered sub-networks whose links belong to specific intervals of distance or time. Given that M&As and trade are complementary ways of reaching foreign markets, we perform our analysis using statistics employed for the study of the international trade network (ITN), highlighting the similarities and differences between the ITN and the IMAN. In contrast to the ITN, the IMAN is a low density network characterized by a persistent giant component with many external nodes and low reciprocity. Clustering patterns are very heterogeneous and dynamic. High-income economies are the main acquirers and are characterized by high connectivity, implying that most countries are targets of a few acquirers. Like in the ITN, geographical distance strongly impacts the structure of the IMAN: link-weights and node degrees have a non-linear relation with distance, and an assortative pattern is present at short distances.

Bio-inspired patterned networks (BIPS) for development of wearable/disposable biosensors

Science.gov (United States)

McLamore, E. S.; Convertino, M.; Hondred, John; Das, Suprem; Claussen, J. C.; Vanegas, D. C.; Gomes, C.

2016-05-01

Here we demonstrate a novel approach for fabricating point of care (POC) wearable electrochemical biosensors based on 3D patterning of bionanocomposite networks. To create Bio-Inspired Patterned network (BIPS) electrodes, we first generate fractal network in silico models that optimize transport of network fluxes according to an energy function. Network patterns are then inkjet printed onto flexible substrate using conductive graphene ink. We then deposit fractal nanometal structures onto the graphene to create a 3D nanocomposite network. Finally, we biofunctionalize the surface with biorecognition agents using covalent bonding. In this paper, BIPS are used to develop high efficiency, low cost biosensors for measuring glucose as a proof of concept. Our results on the fundamental performance of BIPS sensors show that the biomimetic nanostructures significantly enhance biosensor sensitivity, accuracy, response time, limit of detection, and hysteresis compared to conventional POC non fractal electrodes (serpentine, interdigitated, and screen printed electrodes). BIPs, in particular Apollonian patterned BIPS, represent a new generation of POC biosensors based on nanoscale and microscale fractal networks that significantly improve electrical connectivity, leading to enhanced sensor performance.

Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.

Science.gov (United States)

Vecchio, Fabrizio; Miraglia, Francesca; Bramanti, Placido; Rossini, Paolo Maria

2014-01-01

Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.

Are we connected? : Ports in Global Networks

NARCIS (Netherlands)

R.A. Zuidwijk (Rob)

2015-01-01

markdownabstractAbstract Global supply chains are built on organizational, information, and logistics networks. Ports are connected via these networks and also need to connect these networks. Synchromodality is an innovative concept for container transportation, and the port plays an important

Synchronization transmission of laser pattern signal within uncertain switched network

Science.gov (United States)

Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

2017-06-01

We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.

Active patterning and asymmetric transport in a model actomyosin network

Energy Technology Data Exchange (ETDEWEB)

Wang, Shenshen [Department of Chemical Engineering and Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Wolynes, Peter G. [Department of Chemistry and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005 (United States)

2013-12-21

Cytoskeletal networks, which are essentially motor-filament assemblies, play a major role in many developmental processes involving structural remodeling and shape changes. These are achieved by nonequilibrium self-organization processes that generate functional patterns and drive intracellular transport. We construct a minimal physical model that incorporates the coupling between nonlinear elastic responses of individual filaments and force-dependent motor action. By performing stochastic simulations we show that the interplay of motor processes, described as driving anti-correlated motion of the network vertices, and the network connectivity, which determines the percolation character of the structure, can indeed capture the dynamical and structural cooperativity which gives rise to diverse patterns observed experimentally. The buckling instability of individual filaments is found to play a key role in localizing collapse events due to local force imbalance. Motor-driven buckling-induced node aggregation provides a dynamic mechanism that stabilizes the two-dimensional patterns below the apparent static percolation limit. Coordinated motor action is also shown to suppress random thermal noise on large time scales, the two-dimensional configuration that the system starts with thus remaining planar during the structural development. By carrying out similar simulations on a three-dimensional anchored network, we find that the myosin-driven isotropic contraction of a well-connected actin network, when combined with mechanical anchoring that confers directionality to the collective motion, may represent a novel mechanism of intracellular transport, as revealed by chromosome translocation in the starfish oocyte.

Simulation of dynamic expansion, contraction, and connectivity in a mountain stream network

Science.gov (United States)

Ward, Adam S.; Schmadel, Noah M.; Wondzell, Steven M.

2018-04-01

Headwater stream networks expand and contract in response to changes in stream discharge. The changes in the extent of the stream network are also controlled by geologic or geomorphic setting - some reaches go dry even under relatively wet conditions, other reaches remain flowing under relatively dry conditions. While such patterns are well recognized, we currently lack tools to predict the extent of the stream network and the times and locations where the network is dry within large river networks. Here, we develop a perceptual model of the river corridor in a headwater mountainous catchment, translate this into a reduced-complexity mechanistic model, and implement the model to examine connectivity and network extent over an entire water year. Our model agreed reasonably well with our observations, showing that the extent and connectivity of the river network was most sensitive to hydrologic forcing under the lowest discharges (Qgauge 10 L s-1) the extent of the network was relatively insensitive to hydrologic forcing and was instead determined by the network topology. We do not expect that the specific thresholds observed in this study would be transferable to other catchments with different geology, topology, or hydrologic forcing. However, we expect that the general pattern should be robust: the dominant controls will shift from hydrologic forcing to geologic setting as discharge increases. Furthermore, our method is readily transferable as the model can be applied with minimal data requirements (a single stream gauge, a digital terrain model, and estimates of hydrogeologic properties) to estimate flow duration or connectivity along the river corridor in unstudied catchments. As the available information increases, the model could be better calibrated to match site-specific observations of network extent, locations of dry reaches, or solute break through curves as demonstrated in this study. Based on the low initial data requirements and ability to later tune

Aberrant functional connectivity of default-mode network in type 2 diabetes patients

International Nuclear Information System (INIS)

Cui, Ying; Jiao, Yun; Chen, Hua-Jun; Ding, Jie; Luo, Bing; Peng, Cheng-Yu; Ju, Sheng-Hong; Teng, Gao-Jun

2015-01-01

Type 2 diabetes mellitus is associated with increased risk for dementia. Patients with impaired cognition often show default-mode network disruption. We aimed to investigate the integrity of a default-mode network in diabetic patients by using independent component analysis, and to explore the relationship between network abnormalities, neurocognitive performance and diabetic variables. Forty-two patients with type 2 diabetes and 42 well-matched healthy controls were included and underwent resting-state functional MRI in a 3 Tesla unit. Independent component analysis was adopted to extract the default-mode network, including its anterior and posterior components. Z-maps of both sub-networks were compared between the two groups and correlated with each clinical variable. Patients showed increased connectivity around the medial prefrontal cortex in the anterior sub-network, but decreased connectivity around the posterior cingulate cortex in the posterior sub-network. The decreased connectivity in the posterior part was significantly correlated with the score on Complex Figure Test-delay recall test (r = 0.359, p = 0.020), the time spent on Trail-Making Test-part B (r = -0.346, p = 0.025) and the insulin resistance level (r = -0.404, p = 0.024). Dissociation pattern in the default-mode network was found in diabetic patients, which might provide powerful new insights into the neural mechanisms that underlie the diabetes-related cognitive decline. (orig.)

Aberrant functional connectivity of default-mode network in type 2 diabetes patients

Energy Technology Data Exchange (ETDEWEB)

Cui, Ying; Jiao, Yun; Chen, Hua-Jun; Ding, Jie; Luo, Bing; Peng, Cheng-Yu; Ju, Sheng-Hong; Teng, Gao-Jun [Medical School of Southeast University, Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Nanjing, Jiangsu (China)

2015-11-15

Type 2 diabetes mellitus is associated with increased risk for dementia. Patients with impaired cognition often show default-mode network disruption. We aimed to investigate the integrity of a default-mode network in diabetic patients by using independent component analysis, and to explore the relationship between network abnormalities, neurocognitive performance and diabetic variables. Forty-two patients with type 2 diabetes and 42 well-matched healthy controls were included and underwent resting-state functional MRI in a 3 Tesla unit. Independent component analysis was adopted to extract the default-mode network, including its anterior and posterior components. Z-maps of both sub-networks were compared between the two groups and correlated with each clinical variable. Patients showed increased connectivity around the medial prefrontal cortex in the anterior sub-network, but decreased connectivity around the posterior cingulate cortex in the posterior sub-network. The decreased connectivity in the posterior part was significantly correlated with the score on Complex Figure Test-delay recall test (r = 0.359, p = 0.020), the time spent on Trail-Making Test-part B (r = -0.346, p = 0.025) and the insulin resistance level (r = -0.404, p = 0.024). Dissociation pattern in the default-mode network was found in diabetic patients, which might provide powerful new insights into the neural mechanisms that underlie the diabetes-related cognitive decline. (orig.)

Population Connectivity Measures of Fishery-Targeted Coral Reef Species to Inform Marine Reserve Network Design in Fiji.

Science.gov (United States)

Eastwood, Erin K; López, Elora H; Drew, Joshua A

2016-01-25

Coral reef fish serve as food sources to coastal communities worldwide, yet are vulnerable to mounting anthropogenic pressures like overfishing and climate change. Marine reserve networks have become important tools for mitigating these pressures, and one of the most critical factors in determining their spatial design is the degree of connectivity among different populations of species prioritized for protection. To help inform the spatial design of an expanded reserve network in Fiji, we used rapidly evolving mitochondrial genes to investigate connectivity patterns of three coral reef species targeted by fisheries in Fiji: Epinephelus merra (Serranidae), Halichoeres trimaculatus (Labridae), and Holothuria atra (Holothuriidae). The two fish species, E. merra and Ha. trimaculatus, exhibited low genetic structuring and high amounts of gene flow, whereas the sea cucumber Ho. atra displayed high genetic partitioning and predominantly westward gene flow. The idiosyncratic patterns observed among these species indicate that patterns of connectivity in Fiji are likely determined by a combination of oceanographic and ecological characteristics. Our data indicate that in the cases of species with high connectivity, other factors such as representation or political availability may dictate where reserves are placed. In low connectivity species, ensuring upstream and downstream connections is critical.

Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy.

Science.gov (United States)

Fang, Peng; An, Jie; Zeng, Ling-Li; Shen, Hui; Chen, Fanglin; Wang, Wensheng; Qiu, Shijun; Hu, Dewen

2015-01-01

Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE), but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right) mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

Functional Connectivity with Distinct Neural Networks Tracks Fluctuations in Gain/Loss Framing Susceptibility

Science.gov (United States)

Smith, David V.; Sip, Kamila E.; Delgado, Mauricio R.

2016-01-01

Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility—indexed as the increase in gambling behavior in loss frames compared to gain frames—was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. PMID:25858445

Default network connectivity during a working memory task.

Science.gov (United States)

Bluhm, Robyn L; Clark, C Richard; McFarlane, Alexander C; Moores, Kathryn A; Shaw, Marnie E; Lanius, Ruth A

2011-07-01

The default network exhibits correlated activity at rest and has shown decreased activation during performance of cognitive tasks. There has been little investigation of changes in connectivity of this network during task performance. In this study, we examined task-related modulation of connectivity between two seed regions from the default network posterior cingulated cortex (PCC) and medial prefrontal cortex (mPFC) and the rest of the brain in 12 healthy adults. The purpose was to determine (1) whether connectivity within the default network differs between a resting state and performance of a cognitive (working memory) task and (2) whether connectivity differs between these nodes of the default network and other brain regions, particularly those implicated in cognitive tasks. There was little change in connectivity with the other main areas of the default network for either seed region, but moderate task-related changes in connectivity occurred between seed regions and regions outside the default network. For example, connectivity of the mPFC with the right insula and the right superior frontal gyrus decreased during task performance. Increased connectivity during the working memory task occurred between the PCC and bilateral inferior frontal gyri, and between the mPFC and the left inferior frontal gyrus, cuneus, superior parietal lobule, middle temporal gyrus and cerebellum. Overall, the areas showing greater correlation with the default network seed regions during task than at rest have been previously implicated in working memory tasks. These changes may reflect a decrease in the negative correlations occurring between the default and task-positive networks at rest. Copyright © 2010 Wiley-Liss, Inc.

Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

Science.gov (United States)

Rich, Scott; Zochowski, Michal; Booth, Victoria

2018-01-01

Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

Social Network Mixing Patterns In Mergers & Acquisitions - A Simulation Experiment

Directory of Open Access Journals (Sweden)

Robert Fabac

2011-01-01

Full Text Available In the contemporary world of global business and continuously growing competition, organizations tend to use mergers and acquisitions to enforce their position on the market. The future organization’s design is a critical success factor in such undertakings. The field of social network analysis can enhance our uderstanding of these processes as it lets us reason about the development of networks, regardless of their origin. The analysis of mixing patterns is particularly useful as it provides an insight into how nodes in a network connect with each other. We hypothesize that organizational networks with compatible mixing patterns will be integrated more successfully. After conducting a simulation experiment, we suggest an integration model based on the analysis of network assortativity. The model can be a guideline for organizational integration, such as occurs in mergers and acquisitions.

Slowly evolving connectivity in recurrent neural networks: I. The extreme dilution regime

International Nuclear Information System (INIS)

Wemmenhove, B; Skantzos, N S; Coolen, A C C

2004-01-01

We study extremely diluted spin models of neural networks in which the connectivity evolves in time, although adiabatically slowly compared to the neurons, according to stochastic equations which on average aim to reduce frustration. The (fast) neurons and (slow) connectivity variables equilibrate separately, but at different temperatures. Our model is exactly solvable in equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e. recall of one pattern). These show that, as the connectivity temperature is lowered, the volume of the retrieval phase diverges and the fraction of mis-aligned spins is reduced. Still one always retains a region in the retrieval phase where recall states other than the one corresponding to the 'condensed' pattern are locally stable, so the associative memory character of our model is preserved

Altered network hub connectivity after acute LSD administration

Directory of Open Access Journals (Sweden)

Felix Müller

Full Text Available LSD is an ambiguous substance, said to mimic psychosis and to improve mental health in people suffering from anxiety and depression. Little is known about the neuronal correlates of altered states of consciousness induced by this substance. Limited previous studies indicated profound changes in functional connectivity of resting state networks after the administration of LSD. The current investigation attempts to replicate and extend those findings in an independent sample. In a double-blind, randomized, cross-over study, 100 μg LSD and placebo were orally administered to 20 healthy participants. Resting state brain activity was assessed by functional magnetic resonance imaging. Within-network and between-network connectivity measures of ten established resting state networks were compared between drug conditions. Complementary analysis were conducted using resting state networks as sources in seed-to-voxel analyses. Acute LSD administration significantly decreased functional connectivity within visual, sensorimotor and auditory networks and the default mode network. While between-network connectivity was widely increased and all investigated networks were affected to some extent, seed-to-voxel analyses consistently indicated increased connectivity between networks and subcortical (thalamus, striatum and cortical (precuneus, anterior cingulate cortex hub structures. These latter observations are consistent with findings on the importance of hubs in psychopathological states, especially in psychosis, and could underlay therapeutic effects of hallucinogens as proposed by a recent model. Keywords: LSD, fMRI, Functional connectivity, Networks, Hubs

Altered patterns of directed connectivity within the reading network of dyslexic children and their relation to reading dysfluency

Directory of Open Access Journals (Sweden)

Gojko Žarić

2017-02-01

Full Text Available Reading is a complex cognitive skill subserved by a distributed network of visual and language-related regions. Disruptions of connectivity within this network have been associated with developmental dyslexia but their relation to individual differences in the severity of reading problems remains unclear. Here we investigate whether dysfunctional connectivity scales with the level of reading dysfluency by examining EEG recordings during visual word and false font processing in 9-year-old typically reading children (TR and two groups of dyslexic children: severely dysfluent (SDD and moderately dysfluent (MDD dyslexics. Results indicated weaker occipital to inferior-temporal connectivity for words in both dyslexic groups relative to TRs. Furthermore, SDDs exhibited stronger connectivity from left central to right inferior-temporal and occipital sites for words relative to TRs, and for false fonts relative to both MDDs and TRs. Importantly, reading fluency was positively related with forward and negatively with backward connectivity. Our results suggest disrupted visual processing of words in both dyslexic groups, together with a compensatory recruitment of right posterior brain regions especially in the SDDs during word and false font processing. Functional connectivity in the brain’s reading network may thus depend on the level of reading dysfluency beyond group differences between dyslexic and typical readers.

Generalised power graph compression reveals dominant relationship patterns in complex networks.

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Ahnert, Sebastian E

2014-03-25

We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the dominant relationship patterns that define the network. In addition, this procedure also gives rise to a novel, link-based definition of overlapping node communities in which nodes are defined by their relationships with sets of other nodes, rather than through connections within the community. We show that this completely general approach can be applied to undirected, directed, and bipartite networks, yielding valuable insights into the large-scale structure of real-world networks, including social networks and food webs. Our approach therefore provides a novel way in which network architecture can be studied, defined and classified.

Organization of anti-phase synchronization pattern in neural networks: what are the key factors?

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Dong eLi

2011-12-01

Full Text Available Anti-phase oscillation has been widely observed in cortical neuralnetwork. Elucidating the mechanism underlying the organization ofanti-phase pattern is of significance for better understanding morecomplicated pattern formations in brain networks. In dynamicalsystems theory, the organization of anti-phase oscillation patternhas usually been considered to relate to time-delay in coupling.This is consistent to conduction delays in real neural networks inthe brain due to finite propagation velocity of action potentials.However, other structural factors in cortical neural network, suchas modular organization (connection density and the coupling types(excitatory or inhibitory, could also play an important role. Inthis work, we investigate the anti-phase oscillation patternorganized on a two-module network of either neuronal cell model orneural mass model, and analyze the impact of the conduction delaytimes, the connection densities, and coupling types. Our resultsshow that delay times and coupling types can play key roles in thisorganization. The connection densities may have an influence on thestability if an anti-phase pattern exists due to the other factors.Furthermore, we show that anti-phase synchronization of slowoscillations can be achieved with small delay times if there isinteraction between slow and fast oscillations. These results aresignificant for further understanding more realistic spatiotemporaldynamics of cortico-cortical communications.

The brain matures with stronger functional connectivity and decreased randomness of its network.

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Dirk J A Smit

Full Text Available We investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz' (1998 graph parameters C (local clustering and L (global path length for alpha (~10 Hz, beta (~20 Hz, and theta (~4 Hz oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ~50 yrs. Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ~18 yrs. Older age (55+ was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05, while path length was related to both white matter (alpha: max. r = 38, p<001 and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001 volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain.

Replicability of time-varying connectivity patterns in large resting state fMRI samples.

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Abrol, Anees; Damaraju, Eswar; Miller, Robyn L; Stephen, Julia M; Claus, Eric D; Mayer, Andrew R; Calhoun, Vince D

2017-12-01

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Stability of a neural network model with small-world connections

International Nuclear Information System (INIS)

Li Chunguang; Chen Guanrong

2003-01-01

Small-world networks are highly clustered networks with small distances among the nodes. There are many biological neural networks that present this kind of connection. There are no special weightings in the connections of most existing small-world network models. However, this kind of simply connected model cannot characterize biological neural networks, in which there are different weights in synaptic connections. In this paper, we present a neural network model with weighted small-world connections and further investigate the stability of this model

Shifted intrinsic connectivity of central executive and salience network in borderline personality disorder

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Anselm eDoll

2013-10-01

Full Text Available Borderline personality disorder (BPD is characterized by stable instability of emotions and behavior and their regulation. This emotional and behavioral instability corresponds with a neurocognitive triple network model of psychopathology, which suggests that aberrant emotional saliency and cognitive control is associated with aberrant interaction across three intrinsic connectivity networks (ICN (i.e. the salience, default mode, and central executive network, SN, DMN, CEN. The objective of the current study was to investigate whether and how such triple network intrinsic functional connectivity (iFC is changed in patients with BPD. We acquired resting-state functional magnetic resonance imaging (rs-fMRI data from fourteen patients with BPD and sixteen healthy controls (HC. High-model order independent component analysis (ICA was used to extract spatiotemporal patterns of ongoing, coherent blood-oxygen-level-dependent (BOLD signal fluctuations from rs-fMRI data. Main outcome measures were iFC within networks (intra-iFC and between networks (i.e. network time course correlation inter-iFC.Aberrant intra-iFC was found in patients’ DMN, SN, and CEN, consistent with previous findings. While patients’ inter-iFC of the CEN was decreased, inter-iFC of the SN was increased. In particular, a balance index reflecting the relationship of CEN-and SN-inter-iFC across networks was strongly shifted from CEN to SN connectivity in patients. Results provide first preliminary evidence for aberrant triple network intrinsic functional connectivity in BPD. Our data suggest a shift of inter-network iFC from networks involved in cognitive control to those of emotion-related activity in BPD, potentially reflecting the persistent instability of emotion regulation in patients.

Multivariate pattern analysis reveals anatomical connectivity differences between the left and right mesial temporal lobe epilepsy

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Peng Fang

2015-01-01

Full Text Available Previous studies have demonstrated differences of clinical signs and functional brain network organizations between the left and right mesial temporal lobe epilepsy (mTLE, but the anatomical connectivity differences underlying functional variance between the left and right mTLE remain uncharacterized. We examined 43 (22 left, 21 right mTLE patients with hippocampal sclerosis and 39 healthy controls using diffusion tensor imaging. After the whole-brain anatomical networks were constructed for each subject, multivariate pattern analysis was applied to classify the left mTLE from the right mTLE and extract the anatomical connectivity differences between the left and right mTLE patients. The classification results reveal 93.0% accuracy for the left mTLE versus the right mTLE, 93.4% accuracy for the left mTLE versus controls and 90.0% accuracy for the right mTLE versus controls. Compared with the right mTLE, the left mTLE exhibited a different connectivity pattern in the cortical-limbic network and cerebellum. The majority of the most discriminating anatomical connections were located within or across the cortical-limbic network and cerebellum, thereby indicating that these disease-related anatomical network alterations may give rise to a portion of the complex of emotional and memory deficit between the left and right mTLE. Moreover, the orbitofrontal gyrus, cingulate cortex, hippocampus and parahippocampal gyrus, which exhibit high discriminative power in classification, may play critical roles in the pathophysiology of mTLE. The current study demonstrated that anatomical connectivity differences between the left mTLE and the right mTLE may have the potential to serve as a neuroimaging biomarker to guide personalized diagnosis of the left and right mTLE.

Ecological connectivity networks in rapidly expanding cities.

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Nor, Amal Najihah M; Corstanje, Ron; Harris, Jim A; Grafius, Darren R; Siriwardena, Gavin M

2017-06-01

Urban expansion increases fragmentation of the landscape. In effect, fragmentation decreases connectivity, causes green space loss and impacts upon the ecology and function of green space. Restoration of the functionality of green space often requires restoring the ecological connectivity of this green space within the city matrix. However, identifying ecological corridors that integrate different structural and functional connectivity of green space remains vague. Assessing connectivity for developing an ecological network by using efficient models is essential to improve these networks under rapid urban expansion. This paper presents a novel methodological approach to assess and model connectivity for the Eurasian tree sparrow ( Passer montanus ) and Yellow-vented bulbul ( Pycnonotus goiavier ) in three cities (Kuala Lumpur, Malaysia; Jakarta, Indonesia and Metro Manila, Philippines). The approach identifies potential priority corridors for ecological connectivity networks. The study combined circuit models, connectivity analysis and least-cost models to identify potential corridors by integrating structure and function of green space patches to provide reliable ecological connectivity network models in the cities. Relevant parameters such as landscape resistance and green space structure (vegetation density, patch size and patch distance) were derived from an expert and literature-based approach based on the preference of bird behaviour. The integrated models allowed the assessment of connectivity for both species using different measures of green space structure revealing the potential corridors and least-cost pathways for both bird species at the patch sites. The implementation of improvements to the identified corridors could increase the connectivity of green space. This study provides examples of how combining models can contribute to the improvement of ecological networks in rapidly expanding cities and demonstrates the usefulness of such models for

Ecological connectivity networks in rapidly expanding cities

Directory of Open Access Journals (Sweden)

Amal Najihah M. Nor

2017-06-01

Full Text Available Urban expansion increases fragmentation of the landscape. In effect, fragmentation decreases connectivity, causes green space loss and impacts upon the ecology and function of green space. Restoration of the functionality of green space often requires restoring the ecological connectivity of this green space within the city matrix. However, identifying ecological corridors that integrate different structural and functional connectivity of green space remains vague. Assessing connectivity for developing an ecological network by using efficient models is essential to improve these networks under rapid urban expansion. This paper presents a novel methodological approach to assess and model connectivity for the Eurasian tree sparrow (Passer montanus and Yellow-vented bulbul (Pycnonotus goiavier in three cities (Kuala Lumpur, Malaysia; Jakarta, Indonesia and Metro Manila, Philippines. The approach identifies potential priority corridors for ecological connectivity networks. The study combined circuit models, connectivity analysis and least-cost models to identify potential corridors by integrating structure and function of green space patches to provide reliable ecological connectivity network models in the cities. Relevant parameters such as landscape resistance and green space structure (vegetation density, patch size and patch distance were derived from an expert and literature-based approach based on the preference of bird behaviour. The integrated models allowed the assessment of connectivity for both species using different measures of green space structure revealing the potential corridors and least-cost pathways for both bird species at the patch sites. The implementation of improvements to the identified corridors could increase the connectivity of green space. This study provides examples of how combining models can contribute to the improvement of ecological networks in rapidly expanding cities and demonstrates the usefulness of such

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

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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.

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks.

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Kozák, Lajos R; van Graan, Louis André; Chaudhary, Umair J; Szabó, Ádám György; Lemieux, Louis

2017-12-01

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Pramipexole Modulates Interregional Connectivity Within the Sensorimotor Network.

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Ye, Zheng; Hammer, Anke; Münte, Thomas F

2017-05-01

Pramipexole is widely prescribed to treat Parkinson's disease but has been reported to cause impulse control disorders such as pathological gambling. Recent neurocomputational models suggested that D2 agonists may distort functional connections between the striatum and the motor cortex, resulting in impaired reinforcement learning and pathological gambling. To examine how D2 agonists modulate the striatal-motor connectivity, we carried out a pharmacological resting-state functional magnetic resonance imaging study with a double-blind randomized within-subject crossover design. We analyzed the medication-induced changes of network connectivity and topology with two approaches, an independent component analysis (ICA) and a graph theoretical analysis (GTA). The ICA identified the sensorimotor network (SMN) as well as other classical resting-state networks. Within the SMN, the connectivity between the right caudate nucleus and other cortical regions was weaker under pramipexole than under placebo. The GTA measured the topological properties of the whole-brain network at global and regional levels. Both the whole-brain network under placebo and that under pramipexole were identified as small-world networks. The two whole-brain networks were similar in global efficiency, clustering coefficient, small-world index, and modularity. However, the degree of the right caudate nucleus decreased under pramipexole mainly due to the loss of the connectivity with the supplementary motor area, paracentral lobule, and precentral and postcentral gyrus of the SMN. The two network analyses consistently revealed that pramipexole weakened the functional connectivity between the caudate nucleus and the SMN regions.

Selectivity and sparseness in randomly connected balanced networks.

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Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

Dual connectivity for LTE-advanced heterogeneous networks

DEFF Research Database (Denmark)

Wang, Hua; Rosa, Claudio; Pedersen, Klaus I.

2016-01-01

Dual connectivity (DC) allows user equipments (UEs) to receive data simultaneously from different eNodeBs (eNBs) in order to boost the performance in a heterogeneous network with dedicated carrier deployment. Yet, how to efficiently operate with DC opens a number of research questions. In this pa......Dual connectivity (DC) allows user equipments (UEs) to receive data simultaneously from different eNodeBs (eNBs) in order to boost the performance in a heterogeneous network with dedicated carrier deployment. Yet, how to efficiently operate with DC opens a number of research questions...... aggregation (CA) and virtually zerolatency fronthaul connections, and in any case it is significantly higher compared to the case without DC. Keywords: Dual connectivity Heterogeneous network LTE-advanced Radio resource management Performance evaluation...

Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients.

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Zhu, Xueling; Wang, Xiang; Xiao, Jin; Liao, Jian; Zhong, Mingtian; Wang, Wei; Yao, Shuqiao

2012-04-01

Imaging studies have shown that major depressive disorder (MDD) is associated with altered activity patterns of the default mode network (DMN). However, the neural correlates of the resting-state DMN and MDD-related pathopsychological characteristics, such as depressive rumination and overgeneral autobiographical memory (OGM) phenomena, still remain unclear. Using independent component analysis, we analyzed resting-state functional magnetic resonance imaging data obtained from 35 first-episode, treatment-naive young adults with MDD and from 35 matched healthy control subjects. Patients with MDD exhibited higher levels of rumination and OGM than did the control subjects. We observed increased functional connectivity in the anterior medial cortex regions (especially the medial prefrontal cortex and anterior cingulate cortex) and decreased functional connectivity in the posterior medial cortex regions (especially the posterior cingulate cortex/precuneus) in MDD patients compared with control subjects. In the depressed group, the increased functional connectivity in the anterior medial cortex correlated positively with rumination score, while the decreased functional connectivity in the posterior medial cortex correlated negatively with OGM score. We report dissociation between anterior and posterior functional connectivity in resting-state DMNs of first-episode, treatment-naive young adults with MDD. Increased functional connectivity in anterior medial regions of the resting-state DMN was associated with rumination, whereas decreased functional connectivity in posterior medial regions was associated with OGM. These results provide new evidence for the importance of the DMN in the pathophysiology of MDD and suggest that abnormal DMN activity may be an MDD trait. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Default Mode Network Connectivity in Stroke Patients.

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Tuladhar, Anil Man; Snaphaan, Liselore; Shumskaya, Elena; Rijpkema, Mark; Fernandez, Guillén; Norris, David G; de Leeuw, Frank-Erik

2013-01-01

The pathophysiology of episodic memory dysfunction after infarction is not completely understood. It has been suggested that infarctions located anywhere in the brain can induce widespread effects causing disruption of functional networks of the cortical regions. The default mode network, which includes the medial temporal lobe, is a functional network that is associated with episodic memory processing. We investigated whether the default mode network activity is reduced in stroke patients compared to healthy control subjects in the resting state condition. We assessed the whole brain network properties during resting state functional MRI in 21 control subjects and 20 'first-ever' stroke patients. Patients were scanned 9-12 weeks after stroke onset. Stroke lesions were located in various parts of the brain. Independent component analyses were conducted to identify the default mode network and to compare the group differences of the default mode network. Furthermore, region-of-interest based analysis was performed to explore the functional connectivity between the regions of the default mode network. Stroke patients performed significantly worse than control subjects on the delayed recall score on California verbal learning test. We found decreased functional connectivity in the left medial temporal lobe, posterior cingulate and medial prefrontal cortical areas within the default mode network and reduced functional connectivity between these regions in stroke patients compared with controls. There were no significant volumetric differences between the groups. These results demonstrate that connectivity within the default mode network is reduced in 'first-ever' stroke patients compared to control subjects. This phenomenon might explain the occurrence of post-stroke cognitive dysfunction in stroke patients.

Connectivity analysis of one-dimensional ad-hoc networks

DEFF Research Database (Denmark)

Hansen, Martin Bøgsted; Rasmussen, Jakob Gulddahl; Schwefel, Hans-Peter

hop-count; (3) the connectivity distance, expressing the geographic distance that a message can be propagated in the network on multi-hop paths; (4) the connectivity hops, which corresponds to the number of hops that are necessary to reach all nodes in the connected network. The paper develops...

Global patterns of interaction specialization in bird-flower networks

DEFF Research Database (Denmark)

Zanata, Thais B.; Dalsgaard, Bo; Passos, Fernando C.

2017-01-01

, such as plant species richness, asymmetry, latitude, insularity, topography, sampling methods and intensity. Results: Hummingbird–flower networks were more specialized than honeyeater–flower networks. Specifically, hummingbird–flower networks had a lower proportion of realized interactions (lower C), decreased...... in the interaction patterns with their floral resources. Location: Americas, Africa, Asia and Oceania/Australia. Methods: We compiled interaction networks between birds and floral resources for 79 hummingbird, nine sunbird and 33 honeyeater communities. Interaction specialization was quantified through connectance...... (C), complementary specialization (H2′), binary (QB) and weighted modularity (Q), with both observed and null-model corrected values. We compared interaction specialization among the three types of bird–flower communities, both independently and while controlling for potential confounding variables...

Functional Connectivity of Precipitation Networks in the Brazilian Rainforest-Savanna Transition Zone

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Adera, S.; Larsen, L.; Levy, M. C.; Thompson, S. E.

2016-12-01

In the Brazilian rainforest-savanna transition zone, vegetation change has the potential to significantly affect precipitation patterns. Deforestation, in particular, can affect precipitation patterns by increasing land surface albedo, increasing aerosol loading to the atmosphere, changing land surface roughness, and reducing transpiration. Understanding land surface-precipitation couplings in this region is important not only for sustaining Amazon and Cerrado ecosystems, but also for cattle ranching and agriculture, hydropower generation, and drinking water management. Simulations suggest complex, scale-dependent interactions between precipitation and land cover. For example, the size and distribution of deforested patches has been found to affect precipitation patterns. We take an empirical approach to ask: (1) what are the dominant spatial and temporal length scales of precipitation coupling in the Brazilian rainforest-savanna transition zone? (2) How do these length scales change over time? (3) How does the connectivity of precipitation change over time? The answers to these questions will help address fundamental questions about the impacts of deforestation on precipitation. We use rain gauge data from 1100 rain gauges intermittently covering the period 1980 - 2013, a period of intensive land cover change in the region. The dominant spatial and temporal length scales of precipitation coupling are resolved using transfer entropy, a metric from information theory. Connectivity of the emergent network of couplings is quantified using network statistics. Analyses using transfer entropy and network statistics reveal the spatial and temporal interdependencies of rainfall events occurring in different parts of the study domain.

Altered Effective Connectivity of Hippocampus-Dependent Episodic Memory Network in mTBI Survivors

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Hao Yan

2016-01-01

Full Text Available Traumatic brain injuries (TBIs are generally recognized to affect episodic memory. However, less is known regarding how external force altered the way functionally connected brain structures of the episodic memory system interact. To address this issue, we adopted an effective connectivity based analysis, namely, multivariate Granger causality approach, to explore causal interactions within the brain network of interest. Results presented that TBI induced increased bilateral and decreased ipsilateral effective connectivity in the episodic memory network in comparison with that of normal controls. Moreover, the left anterior superior temporal gyrus (aSTG, the concept forming hub, left hippocampus (the personal experience binding hub, and left parahippocampal gyrus (the contextual association hub were no longer network hubs in TBI survivors, who compensated for hippocampal deficits by relying more on the right hippocampus (underlying perceptual memory and the right medial frontal gyrus (MeFG in the anterior prefrontal cortex (PFC. We postulated that the overrecruitment of the right anterior PFC caused dysfunction of the strategic component of episodic memory, which caused deteriorating episodic memory in mTBI survivors. Our findings also suggested that the pattern of brain network changes in TBI survivors presented similar functional consequences to normal aging.

Default network connectivity as a vulnerability marker for obsessive compulsive disorder.

Science.gov (United States)

Peng, Z W; Xu, T; He, Q H; Shi, C Z; Wei, Z; Miao, G D; Jing, J; Lim, K O; Zuo, X N; Chan, R C K

2014-05-01

Aberrant functional connectivity within the default network is generally assumed to be involved in the pathophysiology of obsessive compulsive disorder (OCD); however, the genetic risk of default network connectivity in OCD remains largely unknown. Here, we systematically investigated default network connectivity in 15 OCD patients, 15 paired unaffected siblings and 28 healthy controls. We sought to examine the profiles of default network connectivity in OCD patients and their siblings, exploring the correlation between abnormal default network connectivity and genetic risk for this population. Compared with healthy controls, OCD patients exhibited reduced strength of default network functional connectivity with the posterior cingulate cortex (PCC), and increased functional connectivity in the right inferior frontal lobe, insula, superior parietal cortex and superior temporal cortex, while their unaffected first-degree siblings only showed reduced local connectivity in the PCC. These findings suggest that the disruptions of default network functional connectivity might be associated with family history of OCD. The decreased default network connectivity in both OCD patients and their unaffected siblings may serve as a potential marker of OCD.

Reduced integration and differentiation of the imitation network in autism: A combined functional connectivity magnetic resonance imaging and diffusion-weighted imaging study.

Science.gov (United States)

Fishman, Inna; Datko, Michael; Cabrera, Yuliana; Carper, Ruth A; Müller, Ralph-Axel

2015-12-01

Converging evidence indicates that brain abnormalities in autism spectrum disorder (ASD) involve atypical network connectivity, but few studies have integrated functional with structural connectivity measures. This multimodal investigation examined functional and structural connectivity of the imitation network in children and adolescents with ASD, and its links with clinical symptoms. Resting state functional magnetic resonance imaging and diffusion-weighted imaging were performed in 35 participants with ASD and 35 typically developing controls, aged 8 to 17 years, matched for age, gender, intelligence quotient, and head motion. Within-network analyses revealed overall reduced functional connectivity (FC) between distributed imitation regions in the ASD group. Whole brain analyses showed that underconnectivity in ASD occurred exclusively in regions belonging to the imitation network, whereas overconnectivity was observed between imitation nodes and extraneous regions. Structurally, reduced fractional anisotropy and increased mean diffusivity were found in white matter tracts directly connecting key imitation regions with atypical FC in ASD. These differences in microstructural organization of white matter correlated with weaker FC and greater ASD symptomatology. Findings demonstrate atypical connectivity of the brain network supporting imitation in ASD, characterized by a highly specific pattern. This pattern of underconnectivity within, but overconnectivity outside the functional network is in contrast with typical development and suggests reduced network integration and differentiation in ASD. Our findings also indicate that atypical connectivity of the imitation network may contribute to ASD clinical symptoms, highlighting the role of this fundamental social cognition ability in the pathophysiology of ASD. © 2015 American Neurological Association.

Reef-fish larval dispersal patterns validate no-take marine reserve network connectivity that links human communities

KAUST Repository

Abesamis, Rene A.; Saenz-Agudelo, Pablo; Berumen, Michael L.; Bode, Michael; Jadloc, Claro Renato L.; Solera, Leilani A.; Villanoy, Cesar L.; Bernardo, Lawrence Patrick C.; Alcala, Angel C.; Russ, Garry R.

2017-01-01

Networks of no-take marine reserves (NTMRs) are a widely advocated strategy for managing coral reefs. However, uncertainty about the strength of population connectivity between individual reefs and NTMRs through larval dispersal remains a major

Hyper-connectivity of functional networks for brain disease diagnosis.

Science.gov (United States)

Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang

2016-08-01

Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help

Population coding in sparsely connected networks of noisy neurons.

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Tripp, Bryan P; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Electroconvulsive therapy-induced brain functional connectivity predicts therapeutic efficacy in patients with schizophrenia: a multivariate pattern recognition study.

Science.gov (United States)

Li, Peng; Jing, Ri-Xing; Zhao, Rong-Jiang; Ding, Zeng-Bo; Shi, Le; Sun, Hong-Qiang; Lin, Xiao; Fan, Teng-Teng; Dong, Wen-Tian; Fan, Yong; Lu, Lin

2017-05-11

REVEAL GOOD CANDIDATES FOR BRAIN STIMULATION: Connectivity patterns in the brain may help identify patients with schizophrenia most likely to benefit from electroconvulsive therapy. A team led by Lin Lu from Peking University, China, and Yong Fan from the University of Pennsylvania, USA, took functional magnetic resonance imaging (MRI) scans of 34 people with schizophrenia and 34 control individuals without mental illness. Those with schizophrenia were scanned before and after treatment; some received antipsychotics alone, others received medication plus electroconvulsive therapy. The researchers created organizational brain maps known as "intrinsic connectivity networks" for each individual, and showed that the neuroimaging pattern could discriminate between people with and without schizophrenia. For the schizophrenia patients, the connectivity networks taken prior to treatment also helped predict who would benefit from the brain-stimulation procedure. Such a biomarker could prove a useful diagnostic tool for clinicians.

Income change alters default mode network connectivity for adolescents in poverty

Directory of Open Access Journals (Sweden)

David G. Weissman

2018-04-01

Full Text Available Experiencing poverty during childhood and adolescence may affect brain function. However, income is dynamic, and studies have not addressed whether income change relates to brain function. In the present study, we investigated whether intrinsic functional connectivity of default mode network (DMN regions was influenced by mean family income and family income change. Parents of 68 Mexican-origin adolescents (35 females reported family income annually when adolescents were 10–16 years old. Intercept and slope of income at each of these ages were calculated for each participant. At age 16 years, adolescents completed a resting state functional neuroimaging scan. Adolescents from high and low income families did not differ in their functional connectivity, but for adolescents in families with lower incomes, their connectivity patterns depended on their income slope. Low-income adolescents whose income increased demonstrated greater connectivity between the posterior cingulate cortex (PCC and the medial prefrontal cortex (mPFC, both DMN regions, and between the PCC and the right inferior frontal gyrus. Increases in income were associated with greater connectivity of the mPFC with the right inferior frontal gyrus and the left superior parietal lobule regardless of mean income. Increases in income, especially among adolescents in poverty, may alleviate stressors, influencing the development of brain networks. Keywords: Adversity, Brain, fMRI, Resting state, Socio-economic status, Youth

Income change alters default mode network connectivity for adolescents in poverty.

Science.gov (United States)

Weissman, David G; Conger, Rand D; Robins, Richard W; Hastings, Paul D; Guyer, Amanda E

2018-04-01

Experiencing poverty during childhood and adolescence may affect brain function. However, income is dynamic, and studies have not addressed whether income change relates to brain function. In the present study, we investigated whether intrinsic functional connectivity of default mode network (DMN) regions was influenced by mean family income and family income change. Parents of 68 Mexican-origin adolescents (35 females) reported family income annually when adolescents were 10-16 years old. Intercept and slope of income at each of these ages were calculated for each participant. At age 16 years, adolescents completed a resting state functional neuroimaging scan. Adolescents from high and low income families did not differ in their functional connectivity, but for adolescents in families with lower incomes, their connectivity patterns depended on their income slope. Low-income adolescents whose income increased demonstrated greater connectivity between the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC), both DMN regions, and between the PCC and the right inferior frontal gyrus. Increases in income were associated with greater connectivity of the mPFC with the right inferior frontal gyrus and the left superior parietal lobule regardless of mean income. Increases in income, especially among adolescents in poverty, may alleviate stressors, influencing the development of brain networks. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Towards Designing PLC Networks for Ubiquitous Connectivity in Enterprises

OpenAIRE

Ali, Kamran; Pefkianakis, Ioannis; Liu, Alex X.; Kim, Kyu-Han

2016-01-01

Powerline communication (PLC) provides inexpensive, secure and high speed network connectivity, by leveraging the existing power distribution networks inside the buildings. While PLC technology has the potential to improve connectivity and is considered a key enabler for sensing, control, and automation applications in enterprises, it has been mainly deployed for improving connectivity in homes. Deploying PLCs in enterprises is more challenging since the power distribution network is more com...

Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish.

Science.gov (United States)

Teles, Magda C; Almeida, Olinda; Lopes, João S; Oliveira, Rui F

2015-10-07

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance. © 2015 The Author(s).

Accelerated Distributed Dual Averaging Over Evolving Networks of Growing Connectivity

Science.gov (United States)

Liu, Sijia; Chen, Pin-Yu; Hero, Alfred O.

2018-04-01

We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA) subgradient algorithm to evolving networks of growing connectivity and analyze the corresponding improvement in convergence rate. It is known that the convergence rate of DDA is influenced by the algebraic connectivity of the underlying network, where better connectivity leads to faster convergence. However, the impact of network topology design on the convergence rate of DDA has not been fully understood. In this paper, we begin by designing network topologies via edge selection and scheduling. For edge selection, we determine the best set of candidate edges that achieves the optimal tradeoff between the growth of network connectivity and the usage of network resources. The dynamics of network evolution is then incurred by edge scheduling. Further, we provide a tractable approach to analyze the improvement in the convergence rate of DDA induced by the growth of network connectivity. Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis. Lastly, numerical experiments show that DDA can be significantly accelerated using a sequence of well-designed networks, and our theoretical predictions are well matched to its empirical convergence behavior.

Imbalanced pattern completion vs. separation in cognitive disease: network simulations of synaptic pathologies predict a personalized therapeutics strategy

Directory of Open Access Journals (Sweden)

Hanson Jesse E

2010-08-01

Full Text Available Abstract Background Diverse Mouse genetic models of neurodevelopmental, neuropsychiatric, and neurodegenerative causes of impaired cognition exhibit at least four convergent points of synaptic malfunction: 1 Strength of long-term potentiation (LTP, 2 Strength of long-term depression (LTD, 3 Relative inhibition levels (Inhibition, and 4 Excitatory connectivity levels (Connectivity. Results To test the hypothesis that pathological increases or decreases in these synaptic properties could underlie imbalances at the level of basic neural network function, we explored each type of malfunction in a simulation of autoassociative memory. These network simulations revealed that one impact of impairments or excesses in each of these synaptic properties is to shift the trade-off between pattern separation and pattern completion performance during memory storage and recall. Each type of synaptic pathology either pushed the network balance towards intolerable error in pattern separation or intolerable error in pattern completion. Imbalances caused by pathological impairments or excesses in LTP, LTD, inhibition, or connectivity, could all be exacerbated, or rescued, by the simultaneous modulation of any of the other three synaptic properties. Conclusions Because appropriate modulation of any of the synaptic properties could help re-balance network function, regardless of the origins of the imbalance, we propose a new strategy of personalized cognitive therapeutics guided by assay of pattern completion vs. pattern separation function. Simulated examples and testable predictions of this theorized approach to cognitive therapeutics are presented.

Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

Directory of Open Access Journals (Sweden)

W. L. C. Rutten

2006-01-01

Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

Functional network connectivity underlying food processing: disturbed salience and visual processing in overweight and obese adults.

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Kullmann, Stephanie; Pape, Anna-Antonia; Heni, Martin; Ketterer, Caroline; Schick, Fritz; Häring, Hans-Ulrich; Fritsche, Andreas; Preissl, Hubert; Veit, Ralf

2013-05-01

In order to adequately explore the neurobiological basis of eating behavior of humans and their changes with body weight, interactions between brain areas or networks need to be investigated. In the current functional magnetic resonance imaging study, we examined the modulating effects of stimulus category (food vs. nonfood), caloric content of food, and body weight on the time course and functional connectivity of 5 brain networks by means of independent component analysis in healthy lean and overweight/obese adults. These functional networks included motor sensory, default-mode, extrastriate visual, temporal visual association, and salience networks. We found an extensive modulation elicited by food stimuli in the 2 visual and salience networks, with a dissociable pattern in the time course and functional connectivity between lean and overweight/obese subjects. Specifically, only in lean subjects, the temporal visual association network was modulated by the stimulus category and the salience network by caloric content, whereas overweight and obese subjects showed a generalized augmented response in the salience network. Furthermore, overweight/obese subjects showed changes in functional connectivity in networks important for object recognition, motivational salience, and executive control. These alterations could potentially lead to top-down deficiencies driving the overconsumption of food in the obese population.

Fast long-range connections in transportation networks

International Nuclear Information System (INIS)

Palhares Viana, Matheus; Fontoura Costa, Luciano da

2011-01-01

Multidimensional scaling is applied in order to visualize an analogue of the small-world effect implied by edges having different displacement velocities in transportation networks. Our findings are illustrated for two real-world systems, namely the London urban network (streets and underground) and the US highway network enhanced by some of the main US airlines routes. We also show that the travel time in these two networks is drastically changed by attacks targeting the edges with large displacement velocities. - Highlights: → Multidimensional scaling used to visualize the effects of fast long-range connections. → Fast long-range connections are important to decrease the average travel time. → The average travel time diverges quickly when the network is under target attacks.

Micro-generation network connection (renewables)

Energy Technology Data Exchange (ETDEWEB)

Thornycroft, J.; Russell, T.; Curran, J.

2003-07-01

The drive to reduce emissions of carbon dioxide will result in an increase in the number of small generation units seeking connection to the electric power distribution network. The objectives of this study were to consider connection issues relating to micro-generation from renewables and their integration into the UK distribution network. The document is divided into two sections. The first section describes the present system which includes input from micro-generation, the technical impacts and the financial considerations. The second part discusses technical, financial and governance options for the future. A summary of preferred options and recommendations is given. The study was carried out by the Halcrow Group Ltd under contract to the DTI.

Convergent and divergent functional connectivity patterns in patients with long-term left-sided and right-sided deafness.

Science.gov (United States)

Zhang, Yanyang; Mao, Zhiqi; Feng, Shiyu; Wang, Wenxin; Zhang, Jun; Yu, Xinguang

2018-02-05

Cortical reorganization may be induced in long-term single-sided deafness (SD); however, the influence of the deafness side on the functional changes remains poorly understood. Here, we investigated whole-brain functional connectivity patterns in long-term SD patients. The normalized voxel-based functional connectivity strength (FCS) was determined using resting-state fMRI (rs-fMRI) in 17 left-sided deafness (LD) patients, 21 right-sided deafness (RD) patients and 21 healthy controls (HCs). Relative to the HCs, both the LD and RD patients exhibited a reduction in the FCS in the ipsilateral visual cortex. However, compared to that in the HCs, a significantly higher FCS was observed in some regions in the salience and default-mode networks in the RD patients, but this FCS alternation pattern was not observed in the LD patients. A direct comparison of the two patient groups revealed a significantly increased FCS in the supplemental motor area in the LD group. Altogether, the long-term SD groups with LD and RD exhibited convergent and divergent functional connectivity patterns in whole-brain networks, providing promising evidence that the functional changes in long-term SD are highly deafness-side-dependent. Copyright © 2017 Elsevier B.V. All rights reserved.

Connectivity analysis of one-dimensional ad-hoc networks

DEFF Research Database (Denmark)

Bøgsted, Martin; Rasmussen, Jakob Gulddahl; Schwefel, Hans Peter

2011-01-01

distance, expressing the geographic distance that a message can be propagated in the network on multi-hop paths; (4) the connectivity hops, which corresponds to the number of hops that are necessary to reach all nodes in the connected network. The paper develops analytic expressions for the distributions...

Functional connectivity patterns reflect individual differences in conflict adaptation.

Science.gov (United States)

Wang, Xiangpeng; Wang, Ting; Chen, Zhencai; Hitchman, Glenn; Liu, Yijun; Chen, Antao

2015-04-01

Individuals differ in the ability to utilize previous conflict information to optimize current conflict resolution, which is termed the conflict adaptation effect. Previous studies have linked individual differences in conflict adaptation to distinct brain regions. However, the network-based neural mechanisms subserving the individual differences of the conflict adaptation effect have not been studied. The present study employed a psychophysiological interaction (PPI) analysis with a color-naming Stroop task to examine this issue. The main results were as follows: (1) the anterior cingulate cortex (ACC)-seeded PPI revealed the involvement of the salience network (SN) in conflict adaptation, while the posterior parietal cortex (PPC)-seeded PPI revealed the engagement of the central executive network (CEN). (2) Participants with high conflict adaptation effect showed higher intra-CEN connectivity and lower intra-SN connectivity; while those with low conflict adaptation effect showed higher intra-SN connectivity and lower intra-CEN connectivity. (3) The PPC-centered intra-CEN connectivity positively predicted the conflict adaptation effect; while the ACC-centered intra-SN connectivity had a negative correlation with this effect. In conclusion, our data demonstrated that conflict adaptation is likely supported by the CEN and the SN, providing a new perspective on studying individual differences in conflict adaptation on the basis of large-scale networks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Decreased middle temporal gyrus connectivity in the language network in schizophrenia patients with auditory verbal hallucinations.

Science.gov (United States)

Zhang, Linchuan; Li, Baojuan; Wang, Huaning; Li, Liang; Liao, Qimei; Liu, Yang; Bao, Xianghong; Liu, Wenlei; Yin, Hong; Lu, Hongbing; Tan, Qingrong

2017-07-13

As the most common symptoms of schizophrenia, the long-term persistence of obstinate auditory verbal hallucinations (AVHs) brings about great mental pain to patients. Neuroimaging studies of schizophrenia have indicated that AVHs were associated with altered functional and structural connectivity within the language network. However, effective connectivity that could reflect directed information flow within this network and is of great importance to understand the neural mechanisms of the disorder remains largely unknown. In this study, we utilized stochastic dynamic causal modeling (DCM) to investigate directed connections within the language network in schizophrenia patients with and without AVHs. Thirty-six patients with schizophrenia (18 with AVHs and 18 without AVHs), and 37 healthy controls participated in the current resting-state functional magnetic resonance imaging (fMRI) study. The results showed that the connection from the left inferior frontal gyrus (LIFG) to left middle temporal gyrus (LMTG) was significantly decreased in patients with AVHs compared to those without AVHs. Meanwhile, the effective connection from the left inferior parietal lobule (LIPL) to LMTG was significantly decreased compared to the healthy controls. Our findings suggest aberrant pattern of causal interactions within the language network in patients with AVHs, indicating that the hypoconnectivity or disrupted connection from frontal to temporal speech areas might be critical for the pathological basis of AVHs. Copyright © 2017 Elsevier B.V. All rights reserved.

Large-scale functional networks connect differently for processing words and symbol strings.

Science.gov (United States)

Liljeström, Mia; Vartiainen, Johanna; Kujala, Jan; Salmelin, Riitta

2018-01-01

Reconfigurations of synchronized large-scale networks are thought to be central neural mechanisms that support cognition and behavior in the human brain. Magnetoencephalography (MEG) recordings together with recent advances in network analysis now allow for sub-second snapshots of such networks. In the present study, we compared frequency-resolved functional connectivity patterns underlying reading of single words and visual recognition of symbol strings. Word reading emphasized coherence in a left-lateralized network with nodes in classical perisylvian language regions, whereas symbol processing recruited a bilateral network, including connections between frontal and parietal regions previously associated with spatial attention and visual working memory. Our results illustrate the flexible nature of functional networks, whereby processing of different form categories, written words vs. symbol strings, leads to the formation of large-scale functional networks that operate at distinct oscillatory frequencies and incorporate task-relevant regions. These results suggest that category-specific processing should be viewed not so much as a local process but as a distributed neural process implemented in signature networks. For words, increased coherence was detected particularly in the alpha (8-13 Hz) and high gamma (60-90 Hz) frequency bands, whereas increased coherence for symbol strings was observed in the high beta (21-29 Hz) and low gamma (30-45 Hz) frequency range. These findings attest to the role of coherence in specific frequency bands as a general mechanism for integrating stimulus-dependent information across brain regions.

Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities

Science.gov (United States)

Tsigkri-DeSmedt, Nefeli Dimitra; Hizanidis, Johanne; Schöll, Eckehard; Hövel, Philipp; Provata, Astero

2017-07-01

The effects of attracting-nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which model the exchange of electrical signals between neurons. Earlier investigations have demonstrated that repulsive-nonlocal and hierarchical network connectivity can induce complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are nonlocally linked with positive diffusive coupling on a ring network, the system splits into a number of alternating domains. Half of these domains contain elements whose potential stays near the threshold and they are interrupted by active domains where the elements perform regular LIF oscillations. The active domains travel along the ring with constant velocity, depending on the system parameters. When we introduce reflecting coupling in LIF networks unexpected complex spatio-temporal structures arise. For relatively extensive ranges of parameter values, the system splits into two coexisting domains: one where all elements stay near the threshold and one where incoherent states develop, characterized by multi-leveled mean phase velocity profiles.

Connecting and Networking for Schools

Science.gov (United States)

Resources for connecting and networking for schools through e-newsletters, finding school IAQ Champions and other EPA school programs such as Asthma, Energy Star, Clean School Bus USA, School Flag, etc.

Enabling Research Network Connectivity to Clouds with Virtual Router Technology

Science.gov (United States)

Seuster, R.; Casteels, K.; Leavett-Brown, CR; Paterson, M.; Sobie, RJ

2017-10-01

The use of opportunistic cloud resources by HEP experiments has significantly increased over the past few years. Clouds that are owned or managed by the HEP community are connected to the LHCONE network or the research network with global access to HEP computing resources. Private clouds, such as those supported by non-HEP research funds are generally connected to the international research network; however, commercial clouds are either not connected to the research network or only connect to research sites within their national boundaries. Since research network connectivity is a requirement for HEP applications, we need to find a solution that provides a high-speed connection. We are studying a solution with a virtual router that will address the use case when a commercial cloud has research network connectivity in a limited region. In this situation, we host a virtual router in our HEP site and require that all traffic from the commercial site transit through the virtual router. Although this may increase the network path and also the load on the HEP site, it is a workable solution that would enable the use of the remote cloud for low I/O applications. We are exploring some simple open-source solutions. In this paper, we present the results of our studies and how it will benefit our use of private and public clouds for HEP computing.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

Science.gov (United States)

Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

2018-01-01

It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

Science.gov (United States)

DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

2016-03-29

We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

Change in brain network connectivity during PACAP38-induced migraine attacks

DEFF Research Database (Denmark)

Amin, Faisal Mohammad; Hougaard, Anders; Magon, Stefano

2016-01-01

OBJECTIVE: To investigate resting-state functional connectivity in the salience network (SN), the sensorimotor network (SMN), and the default mode network (DMN) during migraine attacks induced by pituitary adenylate cyclase-activating polypeptide-38 (PACAP38). METHODS: In a double-blind, randomized...... connectivity with the bilateral opercular part of the inferior frontal gyrus in the SN. In SMN, there was increased connectivity with the right premotor cortex and decreased connectivity with the left visual cortex. Several areas showed increased (left primary auditory, secondary somatosensory, premotor......, and visual cortices) and decreased (right cerebellum and left frontal lobe) connectivity with DMN. We found no resting-state network changes after VIP (n = 15). CONCLUSIONS: PACAP38-induced migraine attack is associated with altered connectivity of several large-scale functional networks of the brain....

Spreading Sequence System for Full Connectivity Relay Network

Science.gov (United States)

Kwon, Hyuck M. (Inventor); Yang, Jie (Inventor); Pham, Khanh D. (Inventor)

2018-01-01

Fully connected uplink and downlink fully connected relay network systems using pseudo-noise spreading and despreading sequences subjected to maximizing the signal-to-interference-plus-noise ratio. The relay network systems comprise one or more transmitting units, relays, and receiving units connected via a communication network. The transmitting units, relays, and receiving units each may include a computer for performing the methods and steps described herein and transceivers for transmitting and/or receiving signals. The computer encodes and/or decodes communication signals via optimum adaptive PN sequences found by employing Cholesky decompositions and singular value decompositions (SVD). The PN sequences employ channel state information (CSI) to more effectively and more securely computing the optimal sequences.

Effects of local and global network connectivity on synergistic epidemics

Science.gov (United States)

Broder-Rodgers, David; Pérez-Reche, Francisco J.; Taraskin, Sergei N.

2015-12-01

Epidemics in networks can be affected by cooperation in transmission of infection and also connectivity between nodes. An interplay between these two properties and their influence on epidemic spread are addressed in the paper. A particular type of cooperative effects (called synergy effects) is considered, where the transmission rate between a pair of nodes depends on the number of infected neighbors. The connectivity effects are studied by constructing networks of different topology, starting with lattices with only local connectivity and then with networks that have both local and global connectivity obtained by random bond-rewiring to nodes within a certain distance. The susceptible-infected-removed epidemics were found to exhibit several interesting effects: (i) for epidemics with strong constructive synergy spreading in networks with high local connectivity, the bond rewiring has a negative role in epidemic spread, i.e., it reduces invasion probability; (ii) in contrast, for epidemics with destructive or weak constructive synergy spreading on networks of arbitrary local connectivity, rewiring helps epidemics to spread; (iii) and, finally, rewiring always enhances the spread of epidemics, independent of synergy, if the local connectivity is low.

Resting-state network disruption and APOE genotype in Alzheimer's disease: a lagged functional connectivity study.

Directory of Open Access Journals (Sweden)

Leonides Canuet

Full Text Available BACKGROUND: The apolipoprotein E epsilon 4 (APOE-4 is associated with a genetic vulnerability to Alzheimer's disease (AD and with AD-related abnormalities in cortical rhythms. However, it is unclear whether APOE-4 is linked to a specific pattern of intrinsic functional disintegration of the brain after the development of the disease or during its different stages. This study aimed at identifying spatial patterns and effects of APOE genotype on resting-state oscillations and functional connectivity in patients with AD, using a physiological connectivity index called "lagged phase synchronization". METHODOLOGY/PRINCIPAL FINDINGS: Resting EEG was recorded during awake, eyes-closed state in 125 patients with AD and 60 elderly controls. Source current density and functional connectivity were determined using eLORETA. Patients with AD exhibited reduced parieto-occipital alpha oscillations compared with controls, and those carrying the APOE-4 allele had reduced alpha activity in the left inferior parietal and temporo-occipital cortex relative to noncarriers. There was a decreased alpha2 connectivity pattern in AD, involving the left temporal and bilateral parietal cortex. Several brain regions exhibited increased lagged phase synchronization in low frequencies, specifically in the theta band, across and within hemispheres, where temporal lobe connections were particularly compromised. Areas with abnormal theta connectivity correlated with cognitive scores. In patients with early AD, we found an APOE-4-related decrease in interhemispheric alpha connectivity in frontal and parieto-temporal regions. CONCLUSIONS/SIGNIFICANCE: In addition to regional cortical dysfunction, as indicated by abnormal alpha oscillations, there are patterns of functional network disruption affecting theta and alpha bands in AD that associate with the level of cognitive disturbance or with the APOE genotype. These functional patterns of nonlinear connectivity may potentially

Laterality patterns of brain functional connectivity: gender effects.

Science.gov (United States)

Tomasi, Dardo; Volkow, Nora D

2012-06-01

Lateralization of brain connectivity may be essential for normal brain function and may be sexually dimorphic. Here, we study the laterality patterns of short-range (implicated in functional specialization) and long-range (implicated in functional integration) connectivity and the gender effects on these laterality patterns. Parallel computing was used to quantify short- and long-range functional connectivity densities in 913 healthy subjects. Short-range connectivity was rightward lateralized and most asymmetrical in areas around the lateral sulcus, whereas long-range connectivity was rightward lateralized in lateral sulcus and leftward lateralizated in inferior prefrontal cortex and angular gyrus. The posterior inferior occipital cortex was leftward lateralized (short- and long-range connectivity). Males had greater rightward lateralization of brain connectivity in superior temporal (short- and long-range), inferior frontal, and inferior occipital cortices (short-range), whereas females had greater leftward lateralization of long-range connectivity in the inferior frontal cortex. The greater lateralization of the male's brain (rightward and predominantly short-range) may underlie their greater vulnerability to disorders with disrupted brain asymmetries (schizophrenia, autism).

Light Manipulation in Metallic Nanowire Networks with Functional Connectivity

KAUST Repository

Galinski, Henning

2016-12-27

Guided by ideas from complex systems, a new class of network metamaterials is introduced for light manipulation, which are based on the functional connectivity among heterogeneous subwavelength components arranged in complex networks. The model system is a nanonetwork formed by dealloying a metallic thin film. The connectivity of the network is deterministically controlled, enabling the formation of tunable absorbing states.

Population Coding in Sparsely Connected Networks of Noisy Neurons

Directory of Open Access Journals (Sweden)

Bryan Patrick Tripp

2012-05-01

Full Text Available This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behaviour. However, population coding theory has often ignored network structure, or assumed discrete, fully-connected populations (in contrast with the sparsely connected, continuous sheet of the cortex. In this study, we model a sheet of cortical neurons with sparse, primarily local connections, and find that a network with this structure can encode multiple internal state variables with high signal-to-noise ratio. However, in our model, although connection probability varies with the distance between neurons, we find that the connections cannot be instantiated at random according to these probabilities, but must have additional structure if information is to be encoded with high fidelity.

Prioritizing connection requests in GMPLS-controlled optical networks

DEFF Research Database (Denmark)

Ruepp, Sarah Renée; Koster, A.; Andriolli, N.

2009-01-01

We prioritize bidirectional connection requests by combining dynamic connection provisioning with off-line optimization. Results show that the proposed approach decreases wavelength-converter usage, thereby allowing operators to reduce blocking-probably under bulk connection assignment or network...

Scholastic performance and functional connectivity of brain networks in children.

Directory of Open Access Journals (Sweden)

Laura Chaddock-Heyman

Full Text Available One of the keys to understanding scholastic success is to determine the neural processes involved in school performance. The present study is the first to use a whole-brain connectivity approach to explore whether functional connectivity of resting state brain networks is associated with scholastic performance in seventy-four 7- to 9-year-old children. We demonstrate that children with higher scholastic performance across reading, math and language have more integrated and interconnected resting state networks, specifically the default mode network, salience network, and frontoparietal network. To add specificity, core regions of the dorsal attention and visual networks did not relate to scholastic performance. The results extend the cognitive role of brain networks in children as well as suggest the importance of network connectivity in scholastic success.

Stability of a giant connected component in a complex network

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Kitsak, Maksim; Ganin, Alexander A.; Eisenberg, Daniel A.; Krapivsky, Pavel L.; Krioukov, Dmitri; Alderson, David L.; Linkov, Igor

2018-01-01

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.

Functional organization of intrinsic connectivity networks in Chinese-chess experts.

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Duan, Xujun; Long, Zhiliang; Chen, Huafu; Liang, Dongmei; Qiu, Lihua; Huang, Xiaoqi; Liu, Timon Cheng-Yi; Gong, Qiyong

2014-04-16

The functional architecture of the human brain has been extensively described in terms of functional connectivity networks, detected from the low-frequency coherent neuronal fluctuations during a resting state condition. Accumulating evidence suggests that the overall organization of functional connectivity networks is associated with individual differences in cognitive performance and prior experience. Such an association raises the question of how cognitive expertise exerts an influence on the topological properties of large-scale functional networks. To address this question, we examined the overall organization of brain functional networks in 20 grandmaster and master level Chinese-chess players (GM/M) and twenty novice players, by means of resting-state functional connectivity and graph theoretical analyses. We found that, relative to novices, functional connectivity was increased in GM/Ms between basal ganglia, thalamus, hippocampus, and several parietal and temporal areas, suggesting the influence of cognitive expertise on intrinsic connectivity networks associated with learning and memory. Furthermore, we observed economical small-world topology in the whole-brain functional connectivity networks in both groups, but GM/Ms exhibited significantly increased values of normalized clustering coefficient which resulted in increased small-world topology. These findings suggest an association between the functional organization of brain networks and individual differences in cognitive expertise, which might provide further evidence of the mechanisms underlying expert behavior. Copyright © 2014 Elsevier B.V. All rights reserved.

Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

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Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

2017-02-01

Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

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Christopher E Hart

2006-12-01

Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

Bayesian network analysis reveals alterations to default mode network connectivity in individuals at risk for Alzheimer's disease.

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Li, Rui; Yu, Jing; Zhang, Shouzi; Bao, Feng; Wang, Pengyun; Huang, Xin; Li, Juan

2013-01-01

Alzheimer's disease (AD) is associated with abnormal functioning of the default mode network (DMN). Functional connectivity (FC) changes to the DMN have been found in patients with amnestic mild cognitive impairment (aMCI), which is the prodromal stage of AD. However, whether or not aMCI also alters the effective connectivity (EC) of the DMN remains unknown. We employed a combined group independent component analysis (ICA) and Bayesian network (BN) learning approach to resting-state functional MRI (fMRI) data from 17 aMCI patients and 17 controls, in order to establish the EC pattern of DMN, and to evaluate changes occurring in aMCI. BN analysis demonstrated heterogeneous regional convergence degree across DMN regions, which were organized into two closely interacting subsystems. Compared to controls, the aMCI group showed altered directed connectivity weights between DMN regions in the fronto-parietal, temporo-frontal, and temporo-parietal pathways. The aMCI group also exhibited altered regional convergence degree in the right inferior parietal lobule. Moreover, we found EC changes in DMN regions in aMCI were correlated with regional FC levels, and the connectivity metrics were associated with patients' cognitive performance. This study provides novel sights into our understanding of the functional architecture of the DMN and adds to a growing body of work demonstrating the importance of the DMN as a mechanism of aMCI.

The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach.

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Guo, Qiang; Xu, Pengpeng; Pei, Xin; Wong, S C; Yao, Danya

2017-02-01

Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Knowledge Access in Rural Inter-connected Areas Network ...

International Development Research Centre (IDRC) Digital Library (Canada)

Knowledge Access in Rural Inter-connected Areas Network (KariaNet) - Phase II ... the existing network to include two thematic networks on food security and rural ... Woman conquering male business in Yemen : Waleya's micro-enterprise.

Frequency-Dependent Altered Functional Connections of Default Mode Network in Alzheimer’s Disease

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Youjun Li

2017-08-01

Full Text Available Alzheimer’s disease (AD is a neurodegenerative disorder associated with the progressive dysfunction of cognitive ability. Previous research has indicated that the default mode network (DMN is closely related to cognition and is impaired in Alzheimer’s disease. Because recent studies have shown that different frequency bands represent specific physiological functions, DMN functional connectivity studies of the different frequency bands based on resting state fMRI (RS-fMRI data may provide new insight into AD pathophysiology. In this study, we explored the functional connectivity based on well-defined DMN regions of interest (ROIs from the five frequency bands: slow-5 (0.01–0.027 Hz, slow-4 (0.027–0.073 Hz, slow-3 (0.073–0.198 Hz, slow-2 (0.198–0.25 Hzs and standard low-frequency oscillations (LFO (0.01–0.08 Hz. We found that the altered functional connectivity patterns are mainly in the frequency band of slow-5 and slow-4 and that the decreased connections are long distance, but some relatively short connections are increased. In addition, the altered functional connections of the DMN in AD are frequency dependent and differ between the slow-5 and slow-4 bands. Mini-Mental State Examination scores were significantly correlated with the altered functional connectivity patterns in the slow-5 and slow-4 bands. These results indicate that frequency-dependent functional connectivity changes might provide potential biomarkers for AD pathophysiology.

Persistence of self-recruitment and patterns of larval connectivity in a marine protected area network

KAUST Repository

Berumen, Michael L.; Almany, Glenn R; Planes, Serge; Jones, Geoffrey P; Saenz Agudelo, Pablo; Thorrold, Simon R

2012-01-01

to maintain local populations while simultaneously supplying larvae to other MPA nodes in the network that might otherwise suffer local extinction. Here, we use genetic parentage analysis to demonstrate that patterns of self-recruitment of two reef fishes

A case for motor network contributions to schizophrenia symptoms: Evidence from resting-state connectivity.

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Bernard, Jessica A; Goen, James R M; Maldonado, Ted

2017-09-01

Though schizophrenia (SCZ) is classically defined based on positive symptoms and the negative symptoms of the disease prove to be debilitating for many patients, motor deficits are often present as well. A growing literature highlights the importance of motor systems and networks in the disease, and it may be the case that dysfunction in motor networks relates to the pathophysiology and etiology of SCZ. To test this and build upon recent work in SCZ and in at-risk populations, we investigated cortical and cerebellar motor functional networks at rest in SCZ and controls using publically available data. We analyzed data from 82 patients and 88 controls. We found key group differences in resting-state connectivity patterns that highlight dysfunction in motor circuits and also implicate the thalamus. Furthermore, we demonstrated that in SCZ, these resting-state networks are related to both positive and negative symptom severity. Though the ventral prefrontal cortex and corticostriatal pathways more broadly have been implicated in negative symptom severity, here we extend these findings to include motor-striatal connections, as increased connectivity between the primary motor cortex and basal ganglia was associated with more severe negative symptoms. Together, these findings implicate motor networks in the symptomatology of psychosis, and we speculate that these networks may be contributing to the etiology of the disease. Overt motor deficits in SCZ may signal underlying network dysfunction that contributes to the overall disease state. Hum Brain Mapp 38:4535-4545, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Patterns and drivers for wetland connections in the Prairie Pothole Region, United States

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Vanderhoof, Melanie; Christensen, Jay R.; Alexander, Laurie C.

2017-01-01

Ecosystem function in rivers, lakes and coastal waters depends on the functioning of upstream aquatic ecosystems, necessitating an improved understanding of watershed-scale interactions including variable surface-water flows between wetlands and streams. As surface water in the Prairie Pothole Region expands in wet years, surface-water connections occur between many depressional wetlands and streams. Minimal research has explored the spatial patterns and drivers for the abundance of these connections, despite their potential to inform resource management and regulatory programs including the U.S. Clean Water Act. In this study, wetlands were identified that did not intersect the stream network, but were shown with Landsat images (1990–2011) to become merged with the stream network as surface water expanded. Wetlands were found to spill into or consolidate with other wetlands within both small (2–10 wetlands) and large (>100 wetlands) wetland clusters, eventually intersecting a stream channel, most often via a riparian wetland. These surface-water connections occurred over a wide range of wetland distances from streams (averaging 90–1400 m in different ecoregions). Differences in the spatial abundance of wetlands that show a variable surface-water connection to a stream were best explained by smaller wetland-to-wetland distances, greater wetland abundance, and maximum surface-water extent. This analysis demonstrated that wetland arrangement and surface water expansion are important mechanisms for depressional wetlands to connect to streams and provides a first step to understanding the frequency and abundance of these surface-water connections across the Prairie Pothole Region.

Default mode network connectivity during task execution.

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Vatansever, D; Menon, D K; Manktelow, A E; Sahakian, B J; Stamatakis, E A

2015-11-15

Initially described as task-induced deactivations during goal-directed paradigms of high attentional load, the unresolved functionality of default mode regions has long been assumed to interfere with task performance. However, recent evidence suggests a potential default mode network involvement in fulfilling cognitive demands. We tested this hypothesis in a finger opposition paradigm with task and fixation periods which we compared with an independent resting state scan using functional magnetic resonance imaging and a comprehensive analysis pipeline including activation, functional connectivity, behavioural and graph theoretical assessments. The results indicate task specific changes in the default mode network topography. Behaviourally, we show that increased connectivity of the posterior cingulate cortex with the left superior frontal gyrus predicts faster reaction times. Moreover, interactive and dynamic reconfiguration of the default mode network regions' functional connections illustrates their involvement with the task at hand with higher-level global parallel processing power, yet preserved small-world architecture in comparison with rest. These findings demonstrate that the default mode network does not disengage during this paradigm, but instead may be involved in task relevant processing. Copyright © 2015 Elsevier Inc. All rights reserved.

Flower-Visiting Social Wasps and Plants Interaction: Network Pattern and Environmental Complexity

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Mateus Aparecido Clemente

2012-01-01

Full Text Available Network analysis as a tool for ecological interactions studies has been widely used since last decade. However, there are few studies on the factors that shape network patterns in communities. In this sense, we compared the topological properties of the interaction network between flower-visiting social wasps and plants in two distinct phytophysiognomies in a Brazilian savanna (Riparian Forest and Rocky Grassland. Results showed that the landscapes differed in species richness and composition, and also the interaction networks between wasps and plants had different patterns. The network was more complex in the Riparian Forest, with a larger number of species and individuals and a greater amount of connections between them. The network specialization degree was more generalist in the Riparian Forest than in the Rocky Grassland. This result was corroborated by means of the nestedness index. In both networks was found asymmetry, with a large number of wasps per plant species. In general aspects, most wasps had low niche amplitude, visiting from one to three plant species. Our results suggest that differences in structural complexity of the environment directly influence the structure of the interaction network between flower-visiting social wasps and plants.

On Connectivity of Wireless Sensor Networks with Directional Antennas

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Qiu Wang

2017-01-01

Full Text Available In this paper, we investigate the network connectivity of wireless sensor networks with directional antennas. In particular, we establish a general framework to analyze the network connectivity while considering various antenna models and the channel randomness. Since existing directional antenna models have their pros and cons in the accuracy of reflecting realistic antennas and the computational complexity, we propose a new analytical directional antenna model called the iris model to balance the accuracy against the complexity. We conduct extensive simulations to evaluate the analytical framework. Our results show that our proposed analytical model on the network connectivity is accurate, and our iris antenna model can provide a better approximation to realistic directional antennas than other existing antenna models.

Topological probability and connection strength induced activity in complex neural networks

International Nuclear Information System (INIS)

Du-Qu, Wei; Bo, Zhang; Dong-Yuan, Qiu; Xiao-Shu, Luo

2010-01-01

Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world (SW) connections. Network elements are described by two-dimensional map neurons (2DMNs) with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied. (general)

Neural intrinsic connectivity networks associated with risk aversion in old age.

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Han, S Duke; Boyle, Patricia A; Arfanakis, Konstantinos; Fleischman, Debra A; Yu, Lei; Edmonds, Emily C; Bennett, David A

2012-02-01

Risk aversion is associated with several important real world outcomes. Although the neurobiological correlates of risk aversion have been studied in young persons, little is known of the neurobiological correlates of risk aversion among older persons. Resting-state functional MRI data were collected on 134 non-demented participants of the Rush Memory and Aging Project, a community-based cohort study of aging. Risk aversion was measured using a series of standard questions in which participants were asked to choose between a certain monetary payment ($15) versus a gamble in which they could gain more than $15 or gain nothing, with potential gains varied across questions. Participants determined to be "high" (n=27) and "low" (n=27) in risk aversion were grouped accordingly. Using a spherical seed region of interest in the anterior cingulate cortex, voxel-wise functional connectivity network similarities were observed in bilateral frontal, anterior and posterior cingulate, insula, basal ganglia, temporal, parietal, and thalamic regions. Differences in functional connectivity were observed such that those low in risk aversion had greater connectivity to clusters in the superior, middle, and medial frontal regions, as well as cerebellar, parietal, occipital, and inferior temporal regions. Those high in risk aversion had greater connectivity to clusters in the inferior and orbital frontal, parahippocampal, and insula regions, as well as thalamic, parietal, precentral gyrus, postcentral gyrus, and middle temporal regions. Similarities and differences in functional connectivity patterns may reflect the historical recruitment of specific brain regions as a network in the active processing of risk in older adults. Copyright © 2011 Elsevier B.V. All rights reserved.

Aberrant cerebellar connectivity in motor and association networks in schizophrenia

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Ann K. Shinn

2015-03-01

Full Text Available Schizophrenia is a devastating illness characterized by disturbances in multiple domains. The cerebellum is involved in both motor and non-motor functions, and the cognitive dysmetria and dysmetria of thought models propose that abnormalities of the cerebellum may contribute to schizophrenia signs and symptoms. The cerebellum and cerebral cortex are reciprocally connected via a modular, closed-loop network architecture, but few schizophrenia neuroimaging studies have taken into account the topographical and functional heterogeneity of the cerebellum. In this study, using a previously defined 17-network cerebral cortical parcellation system as the basis for our functional connectivity seeds, we systematically investigated connectivity abnormalities within the cerebellum of 44 schizophrenia patients and 28 healthy control participants. We found selective alterations in cerebro-cerebellar functional connectivity. Specifically, schizophrenia patients showed decreased cerebro-cerebellar functional connectivity in higher level association networks (ventral attention, salience, control, and default mode networks relative to healthy control participants. Schizophrenia patients also showed increased cerebro-cerebellar connectivity in somatomotor and default mode networks, with the latter showing no overlap with the regions found to be hypoconnected within the same default mode network. Finally, we found evidence to suggest that somatomotor and default mode networks may be inappropriately linked in schizophrenia. The relationship of these dysconnectivities to schizophrenia symptoms, such as neurological soft signs and altered sense of agency, is discussed. We conclude that the cerebellum ought to be considered for analysis in all future studies of network abnormalities in SZ, and further suggest the cerebellum as a potential target for further elucidation, and possibly treatment, of the underlying mechanisms and network abnormalities producing symptoms of

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

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Pläschke, Rachel N; Cieslik, Edna C; Müller, Veronika I; Hoffstaedter, Felix; Plachti, Anna; Varikuti, Deepthi P; Goosses, Mareike; Latz, Anne; Caspers, Svenja; Jockwitz, Christiane; Moebus, Susanne; Gruber, Oliver; Eickhoff, Claudia R; Reetz, Kathrin; Heller, Julia; Südmeyer, Martin; Mathys, Christian; Caspers, Julian; Grefkes, Christian; Kalenscher, Tobias; Langner, Robert; Eickhoff, Simon B

2017-12-01

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc. © 2017

Activating and inhibiting connections in biological network dynamics

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Knight Rob

2008-12-01

Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

Patterns of resting state connectivity in human primary visual cortical areas: a 7T fMRI study.

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Raemaekers, Mathijs; Schellekens, Wouter; van Wezel, Richard J A; Petridou, Natalia; Kristo, Gert; Ramsey, Nick F

2014-01-01

The nature and origin of fMRI resting state fluctuations and connectivity are still not fully known. More detailed knowledge on the relationship between resting state patterns and brain function may help to elucidate this matter. We therefore performed an in depth study of how resting state fluctuations map to the well known architecture of the visual system. We investigated resting state connectivity at both a fine and large scale within and across visual areas V1, V2 and V3 in ten human subjects using a 7Tesla scanner. We found evidence for several coexisting and overlapping connectivity structures at different spatial scales. At the fine-scale level we found enhanced connectivity between the same topographic locations in the fieldmaps of V1, V2 and V3, enhanced connectivity to the contralateral functional homologue, and to a lesser extent enhanced connectivity between iso-eccentric locations within the same visual area. However, by far the largest proportion of the resting state fluctuations occurred within large-scale bilateral networks. These large-scale networks mapped to some extent onto the architecture of the visual system and could thereby obscure fine-scale connectivity. In fact, most of the fine-scale connectivity only became apparent after the large-scale network fluctuations were filtered from the timeseries. We conclude that fMRI resting state fluctuations in the visual cortex may in fact be a composite signal of different overlapping sources. Isolating the different sources could enhance correlations between BOLD and electrophysiological correlates of resting state activity. © 2013 Elsevier Inc. All rights reserved.

Exploring the patterns and evolution of self-organized urban street networks through modeling

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Rui, Yikang; Ban, Yifang; Wang, Jiechen; Haas, Jan

2013-03-01

As one of the most important subsystems in cities, urban street networks have recently been well studied by using the approach of complex networks. This paper proposes a growing model for self-organized urban street networks. The model involves a competition among new centers with different values of attraction radius and a local optimal principle of both geometrical and topological factors. We find that with the model growth, the local optimization in the connection process and appropriate probability for the loop construction well reflect the evolution strategy in real-world cities. Moreover, different values of attraction radius in centers competition process lead to morphological change in patterns including urban network, polycentric and monocentric structures. The model succeeds in reproducing a large diversity of road network patterns by varying parameters. The similarity between the properties of our model and empirical results implies that a simple universal growth mechanism exists in self-organized cities.

Shared atypical default mode and salience network functional connectivity between autism and schizophrenia.

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Chen, Heng; Uddin, Lucina Q; Duan, Xujun; Zheng, Junjie; Long, Zhiliang; Zhang, Youxue; Guo, Xiaonan; Zhang, Yan; Zhao, Jingping; Chen, Huafu

2017-11-01

Schizophrenia and autism spectrum disorder (ASD) are two prevalent neurodevelopmental disorders sharing some similar genetic basis and clinical features. The extent to which they share common neural substrates remains unclear. Resting-state fMRI data were collected from 35 drug-naïve adolescent participants with first-episode schizophrenia (15.6 ± 1.8 years old) and 31 healthy controls (15.4 ± 1.6 years old). Data from 22 participants with ASD (13.1 ± 3.1 years old) and 21 healthy controls (12.9 ± 2.9 years old) were downloaded from the Autism Brain Imaging Data Exchange. Resting-state functional networks were constructed using predefined regions of interest. Multivariate pattern analysis combined with multi-task regression feature selection methods were conducted in two datasets separately. Classification between individuals with disorders and controls was achieved with high accuracy (schizophrenia dataset: accuracy = 83%; ASD dataset: accuracy = 80%). Shared atypical brain connections contributing to classification were mostly present in the default mode network (DMN) and salience network (SN). These functional connections were further related to severity of social deficits in ASD (p = 0.002). Distinct atypical connections were also more related to the DMN and SN, but showed different atypical connectivity patterns between the two disorders. These results suggest some common neural mechanisms contributing to schizophrenia and ASD, and may aid in understanding the pathology of these two neurodevelopmental disorders. Autism Res 2017, 10: 1776-1786. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism spectrum disorder (ASD) and schizophrenia are two common neurodevelopmental disorders which share several genetic and behavioral features. The present study identified common neural mechanisms contributing to ASD and schizophrenia using resting-state functional MRI data. The results may help to understand

No Reef Is an Island: Integrating Coral Reef Connectivity Data into the Design of Regional-Scale Marine Protected Area Networks.

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Schill, Steven R; Raber, George T; Roberts, Jason J; Treml, Eric A; Brenner, Jorge; Halpin, Patrick N

2015-01-01

We integrated coral reef connectivity data for the Caribbean and Gulf of Mexico into a conservation decision-making framework for designing a regional scale marine protected area (MPA) network that provides insight into ecological and political contexts. We used an ocean circulation model and regional coral reef data to simulate eight spawning events from 2008-2011, applying a maximum 30-day pelagic larval duration and 20% mortality rate. Coral larval dispersal patterns were analyzed between coral reefs across jurisdictional marine zones to identify spatial relationships between larval sources and destinations within countries and territories across the region. We applied our results in Marxan, a conservation planning software tool, to identify a regional coral reef MPA network design that meets conservation goals, minimizes underlying threats, and maintains coral reef connectivity. Our results suggest that approximately 77% of coral reefs identified as having a high regional connectivity value are not included in the existing MPA network. This research is unique because we quantify and report coral larval connectivity data by marine ecoregions and Exclusive Economic Zones (EZZ) and use this information to identify gaps in the current Caribbean-wide MPA network by integrating asymmetric connectivity information in Marxan to design a regional MPA network that includes important reef network connections. The identification of important reef connectivity metrics guides the selection of priority conservation areas and supports resilience at the whole system level into the future.

No Reef Is an Island: Integrating Coral Reef Connectivity Data into the Design of Regional-Scale Marine Protected Area Networks.

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Steven R Schill

Full Text Available We integrated coral reef connectivity data for the Caribbean and Gulf of Mexico into a conservation decision-making framework for designing a regional scale marine protected area (MPA network that provides insight into ecological and political contexts. We used an ocean circulation model and regional coral reef data to simulate eight spawning events from 2008-2011, applying a maximum 30-day pelagic larval duration and 20% mortality rate. Coral larval dispersal patterns were analyzed between coral reefs across jurisdictional marine zones to identify spatial relationships between larval sources and destinations within countries and territories across the region. We applied our results in Marxan, a conservation planning software tool, to identify a regional coral reef MPA network design that meets conservation goals, minimizes underlying threats, and maintains coral reef connectivity. Our results suggest that approximately 77% of coral reefs identified as having a high regional connectivity value are not included in the existing MPA network. This research is unique because we quantify and report coral larval connectivity data by marine ecoregions and Exclusive Economic Zones (EZZ and use this information to identify gaps in the current Caribbean-wide MPA network by integrating asymmetric connectivity information in Marxan to design a regional MPA network that includes important reef network connections. The identification of important reef connectivity metrics guides the selection of priority conservation areas and supports resilience at the whole system level into the future.

Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity.

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Srinivasa, Narayan; Cho, Youngkwan

2014-01-01

A spiking neural network model is described for learning to discriminate among spatial patterns in an unsupervised manner. The network anatomy consists of source neurons that are activated by external inputs, a reservoir that resembles a generic cortical layer with an excitatory-inhibitory (EI) network and a sink layer of neurons for readout. Synaptic plasticity in the form of STDP is imposed on all the excitatory and inhibitory synapses at all times. While long-term excitatory STDP enables sparse and efficient learning of the salient features in inputs, inhibitory STDP enables this learning to be stable by establishing a balance between excitatory and inhibitory currents at each neuron in the network. The synaptic weights between source and reservoir neurons form a basis set for the input patterns. The neural trajectories generated in the reservoir due to input stimulation and lateral connections between reservoir neurons can be readout by the sink layer neurons. This activity is used for adaptation of synapses between reservoir and sink layer neurons. A new measure called the discriminability index (DI) is introduced to compute if the network can discriminate between old patterns already presented in an initial training session. The DI is also used to compute if the network adapts to new patterns without losing its ability to discriminate among old patterns. The final outcome is that the network is able to correctly discriminate between all patterns-both old and new. This result holds as long as inhibitory synapses employ STDP to continuously enable current balance in the network. The results suggest a possible direction for future investigation into how spiking neural networks could address the stability-plasticity question despite having continuous synaptic plasticity.

Periodic Hydraulic Testing for Discerning Fracture Network Connections

Science.gov (United States)

Becker, M.; Le Borgne, T.; Bour, O.; Guihéneuf, N.; Cole, M.

2015-12-01

Discrete fracture network (DFN) models often predict highly variable hydraulic connections between injection and pumping wells used for enhanced oil recovery, geothermal energy extraction, and groundwater remediation. Such connections can be difficult to verify in fractured rock systems because standard pumping or pulse interference tests interrogate too large a volume to pinpoint specific connections. Three field examples are presented in which periodic hydraulic tests were used to obtain information about hydraulic connectivity in fractured bedrock. The first site, a sandstone in New York State, involves only a single fracture at a scale of about 10 m. The second site, a granite in Brittany, France, involves a fracture network at about the same scale. The third site, a granite/schist in the U.S. State of New Hampshire, involves a complex network at scale of 30-60 m. In each case periodic testing provided an enhanced view of hydraulic connectivity over previous constant rate tests. Periodic testing is particularly adept at measuring hydraulic diffusivity, which is a more effective parameter than permeability for identify the complexity of flow pathways between measurement locations. Periodic tests were also conducted at multiple frequencies which provides a range in the radius of hydraulic penetration away from the oscillating well. By varying the radius of penetration, we attempt to interrogate the structure of the fracture network. Periodic tests, therefore, may be uniquely suited for verifying and/or calibrating DFN models.

Node-based measures of connectivity in genetic networks.

Science.gov (United States)

Koen, Erin L; Bowman, Jeff; Wilson, Paul J

2016-01-01

At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal. © 2015 John Wiley & Sons Ltd.

Social network of an internationally connected nurse leader.

Science.gov (United States)

Benton, David

2016-03-01

Over the past decade, there has been a proliferation of social media sites offering the opportunity for colleagues to connect with each other locally, nationally and internationally. Meanwhile, nurses have been increasingly using social network analytical techniques to look at team functioning and communication pathways. This article uses the author's LinkedIn social network to illustrate how analysis can offer insights into the connections, and how the results can be used to professional advantage.

Finite connectivity attractor neural networks

International Nuclear Information System (INIS)

Wemmenhove, B; Coolen, A C C

2003-01-01

We study a family of diluted attractor neural networks with a finite average number of (symmetric) connections per neuron. As in finite connectivity spin glasses, their equilibrium properties are described by order parameter functions, for which we derive an integral equation in replica symmetric approximation. A bifurcation analysis of this equation reveals the locations of the paramagnetic to recall and paramagnetic to spin-glass transition lines in the phase diagram. The line separating the retrieval phase from the spin-glass phase is calculated at zero temperature. All phase transitions are found to be continuous

Connectivity, cycles, and persistence thresholds in metapopulation networks.

Directory of Open Access Journals (Sweden)

Yael Artzy-Randrup

2010-08-01

Full Text Available Synthesising the relationships between complexity, connectivity, and the stability of large biological systems has been a longstanding fundamental quest in theoretical biology and ecology. With the many exciting developments in modern network theory, interest in these issues has recently come to the forefront in a range of multidisciplinary areas. Here we outline a new theoretical analysis specifically relevant for the study of ecological metapopulations focusing primarily on marine systems, where subpopulations are generally connected via larval dispersal. Our work determines the qualitative and quantitative conditions by which dispersal and network structure control the persistence of a set of age-structured patch populations. Mathematical modelling combined with a graph theoretic analysis demonstrates that persistence depends crucially on the topology of cycles in the dispersal network which tend to enhance the effect of larvae "returning home." Our method clarifies the impact directly due to network structure, but this almost by definition can only be achieved by examining the simplified case in which patches are identical; an assumption that we later relax. The methodology identifies critical migration routes, whose presence are vital to overall stability, and therefore should have high conservation priority. In contrast, "lonely links," or links in the network that do not participate in a cyclical component, have no impact on persistence and thus have low conservation priority. A number of other intriguing criteria for persistence are derived. Our modelling framework reveals new insights regarding the determinants of persistence, stability, and thresholds in complex metapopulations. In particular, while theoretical arguments have, in the past, suggested that increasing connectivity is a destabilizing feature in complex systems, this is not evident in metapopulation networks where connectivity, cycles, coherency, and heterogeneity all tend

Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays.

Directory of Open Access Journals (Sweden)

Joost eLe Feber

2015-11-01

Full Text Available Cultured neurons on multi electrode arrays (MEAs have been widely used to study various as-pects of neuronal (network functioning. A possible drawback of this approach is the lack of structure in these networks. At the single cell level, several solutions have been proposed to ena-ble directed connectivity, and promising results were obtained. At the level of connected sub-populations, a few attempts have been made with promising results. First assessment of the de-signs’ functionality, however, suggested room for further improvement.We designed a two chamber MEA aiming to create a unidirectional connection between the net-works in both chambers (‘emitting’ and ‘receiving’. To achieve this unidirectionality, all inter-connecting channels contained barbs that hindered axon growth in the opposite direction (from receiving to emitting chamber. Visual inspection showed that axons predominantly grew through the channels in the promoted direction . This observation was confirmed by spontaneous activity recordings. Cross-correlation between the signals from two electrodes inside the channels suggested signal propagation at ≈2 m/s from emitting to receiving chamber. Cross-correlation between the firing patterns in both chambers indicated that most correlated activity was initiated in the emitting chamber, which was also reflected by a significantly lower fraction of partial bursts (e. a one-chamber-only burst in the emitting chamber. Finally, electrical stimulation in the emitting chamber induced a fast response in that chamber, and a slower response in the receiving chamber. Stimulation in the receiving chamber evoked a fast response in that chamber, but no response in the emitting chamber. These results confirm the predominantly unidirectional nature of the connecting channels from emitting to receiving chamber.

Path connectivity based spectral defragmentation in flexible bandwidth networks.

Science.gov (United States)

Wang, Ying; Zhang, Jie; Zhao, Yongli; Zhang, Jiawei; Zhao, Jie; Wang, Xinbo; Gu, Wanyi

2013-01-28

Optical networks with flexible bandwidth provisioning have become a very promising networking architecture. It enables efficient resource utilization and supports heterogeneous bandwidth demands. In this paper, two novel spectrum defragmentation approaches, i.e. Maximum Path Connectivity (MPC) algorithm and Path Connectivity Triggering (PCT) algorithm, are proposed based on the notion of Path Connectivity, which is defined to represent the maximum variation of node switching ability along the path in flexible bandwidth networks. A cost-performance-ratio based profitability model is given to denote the prons and cons of spectrum defragmentation. We compare these two proposed algorithms with non-defragmentation algorithm in terms of blocking probability. Then we analyze the differences of defragmentation profitability between MPC and PCT algorithms.

Directional patterns of cross frequency phase and amplitude coupling within the resting state mimic patterns of fMRI functional connectivity

Science.gov (United States)

Weaver, Kurt E.; Wander, Jeremiah D.; Ko, Andrew L.; Casimo, Kaitlyn; Grabowski, Thomas J.; Ojemann, Jeffrey G.; Darvas, Felix

2016-01-01

Functional imaging investigations into the brain's resting state interactions have yielded a wealth of insight into the intrinsic and dynamic neural architecture supporting cognition and behavior. Electrophysiological studies however have highlighted the fact that synchrony across large-scale cortical systems is composed of spontaneous interactions occurring at timescales beyond the traditional resolution of fMRI, a feature that limits the capacity of fMRI to draw inference on the true directional relationship between network nodes. To approach the question of directionality in resting state signals, we recorded resting state functional MRI (rsfMRI) and electrocorticography (ECoG) from four human subjects undergoing invasive epilepsy monitoring. Using a seed-point based approach, we employed phase-amplitude coupling (PAC) and biPhase Locking Values (bPLV), two measures of cross-frequency coupling (CFC) to explore both outgoing and incoming connections between the seed and all non-seed, site electrodes. We observed robust PAC between a wide range of low-frequency phase and high frequency amplitude estimates. However, significant bPLV, a CFC measure of phase-phase synchrony, was only observed at specific narrow low and high frequency bandwidths. Furthermore, the spatial patterns of outgoing PAC connectivity were most closely associated with the rsfMRI connectivity maps. Our results support the hypothesis that PAC is relatively ubiquitous phenomenon serving as a mechanism for coordinating high-frequency amplitudes across distant neuronal assemblies even in absence of overt task structure. Additionally, we demonstrate that the spatial distribution of a seed-point rsfMRI sensorimotor network is strikingly similar to specific patterns of directional PAC. Specifically, the high frequency activities of distal patches of cortex owning membership in a rsfMRI sensorimotor network were most likely to be entrained to the phase of a low frequency rhythm engendered from the

Decreased triple network connectivity in patients with post-traumatic stress disorder

Science.gov (United States)

Liu, Yang; Li, Liang; Li, Baojuan; Zhang, Xi; Lu, Hongbing

2017-03-01

The triple network model provides a common framework for understanding affective and neurocognitive dysfunctions across multiple disorders, including central executive network (CEN), default mode network (DMN), and salience network (SN). Considering the effect of traumatic experience on post-traumatic stress disorder (PTSD), this study aims to explore the alteration of triple network connectivity in a specific PTSD induced by a single prolonged trauma exposure. With arterial spin labeling sequence, three networks were identified using independent component analysis in 10 PTSD patients and 10 healthy survivors, who experienced the same coal mining flood disaster. In PTSD patients, decreased connectivity was identified in left middle frontal gyrus of CEN, left precuneus and bilateral superior frontal gyrus of DMN, and right anterior insula of SN. The decreased connectivity in left middle frontal gyrus was identified to associate with clinical severity. These results indicated the decreased triple network connectivity, which not only supported the proposal of the triple network model, but also prompted possible neurobiology mechanism of cognitive dysfunction for this kind of PTSD.

Hyperthermia-induced disruption of functional connectivity in the human brain network.

Directory of Open Access Journals (Sweden)

Gang Sun

executive control reaction time. CONCLUSIONS/SIGNIFICANCE: We first identified the hyperthermia-induced altered functional connectivity patterns. The changes in the functional connectivity network might be a possible explanation for the cognitive performance and work behavior alteration.

Connectivities and synchronous firing in cortical neuronal networks

International Nuclear Information System (INIS)

Jia, L.C.; Sano, M.; Lai, P.-Y.; Chan, C.K.

2004-01-01

Network connectivities (k-bar) of cortical neural cultures are studied by synchronized firing and determined from measured correlations between fluorescence intensities of firing neurons. The bursting frequency (f) during synchronized firing of the networks is found to be an increasing function of k-bar. With f taken to be proportional to k-bar, a simple random model with a k-bar dependent connection probability p(k-bar) has been constructed to explain our experimental findings successfully

Hydraulic Stability of Heat Networks for Connection of New Consumers

Science.gov (United States)

Seminenko, A. S.; Sheremet, E. O.; Gushchin, S. V.; Elistratova, J. V.; Kireev, V. M.

2018-03-01

Nowadays due to intensive urban construction, there is a need to connect new consumers to existing heating networks. Often the connection of new consumers leads to a hydraulic misalignment of the network, which in turn affects supplying existing consumers with heat. In order to minimize the possibility of misalignment, appropriate recommendations are needed that can be obtained during the research. In the article, the authors carried out a required experiment aimed at revealing the influence of the new consumers’ connection on the hydraulic stability of the entire network. The result of the research is relevant recommendations that will be useful for engineering specialists both for the design of new networks and the reconstruction of the old ones.

Connecting to the Internet Securely; Protecting Home Networks CIAC-2324

Energy Technology Data Exchange (ETDEWEB)

Orvis, W J; Krystosek, P; Smith, J

2002-11-27

With more and more people working at home and connecting to company networks via the Internet, the risk to company networks to intrusion and theft of sensitive information is growing. Working from home has many positive advantages for both the home worker and the company they work for. However, as companies encourage people to work from home, they need to start considering the interaction of the employee's home network and the company network he connects to. This paper discusses problems and solutions related to protection of home computers from attacks on those computers via the network connection. It does not consider protection of those systems from people who have physical access to the computers nor does it consider company laptops taken on-the-road. Home networks are often targeted by intruders because they are plentiful and they are usually not well secured. While companies have departments of professionals to maintain and secure their networks, home networks are maintained by the employee who may be less knowledgeable about network security matters. The biggest problems with home networks are that: Home networks are not designed to be secure and may use technologies (wireless) that are not secure; The operating systems are not secured when they are installed; The operating systems and applications are not maintained (for security considerations) after they are installed; and The networks are often used for other activities that put them at risk for being compromised. Home networks that are going to be connected to company networks need to be cooperatively secured by the employee and the company so they do not open up the company network to intruders. Securing home networks involves many of the same operations as securing a company network: Patch and maintain systems; Securely configure systems; Eliminate unneeded services; Protect remote logins; Use good passwords; Use current antivirus software; and Moderate your Internet usage habits. Most of these

Women’s connectivity in extreme networks

Science.gov (United States)

Manrique, Pedro; Cao, Zhenfeng; Gabriel, Andrew; Horgan, John; Gill, Paul; Qi, Hong; Restrepo, Elvira M.; Johnson, Daniela; Wuchty, Stefan; Song, Chaoming; Johnson, Neil

2016-01-01

A popular stereotype is that women will play more minor roles than men as environments become more dangerous and aggressive. Our analysis of new longitudinal data sets from offline and online operational networks [for example, ISIS (Islamic State)] shows that although men dominate numerically, women emerge with superior network connectivity that can benefit the underlying system’s robustness and survival. Our observations suggest new female-centric approaches that could be used to affect such networks. They also raise questions about how individual contributions in high-pressure systems are evaluated. PMID:27386564

Connectivity strategies to enhance the capacity of weight-bearing networks

International Nuclear Information System (INIS)

Janaki, T.M.; Gupte, Neelima

2003-01-01

The connectivity properties of a weight-bearing network are exploited to enhance its capacity. We study a 2D network of sites where the weight-bearing capacity of a given site depends on the capacities of the sites connected to it in the layers above. The network consists of clusters, viz., a set of sites connected with each other with the largest such collection of sites being denoted as the maximal cluster. New connections are made between sites in successive layers using two distinct strategies. The key element of our strategies consists of adding as many disjoint clusters as possible to the sites on the trunk T of the maximal cluster. In the first strategy the reconnections start from the last layer upwards and stop when no new sites are added. In the second case, the reconnections start from the top layer and go all the way down to the last layer. The new networks can bear much higher weights than the original networks and have much lower failure rates. The first strategy leads to a greater enhancement of stability, whereas the second leads to a greater enhancement of capacity compared to the original networks. The original network used here is a typical example of the branching hierarchical class. However, the application of strategies similar to ours can yield useful results in other types of networks as well

Knowledge Access in Rural Inter-connected Areas Network ...

International Development Research Centre (IDRC) Digital Library (Canada)

Knowledge Access in Rural Inter-connected Areas Network (KariaNet) - Phase II ... poor by sharing innovations, best practices and indigenous knowledge using ... A third thematic network - on knowledge management strategies - will play an ...

Impact of connected vehicle guidance information on network-wide average travel time

Directory of Open Access Journals (Sweden)

Jiangfeng Wang

2016-12-01

Full Text Available With the emergence of connected vehicle technologies, the potential positive impact of connected vehicle guidance on mobility has become a research hotspot by data exchange among vehicles, infrastructure, and mobile devices. This study is focused on micro-modeling and quantitatively evaluating the impact of connected vehicle guidance on network-wide travel time by introducing various affecting factors. To evaluate the benefits of connected vehicle guidance, a simulation architecture based on one engine is proposed representing the connected vehicle–enabled virtual world, and connected vehicle route guidance scenario is established through the development of communication agent and intelligent transportation systems agents using connected vehicle application programming interface considering the communication properties, such as path loss and transmission power. The impact of connected vehicle guidance on network-wide travel time is analyzed by comparing with non-connected vehicle guidance in response to different market penetration rate, following rate, and congestion level. The simulation results explore that average network-wide travel time in connected vehicle guidance shows a significant reduction versus that in non–connected vehicle guidance. Average network-wide travel time in connected vehicle guidance have an increase of 42.23% comparing to that in non-connected vehicle guidance, and average travel time variability (represented by the coefficient of variance increases as the travel time increases. Other vital findings include that higher penetration rate and following rate generate bigger savings of average network-wide travel time. The savings of average network-wide travel time increase from 17% to 38% according to different congestion levels, and savings of average travel time in more serious congestion have a more obvious improvement for the same penetration rate or following rate.

Technical guide to the connection of generation to the distribution network

Energy Technology Data Exchange (ETDEWEB)

Jarrett, K.; Hedgecock, J.; Gregory, R.; Warham, T.

2003-07-01

This guide provides a 'route map' of the processes of getting a generation scheme connected to the network and is intended to help developers of any form of distributed generation connected to the UK's local electricity networks, eg: renewable energy schemes; waste-to-energy schemes; on-site generation and combined heat and power (CHP) schemes; and peak lopping schemes using back-up generators. Where necessary, the guide distinguishes between arrangements that apply in Scotland and those that apply in England and Wales. The guide aims to: provide background information about the electricity industry; highlight common technical issues that arise during connection negotiation and their implications for distribution network operators (DNOs) and developers; examine the main factors affecting connection costs and timescales for achieving connections; and identify the different types of contracts relating to connection. The report considers the connection process, the connection application process and timescales, costs and charges, competition in connection, the structure of the UK electricity industry, the statutory framework, the effects of distributed generation of the distribution system, earthing and protection design, safety issues and DNO network information. It includes a glossary, checklists, useful contact details and information about standards and other useful documents.

Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks

OpenAIRE

Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

2016-01-01

In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control...

Latent and Abnormal Functional Connectivity Circuits in Autism Spectrum Disorder.

Science.gov (United States)

Chen, Shuo; Xing, Yishi; Kang, Jian

2017-01-01

Autism spectrum disorder (ASD) is associated with disrupted brain networks. Neuroimaging techniques provide noninvasive methods of investigating abnormal connectivity patterns in ASD. In the present study, we compare functional connectivity networks in people with ASD with those in typical controls, using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE) project. Specifically, we focus on the characteristics of intrinsic functional connectivity based on data collected by resting-state functional magnetic resonance imaging (rs-fMRI). Our aim was to identify disrupted brain connectivity patterns across all networks, instead of in individual edges, by using advanced statistical methods. Unlike many brain connectome studies, in which networks are prespecified before the edge connectivity in each network is compared between clinical groups, we detected the latent differentially expressed networks automatically. Our network-level analysis identified abnormal connectome networks that (i) included a high proportion of edges that were differentially expressed between people with ASD and typical controls; and (ii) showed highly-organized graph topology. These findings provide new insight into the study of the underlying neuropsychiatric mechanism of ASD.

Intrinsic connectivity networks from childhood to late adolescence: Effects of age and sex

Directory of Open Access Journals (Sweden)

Cristina Solé-Padullés

2016-02-01

Full Text Available There is limited evidence on the effects of age and sex on intrinsic connectivity of networks underlying cognition during childhood and adolescence. Independent component analysis was conducted in 113 subjects aged 7–18; the default mode, executive control, anterior salience, basal ganglia, language and visuospatial networks were identified. The effect of age was examined with multiple regression, while sex and ‘age × sex’ interactions were assessed by dividing the sample according to age (7–12 and 13–18 years. As age increased, connectivity in the dorsal and ventral default mode network became more anterior and posterior, respectively, while in the executive control network, connectivity increased within frontoparietal regions. The basal ganglia network showed increased engagement of striatum, thalami and precuneus. The anterior salience network showed greater connectivity in frontal areas and anterior cingulate, and less connectivity of orbitofrontal, middle cingulate and temporoparietal regions. The language network presented increased connectivity of inferior frontal and decreased connectivity within the right middle frontal and left inferior parietal cortices. The visuospatial network showed greater engagement of inferior parietal and frontal cortices. No effect of sex, nor age by sex interactions was observed. These findings provide evidence of strengthening of cortico-cortical and cortico-subcortical networks across childhood and adolescence.

Implementation of neural networks on 'Connection Machine'

International Nuclear Information System (INIS)

Belmonte, Ghislain

1990-12-01

This report is a first approach to the notion of neural networks and their possible applications within the framework of artificial intelligence activities of the Department of Applied Mathematics of the Limeil-Valenton Research Center. The first part is an introduction to the field of neural networks; the main neural network models are described in this section. The applications of neural networks in the field of classification have mainly been studied because they could more particularly help to solve some of the decision support problems dealt with by the C.E.A. As the neural networks perform a large number of parallel operations, it was therefore logical to use a parallel architecture computer: the Connection Machine (which uses 16384 processors and is located at E.T.C.A. Arcueil). The second part presents some generalities on the parallelism and the Connection Machine, and two implementations of neural networks on Connection Machine. The first of these implementations concerns one of the most used algorithms to realize the learning of neural networks: the Gradient Retro-propagation algorithm. The second one, less common, concerns a network of neurons destined mainly to the recognition of forms: the Fukushima Neocognitron. The latter is studied by the C.E.A. of Bruyeres-le-Chatel in order to realize an embedded system (including hardened circuits) for the fast recognition of forms [fr

Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

Science.gov (United States)

Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf

2018-04-01

Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

The Science DMZ: A Network Design Pattern for Data-Intensive Science

Energy Technology Data Exchange (ETDEWEB)

Dart, Eli; Rotman, Lauren; Tierney, Brian; Hester, Mary; Zurawski, Jason

2013-08-13

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

Science.gov (United States)

Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

Low-stress bicycling and network connectivity.

Science.gov (United States)

2012-05-01

For a bicycling network to attract the widest possible segment of the population, its most fundamental attribute should be low-stress connectivity, that is, providing routes between peoples origins and destinations that do not require cyclists to ...

Violence-related content in video game may lead to functional connectivity changes in brain networks as revealed by fMRI-ICA in young men.

Science.gov (United States)

Zvyagintsev, M; Klasen, M; Weber, R; Sarkheil, P; Esposito, F; Mathiak, K A; Schwenzer, M; Mathiak, K

2016-04-21

In violent video games, players engage in virtual aggressive behaviors. Exposure to virtual aggressive behavior induces short-term changes in players' behavior. In a previous study, a violence-related version of the racing game "Carmageddon TDR2000" increased aggressive affects, cognitions, and behaviors compared to its non-violence-related version. This study investigates the differences in neural network activity during the playing of both versions of the video game. Functional magnetic resonance imaging (fMRI) recorded ongoing brain activity of 18 young men playing the violence-related and the non-violence-related version of the video game Carmageddon. Image time series were decomposed into functional connectivity (FC) patterns using independent component analysis (ICA) and template-matching yielded a mapping to established functional brain networks. The FC patterns revealed a decrease in connectivity within 6 brain networks during the violence-related compared to the non-violence-related condition: three sensory-motor networks, the reward network, the default mode network (DMN), and the right-lateralized frontoparietal network. Playing violent racing games may change functional brain connectivity, in particular and even after controlling for event frequency, in the reward network and the DMN. These changes may underlie the short-term increase of aggressive affects, cognitions, and behaviors as observed after playing violent video games. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Connected Dominating Set Based Topology Control in Wireless Sensor Networks

Science.gov (United States)

He, Jing

2012-01-01

Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

Chaos in complex motor networks induced by Newman—Watts small-world connections

International Nuclear Information System (INIS)

Wei Du-Qu; Luo Xiao-Shu; Zhang Bo

2011-01-01

We investigate how dynamical behaviours of complex motor networks depend on the Newman—Watts small-world (NWSW) connections. Network elements are described by the permanent magnet synchronous motor (PMSM) with the values of parameters at which each individual PMSM is stable. It is found that with the increase of connection probability p, the motor in networks becomes periodic and falls into chaotic motion as p further increases. These phenomena imply that NWSW connections can induce and enhance chaos in motor networks. The possible mechanism behind the action of NWSW connections is addressed based on stability theory. (interdisciplinary physics and related areas of science and technology)

Fuzzy logic and neural networks in artificial intelligence and pattern recognition

Science.gov (United States)

Sanchez, Elie

1991-10-01

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder.

Science.gov (United States)

Wang, Y; Wang, J; Jia, Y; Zhong, S; Zhong, M; Sun, Y; Niu, M; Zhao, L; Zhao, L; Pan, J; Huang, L; Huang, R

2017-07-04

Bipolar disorder (BD), particularly BD II, is frequently misdiagnosed as unipolar depression (UD), leading to inappropriate treatment and poor clinical outcomes. Although depressive symptoms may be expressed similarly in UD and BD, the similarities and differences in the architecture of brain functional networks between the two disorders are still unknown. In this study, we hypothesized that UD and BD II patients would show convergent and divergent patterns of disrupted topological organization of the functional connectome, especially in the default mode network (DMN) and the limbic network. Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 32 UD-unmedicated patients, 31 unmedicated BD II patients (current episode depressed) and 43 healthy subjects. Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the following three levels: whole brain, modularity and node. First, both the UD and BD II patients showed increased characteristic path length and decreased global efficiency compared with the controls. Second, both the UD and BD II patients showed disrupted intramodular connectivity within the DMN and limbic system network. Third, decreased nodal characteristics (nodal strength and nodal efficiency) were found predominantly in brain regions in the DMN, limbic network and cerebellum of both the UD and BD II patients, whereas differences between the UD and BD II patients in the nodal characteristics were also observed in the precuneus and temporal pole. Convergent deficits in the topological organization of the whole brain, DMN and limbic networks may reflect overlapping pathophysiological processes in unipolar and bipolar depression. Our discovery of divergent regional connectivity that supports emotion processing could help to identify biomarkers that will aid in differentiating these disorders.

Altered cerebellar functional connectivity with intrinsic connectivity networks in adults with major depressive disorder.

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Li Liu

Full Text Available BACKGROUND: Numerous studies have demonstrated the higher-order functions of the cerebellum, including emotion regulation and cognitive processing, and have indicated that the cerebellum should therefore be included in the pathophysiological models of major depressive disorder. The aim of this study was to compare the resting-state functional connectivity of the cerebellum in adults with major depression and healthy controls. METHODS: Twenty adults with major depression and 20 gender-, age-, and education-matched controls were investigated using seed-based resting-state functional connectivity magnetic resonance imaging. RESULTS: Compared with the controls, depressed patients showed significantly increased functional connectivity between the cerebellum and the temporal poles. However, significantly reduced cerebellar functional connectivity was observed in the patient group in relation to both the default-mode network, mainly including the ventromedial prefrontal cortex and the posterior cingulate cortex/precuneus, and the executive control network, mainly including the superior frontal cortex and orbitofrontal cortex. Moreover, the Hamilton Depression Rating Scale score was negatively correlated with the functional connectivity between the bilateral Lobule VIIb and the right superior frontal gyrus in depressed patients. CONCLUSIONS: This study demonstrated increased cerebellar coupling with the temporal poles and reduced coupling with the regions in the default-mode and executive control networks in adults with major depression. These differences between patients and controls could be associated with the emotional disturbances and cognitive control function deficits that accompany major depression. Aberrant cerebellar connectivity during major depression may also imply a substantial role for the cerebellum in the pathophysiological models of depression.

Connectivity, topology and dynamics in climate networks

Czech Academy of Sciences Publication Activity Database

Paluš, Milan; Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin

2012-01-01

Roč. 14, - (2012), s. 8397 ISSN 1607-7962. [European Geosciences Union General Assembly 2012. 22.04.2012-27.04.2012, Vienna] R&D Projects: GA ČR GCP103/11/J068 Institutional support: RVO:67985807 Keywords : complex networks * climate network * connectivity * entropy rate * El Nino Southern Oscillation * North Atlantic Oscillation Subject RIV: BB - Applied Statistics, Operational Research

Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks

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Lindsay eRutter

2013-07-01

Full Text Available Complex networks have been observed to comprise small-world properties, believed to represent an optimal organization of local specialization and global integration of information processing at reduced wiring cost. Here, we applied magnitude squared coherence to resting magnetoencephalographic time series in reconstructed source space, acquired from controls and patients with schizophrenia, and generated frequency-dependent adjacency matrices modeling functional connectivity between virtual channels. After configuring undirected binary and weighted graphs, we found that all human networks demonstrated highly localized clustering and short characteristic path lengths. The most conservatively thresholded networks showed efficient wiring, with topographical distance between connected vertices amounting to one-third as observed in surrogate randomized topologies. Nodal degrees of the human networks conformed to a heavy-tailed exponentially truncated power-law, compatible with the existence of hubs, which included theta and alpha bilateral cerebellar tonsil, beta and gamma bilateral posterior cingulate, and bilateral thalamus across all frequencies. We conclude that all networks showed small-worldness, minimal physical connection distance, and skewed degree distributions characteristic of physically-embedded networks, and that these calculations derived from graph theoretical mathematics did not quantifiably distinguish between subject populations, independent of bandwidth. However, post-hoc measurements of edge computations at the scale of the individual vertex revealed trends of reduced gamma connectivity across the posterior medial parietal cortex in patients, an observation consistent with our prior resting activation study that found significant reduction of synthetic aperture magnetometry gamma power across similar regions. The basis of these small differences remains unclear.

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

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Liu, Jin; Liao, Xuhong; Xia, Mingrui; He, Yong

2018-02-01

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease. © 2017 Wiley Periodicals, Inc.

Barbed channels enhance unidirectional connectivity between neuronal networks cultured on multi electrode arrays

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le Feber, Joost; Postma, Wybren; de Weerd, Eddy; Weusthof, Marcel; Rutten, Wim L. C.

2015-01-01

Cultured neurons on multi electrode arrays (MEAs) have been widely used to study various aspects of neuronal (network) functioning. A possible drawback of this approach is the lack of structure in these networks. At the single cell level, several solutions have been proposed to enable directed connectivity, and promising results were obtained. At the level of connected sub-populations, a few attempts have been made with promising results. First assessment of the designs' functionality, however, suggested room for further improvement. We designed a two chamber MEA aiming to create a unidirectional connection between the networks in both chambers (“emitting” and “receiving”). To achieve this unidirectionality, all interconnecting channels contained barbs that hindered axon growth in the opposite direction (from receiving to emitting chamber). Visual inspection showed that axons predominantly grew through the channels in the promoted direction. This observation was confirmed by spontaneous activity recordings. Cross-correlation between the signals from two electrodes inside the channels suggested signal propagation at ≈2 m/s from emitting to receiving chamber. Cross-correlation between the firing patterns in both chambers indicated that most correlated activity was initiated in the emitting chamber, which was also reflected by a significantly lower fraction of partial bursts (i.e., a one-chamber-only burst) in the emitting chamber. Finally, electrical stimulation in the emitting chamber induced a fast response in that chamber, and a slower response in the receiving chamber. Stimulation in the receiving chamber evoked a fast response in that chamber, but no response in the emitting chamber. These results confirm the predominantly unidirectional nature of the connecting channels from emitting to receiving chamber. PMID:26578869

On Connected Target k-Coverage in Heterogeneous Wireless Sensor Networks.

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Yu, Jiguo; Chen, Ying; Ma, Liran; Huang, Baogui; Cheng, Xiuzhen

2016-01-15

Coverage and connectivity are two important performance evaluation indices for wireless sensor networks (WSNs). In this paper, we focus on the connected target k-coverage (CTC k) problem in heterogeneous wireless sensor networks (HWSNs). A centralized connected target k-coverage algorithm (CCTC k) and a distributed connected target k-coverage algorithm (DCTC k) are proposed so as to generate connected cover sets for energy-efficient connectivity and coverage maintenance. To be specific, our proposed algorithms aim at achieving minimum connected target k-coverage, where each target in the monitored region is covered by at least k active sensor nodes. In addition, these two algorithms strive to minimize the total number of active sensor nodes and guarantee that each sensor node is connected to a sink, such that the sensed data can be forwarded to the sink. Our theoretical analysis and simulation results show that our proposed algorithms outperform a state-of-art connected k-coverage protocol for HWSNs.

Two Distinct Scene-Processing Networks Connecting Vision and Memory.

Science.gov (United States)

Baldassano, Christopher; Esteva, Andre; Fei-Fei, Li; Beck, Diane M

2016-01-01

A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.

Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

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Okamoto, Hiroshi

2016-08-01

Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study.

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Gabard-Durnam, Laurel Joy; Gee, Dylan Grace; Goff, Bonnie; Flannery, Jessica; Telzer, Eva; Humphreys, Kathryn Leigh; Lumian, Daniel Stephen; Fareri, Dominic Stephen; Caldera, Christina; Tottenham, Nim

2016-04-27

Although the functional architecture of the brain is indexed by resting-state connectivity networks, little is currently known about the mechanisms through which these networks assemble into stable mature patterns. The current study posits and tests the long-term phasic molding hypothesis that resting-state networks are gradually shaped by recurring stimulus-elicited connectivity across development by examining how both stimulus-elicited and resting-state functional connections of the human brain emerge over development at the systems level. Using a sequential design following 4- to 18-year-olds over a 2 year period, we examined the predictive associations between stimulus-elicited and resting-state connectivity in amygdala-cortical circuitry as an exemplar case (given this network's protracted development across these ages). Age-related changes in amygdala functional connectivity converged on the same regions of medial prefrontal cortex (mPFC) and inferior frontal gyrus when elicited by emotional stimuli and when measured at rest. Consistent with the long-term phasic molding hypothesis, prospective analyses for both connections showed that the magnitude of an individual's stimulus-elicited connectivity unidirectionally predicted resting-state functional connectivity 2 years later. For the amygdala-mPFC connection, only stimulus-elicited connectivity during childhood and the transition to adolescence shaped future resting-state connectivity, consistent with a sensitive period ending with adolescence for the amygdala-mPFC circuit. Together, these findings suggest that resting-state functional architecture may arise from phasic patterns of functional connectivity elicited by environmental stimuli over the course of development on the order of years. A fundamental issue in understanding the ontogeny of brain function is how resting-state (intrinsic) functional networks emerge and relate to stimulus-elicited functional connectivity. Here, we posit and test the long

The Science DMZ: A Network Design Pattern for Data-Intensive Science

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Eli Dart

2014-01-01

Full Text Available The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

Science.gov (United States)

Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

2017-05-01

Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study

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Yuhui Du

2018-01-01

Full Text Available Although individuals at clinical high risk (CHR for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ, it remains unclear whether their resting-state brain intrinsic functional networks (INs show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs, 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive, and 58 early illness SZ (ESZ patients (among which 53 patients took antipsychotic medication within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a

Reconstruction of sparse connectivity in neural networks from spike train covariances

International Nuclear Information System (INIS)

Pernice, Volker; Rotter, Stefan

2013-01-01

The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L 1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model. (paper)

Modelling fast spreading patterns of airborne infectious diseases using complex networks

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Brenner, Frank; Marwan, Norbert; Hoffmann, Peter

2017-04-01

The pandemics of SARS (2002/2003) and H1N1 (2009) have impressively shown the potential of epidemic outbreaks of infectious diseases in a world that is strongly connected. Global air travelling established an easy and fast opportunity for pathogens to migrate globally in only a few days. This made epidemiological prediction harder. By understanding this complex development and its link to climate change we can suggest actions to control a part of global human health affairs. In this study we combine the following data components to simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human: em{Global Air Traffic Network (from openflights.org) with information on airports, airport location, direct flight connection, airplane type} em{Global population dataset (from SEDAC, NASA)} em{Susceptible-Infected-Recovered (SIR) compartmental model to simulate disease spreading in the vicinity of airports. A modified Susceptible-Exposed-Infected-Recovered (SEIR) model to analyze the impact of the incubation period.} em{WATCH-Forcing-Data-ERA-Interim (WFDEI) climate data: temperature, specific humidity, surface air pressure, and water vapor pressure} These elements are implemented into a complex network. Nodes inside the network represent airports. Each single node is equipped with its own SIR/SEIR compartmental model with node specific attributes. Edges between those nodes represent direct flight connections that allow infected individuals to move between linked nodes. Therefore the interaction of the set of unique SIR models creates the model dynamics we will analyze. To better figure out the influence on climate change on disease spreading patterns, we focus on Influenza-like-Illnesses (ILI). The transmission rate of ILI has a dependency on climate parameters like humidity and temperature. Even small changes of environmental variables can trigger significant differences in the global outbreak behavior. Apart from the direct

Structural Connectivity Asymmetry in the Neonatal Brain

OpenAIRE

Ratnarajah, Nagulan; Rifkin-Graboi, Anne; Fortier, Marielle V.; Chong, Yap Seng; Kwek, Kenneth; Saw, Seang-Mei; Godfrey, Keith M; Gluckman, Peter D.; Meaney, Michael J.; Qiu, Anqi

2013-01-01

Asymmetry of the neonatal brain is not yet understood at the level of structural connectivity. We utilized DTI deterministic tractography and structural network analysis based on graph theory to determine the pattern of structural connectivity asymmetry in 124 normal neonates. We tracted white matter axonal pathways characterizing interregional connections among brain regions and inferred asymmetry in left and right anatomical network properties. Our findings revealed that in neonates, small-...

Network-based analysis reveals functional connectivity related to internet addiction tendency

Directory of Open Access Journals (Sweden)

Tanya eWen

2016-02-01

Full Text Available IntroductionPreoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills. Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.

l-Dopa responsiveness is associated with distinctive connectivity patterns in advanced Parkinson's disease.

Science.gov (United States)

Akram, Harith; Wu, Chengyuan; Hyam, Jonathan; Foltynie, Thomas; Limousin, Patricia; De Vita, Enrico; Yousry, Tarek; Jahanshahi, Marjan; Hariz, Marwan; Behrens, Timothy; Ashburner, John; Zrinzo, Ludvic

2017-06-01

Neuronal loss and dopamine depletion alter motor signal processing between cortical motor areas, basal ganglia, and the thalamus, resulting in the motor manifestations of Parkinson's disease. Dopamine replacement therapy can reverse these manifestations with varying degrees of improvement. To evaluate functional connectivity in patients with advanced Parkinson's disease and changes in functional connectivity in relation to the degree of response to l-dopa, 19 patients with advanced Parkinson's disease underwent resting-state functional magnetic resonance imaging in the on-medication state. Scans were obtained on a 3-Tesla scanner in 3 × 3 × 2.5 mm 3 voxels. Seed-based bivariate regression analyses were carried out with atlas-defined basal ganglia regions as seeds, to explore relationships between functional connectivity and improvement in the motor section of the UPDRS-III following an l-dopa challenge. False discovery rate-corrected P was set at basal ganglia resting-state functional connectivity patterns associated with different degrees of l-dopa responsiveness in patients with advanced Parkinson's disease. l-Dopa exerts a graduated influence on remapping connectivity in distinct motor control networks, potentially explaining some of the variance in treatment response. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

Connectivity effects in the dynamic model of neural networks

International Nuclear Information System (INIS)

Choi, J; Choi, M Y; Yoon, B-G

2009-01-01

We study, via extensive Monte Carlo calculations, the effects of connectivity in the dynamic model of neural networks, to observe that the Mattis-state order parameter increases with the number of coupled neurons. Such effects appear more pronounced when the average number of connections is increased by introducing shortcuts in the network. In particular, the power spectra of the order parameter at stationarity are found to exhibit power-law behavior, depending on how the average number of connections is increased. The cluster size distribution of the 'memory-unmatched' sites also follows a power law and possesses strong correlations with the power spectra. It is further observed that the distribution of waiting times for neuron firing fits roughly to a power law, again depending on how neuronal connections are increased

Intercluster Connection in Cognitive Wireless Mesh Networks Based on Intelligent Network Coding

Science.gov (United States)

Chen, Xianfu; Zhao, Zhifeng; Jiang, Tao; Grace, David; Zhang, Honggang

2009-12-01

Cognitive wireless mesh networks have great flexibility to improve spectrum resource utilization, within which secondary users (SUs) can opportunistically access the authorized frequency bands while being complying with the interference constraint as well as the QoS (Quality-of-Service) requirement of primary users (PUs). In this paper, we consider intercluster connection between the neighboring clusters under the framework of cognitive wireless mesh networks. Corresponding to the collocated clusters, data flow which includes the exchanging of control channel messages usually needs four time slots in traditional relaying schemes since all involved nodes operate in half-duplex mode, resulting in significant bandwidth efficiency loss. The situation is even worse at the gateway node connecting the two colocated clusters. A novel scheme based on network coding is proposed in this paper, which needs only two time slots to exchange the same amount of information mentioned above. Our simulation shows that the network coding-based intercluster connection has the advantage of higher bandwidth efficiency compared with the traditional strategy. Furthermore, how to choose an optimal relaying transmission power level at the gateway node in an environment of coexisting primary and secondary users is discussed. We present intelligent approaches based on reinforcement learning to solve the problem. Theoretical analysis and simulation results both show that the intelligent approaches can achieve optimal throughput for the intercluster relaying in the long run.

Network analysis of mesoscale optical recordings to assess regional, functional connectivity.

Science.gov (United States)

Lim, Diana H; LeDue, Jeffrey M; Murphy, Timothy H

2015-10-01

With modern optical imaging methods, it is possible to map structural and functional connectivity. Optical imaging studies that aim to describe large-scale neural connectivity often need to handle large and complex datasets. In order to interpret these datasets, new methods for analyzing structural and functional connectivity are being developed. Recently, network analysis, based on graph theory, has been used to describe and quantify brain connectivity in both experimental and clinical studies. We outline how to apply regional, functional network analysis to mesoscale optical imaging using voltage-sensitive-dye imaging and channelrhodopsin-2 stimulation in a mouse model. We include links to sample datasets and an analysis script. The analyses we employ can be applied to other types of fluorescence wide-field imaging, including genetically encoded calcium indicators, to assess network properties. We discuss the benefits and limitations of using network analysis for interpreting optical imaging data and define network properties that may be used to compare across preparations or other manipulations such as animal models of disease.

Combining a dispersal model with network theory to assess habitat connectivity.

Science.gov (United States)

Lookingbill, Todd R; Gardner, Robert H; Ferrari, Joseph R; Keller, Cherry E

2010-03-01

Assessing the potential for threatened species to persist and spread within fragmented landscapes requires the identification of core areas that can sustain resident populations and dispersal corridors that can link these core areas with isolated patches of remnant habitat. We developed a set of GIS tools, simulation methods, and network analysis procedures to assess potential landscape connectivity for the Delmarva fox squirrel (DFS; Sciurus niger cinereus), an endangered species inhabiting forested areas on the Delmarva Peninsula, USA. Information on the DFS's life history and dispersal characteristics, together with data on the composition and configuration of land cover on the peninsula, were used as input data for an individual-based model to simulate dispersal patterns of millions of squirrels. Simulation results were then assessed using methods from graph theory, which quantifies habitat attributes associated with local and global connectivity. Several bottlenecks to dispersal were identified that were not apparent from simple distance-based metrics, highlighting specific locations for landscape conservation, restoration, and/or squirrel translocations. Our approach links simulation models, network analysis, and available field data in an efficient and general manner, making these methods useful and appropriate for assessing the movement dynamics of threatened species within landscapes being altered by human and natural disturbances.

Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine.

Science.gov (United States)

Palma, Jesse; Grossberg, Stephen; Versace, Massimiliano

2012-01-01

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 functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network

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 functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 milliseconds or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners

Meta-connectomics: human brain network and connectivity meta-analyses.

Science.gov (United States)

Crossley, N A; Fox, P T; Bullmore, E T

2016-04-01

Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as 'meta-connectomics'. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI 'lesions' to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

Brain resting-state networks in adolescents with high-functioning autism: Analysis of spatial connectivity and temporal neurodynamics.

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Bernas, Antoine; Barendse, Evelien M; Aldenkamp, Albert P; Backes, Walter H; Hofman, Paul A M; Hendriks, Marc P H; Kessels, Roy P C; Willems, Frans M J; de With, Peter H N; Zinger, Svitlana; Jansen, Jacobus F A

2018-02-01

Autism spectrum disorder (ASD) is mainly characterized by functional and communication impairments as well as restrictive and repetitive behavior. The leading hypothesis for the neural basis of autism postulates globally abnormal brain connectivity, which can be assessed using functional magnetic resonance imaging (fMRI). Even in the absence of a task, the brain exhibits a high degree of functional connectivity, known as intrinsic, or resting-state, connectivity. Global default connectivity in individuals with autism versus controls is not well characterized, especially for a high-functioning young population. The aim of this study is to test whether high-functioning adolescents with ASD (HFA) have an abnormal resting-state functional connectivity. We performed spatial and temporal analyses on resting-state networks (RSNs) in 13 HFA adolescents and 13 IQ- and age-matched controls. For the spatial analysis, we used probabilistic independent component analysis (ICA) and a permutation statistical method to reveal the RSN differences between the groups. For the temporal analysis, we applied Granger causality to find differences in temporal neurodynamics. Controls and HFA display very similar patterns and strengths of resting-state connectivity. We do not find any significant differences between HFA adolescents and controls in the spatial resting-state connectivity. However, in the temporal dynamics of this connectivity, we did find differences in the causal effect properties of RSNs originating in temporal and prefrontal cortices. The results show a difference between HFA and controls in the temporal neurodynamics from the ventral attention network to the salience-executive network: a pathway involving cognitive, executive, and emotion-related cortices. We hypothesized that this weaker dynamic pathway is due to a subtle trigger challenging the cognitive state prior to the resting state.

Controlling Voltage Levels of Distribution Network-Radial Feeder after Connecting Wind Turbines to the Network

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Muhammad Al Badri

2017-11-01

Full Text Available Several factors in power generation and supply need to be taken into account such as shortages of energy supply, system stability, and energy quality and system disruption due to network losses, industrial development and population expansion. The addition of wind turbines to the distribution network is of great benefit in providing additional power and solving these problems, but this addition is accompanied by the problem of low voltage network. This research found optimal solutions to the problem of low voltage distribution network after connecting wind turbines. The main idea of this paper is to optimize the low-voltage problem as a result of connecting the wind turbines to the "far end" of the radial feeder for a distribution network and to obtain a voltage level within an acceptable and stable range. The problem of low voltage solved by using the load-drop compensation, capacitor-bank and “doubly-fed” induction generators. The results of this study were based on the operation of the entire design of the simulation system which would be compatible with the reality of the energy flow of all network components by using the PSCAD program. The present analysis program revealed an optimum solution for the low voltage profile of the distribution network after connecting the wind turbine.

Brain intrinsic network connectivity in individuals with frequent tanning behavior.

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Ketcherside, Ariel; Filbey, Francesca M; Aubert, Pamela M; Seibyl, John P; Price, Julianne L; Adinoff, Bryon

2018-05-01

Emergent studies suggest a bidirectional relationship between brain functioning and the skin. This neurocutaneous connection may be responsible for the reward response to tanning and, thus, may contribute to excessive tanning behavior. To date, however, this association has not yet been examined. To explore whether intrinsic brain functional connectivity within the default mode network (DMN) is related to indoor tanning behavior. Resting state functional connectivity (rsFC) was obtained in twenty adults (16 females) with a history of indoor tanning. Using a seed-based [(posterior cingulate cortex (PCC)] approach, the relationship between tanning severity and FC strength was assessed. Tanning severity was measured with symptom count from the Structured Clinical Interview for Tanning Abuse and Dependence (SITAD) and tanning intensity (lifetime indoor tanning episodes/years tanning). rsFC strength between the PCC and other DMN regions (left globus pallidus, left medial frontal gyrus, left superior frontal gyrus) is positively correlated with tanning symptom count. rsFC strength between the PCC and salience network regions (right anterior cingulate cortex, left inferior parietal lobe, left inferior temporal gyrus) is correlated with tanning intensity. Greater connectivity between tanning severity and DMN and salience network connectivity suggests that heightened self-awareness of salient stimuli may be a mechanism that underlies frequent tanning behavior. These findings add to the growing evidence of brain-skin connection and reflect dysregulation in the reward processing networks in those with frequent tanning.

Light Manipulation in Metallic Nanowire Networks with Functional Connectivity

KAUST Repository

Galinski, Henning; Fratalocchi, Andrea; Dö beli, Max; Capasso, Federico

2016-01-01

Guided by ideas from complex systems, a new class of network metamaterials is introduced for light manipulation, which are based on the functional connectivity among heterogeneous subwavelength components arranged in complex networks. The model

A generative modeling approach to connectivity-Electrical conduction in vascular networks

DEFF Research Database (Denmark)

Hald, Bjørn Olav

2016-01-01

The physiology of biological structures is inherently dynamic and emerges from the interaction and assembly of large collections of small entities. The extent of coupled entities complicates modeling and increases computational load. Here, microvascular networks are used to present a novel...... to synchronize vessel tone across the vast distances within a network. We hypothesize that electrical conduction capacity is delimited by the size of vascular structures and connectivity of the network. Generation and simulation of series of dynamical models of electrical spread within vascular networks...... of different size and composition showed that (1) Conduction is enhanced in models harboring long and thin endothelial cells that couple preferentially along the longitudinal axis. (2) Conduction across a branch point depends on endothelial connectivity between branches. (3) Low connectivity sub...

Directed connectivity of brain default networks in resting state using GCA and motif.

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Jiao, Zhuqing; Wang, Huan; Ma, Kai; Zou, Ling; Xiang, Jianbo

2017-06-01

Nowadays, there is a lot of interest in assessing functional interactions between key brain regions. In this paper, Granger causality analysis (GCA) and motif structure are adopted to study directed connectivity of brain default mode networks (DMNs) in resting state. Firstly, the time series of functional magnetic resonance imaging (fMRI) data in resting state were extracted, and the causal relationship values of the nodes representing related brain regions are analyzed in time domain to construct a default network. Then, the network structures were searched from the default networks of controls and patients to determine the fixed connection mode in the networks. The important degree of motif structures in directed connectivity of default networks was judged according to p-value and Z-score. Both node degree and average distance were used to analyze the effect degree an information transfer rate of brain regions in motifs and default networks, and efficiency of the network. Finally, activity and functional connectivity strength of the default brain regions are researched according to the change of energy distributions between the normals and the patients' brain regions. Experimental results demonstrate that, both normal subjects and stroke patients have some corresponding fixed connection mode of three nodes, and the efficiency and power spectrum of the patient's default network is somewhat lower than that of the normal person. In particular, the Right Posterior Cingulate Gyrus (PCG.R) has a larger change in functional connectivity and its activity. The research results verify the feasibility of the application of GCA and motif structure to study the functional connectivity of default networks in resting state.

Embedded generation connection incentives for distribution network operators

Energy Technology Data Exchange (ETDEWEB)

Williams, P.; Andrews, S.

2002-07-01

This is the final report with respect to work commissioned by the Department of Trade and Industry (DTI) as part of the New and Renewable Energy Programme into incentives for distribution network operators (DNOs) for the connection of embedded generation. This report, which incorporates the contents of the interim report submitted in February 2002, considers the implications of changes in the structure and regulation in the UK electricity industry on the successful technical and commercial integrated of embedded generation into distribution networks. The report examines: the obligations of public electricity suppliers (PESs); current DNO practices regarding the connection of embedded generation; the changes introduced by the Utilities Act 2000, including the impact of new obligations placed on DNOs on the connection of embedded generation and the requirements of the new Electricity Distribution Standard Licence conditions; and problems and prospects for DNO incentives.

Abnormal resting-state connectivity of motor and cognitive networks in early manifest Huntington's disease.

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Wolf, R C; Sambataro, F; Vasic, N; Depping, M S; Thomann, P A; Landwehrmeyer, G B; Süssmuth, S D; Orth, M

2014-11-01

Functional magnetic resonance imaging (fMRI) of multiple neural networks during the brain's 'resting state' could facilitate biomarker development in patients with Huntington's disease (HD) and may provide new insights into the relationship between neural dysfunction and clinical symptoms. To date, however, very few studies have examined the functional integrity of multiple resting state networks (RSNs) in manifest HD, and even less is known about whether concomitant brain atrophy affects neural activity in patients. Using MRI, we investigated brain structure and RSN function in patients with early HD (n = 20) and healthy controls (n = 20). For resting-state fMRI data a group-independent component analysis identified spatiotemporally distinct patterns of motor and prefrontal RSNs of interest. We used voxel-based morphometry to assess regional brain atrophy, and 'biological parametric mapping' analyses to investigate the impact of atrophy on neural activity. Compared with controls, patients showed connectivity changes within distinct neural systems including lateral prefrontal, supplementary motor, thalamic, cingulate, temporal and parietal regions. In patients, supplementary motor area and cingulate cortex connectivity indices were associated with measures of motor function, whereas lateral prefrontal connectivity was associated with cognition. This study provides evidence for aberrant connectivity of RSNs associated with motor function and cognition in early manifest HD when controlling for brain atrophy. This suggests clinically relevant changes of RSN activity in the presence of HD-associated cortical and subcortical structural abnormalities.

Internet Connection Control based on Idle Time Using User Behavior Pattern Analysis

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Fadilah Fahrul Hardiansyah

2014-12-01

Full Text Available The increase of smartphone ability is rapidly increasing the power consumption. Many methods have been proposed to reduce smartphone power consumption. Most of these methods use the internet connection control based on the availability of the battery power level regardless of when and where a waste of energy occurs. This paper proposes a new approach to control the internet connection based on idle time using user behavior pattern analysis. User behavior patterns are used to predict idle time duration. Internet connection control performed during idle time. During idle time internet connection periodically switched on and off by a certain time interval. This method effectively reduces a waste of energy. Control of the internet connection does not interfere the user because it is implemented on idle time. Keywords: Smartphone, User Behavior, Pattern Recognition, Idle Time, Internet Connection Control

Pattern-recalling processes in quantum Hopfield networks far from saturation

International Nuclear Information System (INIS)

Inoue, Jun-ichi

2011-01-01

As a mathematical model of associative memories, the Hopfield model was now well-established and a lot of studies to reveal the pattern-recalling process have been done from various different approaches. As well-known, a single neuron is itself an uncertain, noisy unit with a finite unnegligible error in the input-output relation. To model the situation artificially, a kind of 'heat bath' that surrounds neurons is introduced. The heat bath, which is a source of noise, is specified by the 'temperature'. Several studies concerning the pattern-recalling processes of the Hopfield model governed by the Glauber-dynamics at finite temperature were already reported. However, we might extend the 'thermal noise' to the quantum-mechanical variant. In this paper, in terms of the stochastic process of quantum-mechanical Markov chain Monte Carlo method (the quantum MCMC), we analytically derive macroscopically deterministic equations of order parameters such as 'overlap' in a quantum-mechanical variant of the Hopfield neural networks (let us call quantum Hopfield model or quantum Hopfield networks). For the case in which non-extensive number p of patterns are embedded via asymmetric Hebbian connections, namely, p/N → 0 for the number of neuron N → ∞ ('far from saturation'), we evaluate the recalling processes for one of the built-in patterns under the influence of quantum-mechanical noise.

Impaired consciousness is linked to changes in effective connectivity of the posterior cingulate cortex within the default mode network.

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Crone, Julia Sophia; Schurz, Matthias; Höller, Yvonne; Bergmann, Jürgen; Monti, Martin; Schmid, Elisabeth; Trinka, Eugen; Kronbichler, Martin

2015-04-15

The intrinsic connectivity of the default mode network has been associated with the level of consciousness in patients with severe brain injury. Especially medial parietal regions are considered to be highly involved in impaired consciousness. To better understand what aspect of this intrinsic architecture is linked to consciousness, we applied spectral dynamic causal modeling to assess effective connectivity within the default mode network in patients with disorders of consciousness. We included 12 controls, 12 patients in minimally conscious state and 13 in vegetative state in this study. For each subject, we first defined the four key regions of the default mode network employing a subject-specific independent component analysis approach. The resulting regions were then included as nodes in a spectral dynamic causal modeling analysis in order to assess how the causal interactions across these regions as well as the characteristics of neuronal fluctuations change with the level of consciousness. The resulting pattern of interaction in controls identified the posterior cingulate cortex as the main driven hub with positive afferent but negative efferent connections. In patients, this pattern appears to be disrupted. Moreover, the vegetative state patients exhibit significantly reduced self-inhibition and increased oscillations in the posterior cingulate cortex compared to minimally conscious state and controls. Finally, the degree of self-inhibition and strength of oscillation in this region is correlated with the level of consciousness. These findings indicate that the equilibrium between excitatory connectivity towards posterior cingulate cortex and its feedback projections is a key aspect of the relationship between alterations in consciousness after severe brain injury and the intrinsic functional architecture of the default mode network. This impairment might be principally due to the disruption of the mechanisms underlying self-inhibition and neuronal

Multi-channels coupling-induced pattern transition in a tri-layer neuronal network

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Wu, Fuqiang; Wang, Ya; Ma, Jun; Jin, Wuyin; Hobiny, Aatef

2018-03-01

Neurons in nerve system show complex electrical behaviors due to complex connection types and diversity in excitability. A tri-layer network is constructed to investigate the signal propagation and pattern formation by selecting different coupling channels between layers. Each layer is set as different states, and the local kinetics is described by Hindmarsh-Rose neuron model. By changing the number of coupling channels between layers and the state of the first layer, the collective behaviors of each layer and synchronization pattern of network are investigated. A statistical factor of synchronization on each layer is calculated. It is found that quiescent state in the second layer can be excited and disordered state in the third layer is suppressed when the first layer is controlled by a pacemaker, and the developed state is dependent on the number of coupling channels. Furthermore, the collapse in the first layer can cause breakdown of other layers in the network, and the mechanism is that disordered state in the third layer is enhanced when sampled signals from the collapsed layer can impose continuous disturbance on the next layer.

Rest but busy: Aberrant resting-state functional connectivity of triple network model in insomnia.

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Dong, Xiaojuan; Qin, Haixia; Wu, Taoyu; Hu, Hua; Liao, Keren; Cheng, Fei; Gao, Dong; Lei, Xu

2018-02-01

One classical hypothesis among many models to explain the etiology and maintenance of insomnia disorder (ID) is hyperarousal. Aberrant functional connectivity among resting-state large-scale brain networks may be the underlying neurological mechanisms of this hypothesis. The aim of current study was to investigate the functional network connectivity (FNC) among large-scale brain networks in patients with insomnia disorder (ID) during resting state. In the present study, the resting-state fMRI was used to evaluate whether patients with ID showed aberrant FNC among dorsal attention network (DAN), frontoparietal control network (FPC), anterior default mode network (aDMN), and posterior default mode network (pDMN) compared with healthy good sleepers (HGSs). The Pearson's correlation analysis was employed to explore whether the abnormal FNC observed in patients with ID was associated with sleep parameters, cognitive and emotional scores, and behavioral performance assessed by questionnaires and tasks. Patients with ID had worse subjective thought control ability measured by Thought Control Ability Questionnaire (TCAQ) and more negative affect than HGSs. Intriguingly, relative to HGSs, patients with ID showed a significant increase in FNC between DAN and FPC, but a significant decrease in FNC between aDMN and pDMN. Exploratory analysis in patients with ID revealed a significantly positive correlation between the DAN-FPC FNC and reaction time (RT) of psychomotor vigilance task (PVT). The current study demonstrated that even during the resting state, the task-activated and task-deactivated large-scale brain networks in insomniacs may still maintain a hyperarousal state, looking quite similar to the pattern in a task condition with external stimuli. Those results support the hyperarousal model of insomnia.

Wave speed in excitable random networks with spatially constrained connections.

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Nikita Vladimirov

Full Text Available Very fast oscillations (VFO in neocortex are widely observed before epileptic seizures, and there is growing evidence that they are caused by networks of pyramidal neurons connected by gap junctions between their axons. We are motivated by the spatio-temporal waves of activity recorded using electrocorticography (ECoG, and study the speed of activity propagation through a network of neurons axonally coupled by gap junctions. We simulate wave propagation by excitable cellular automata (CA on random (Erdös-Rényi networks of special type, with spatially constrained connections. From the cellular automaton model, we derive a mean field theory to predict wave propagation. The governing equation resolved by the Fisher-Kolmogorov PDE fails to describe wave speed. A new (hyperbolic PDE is suggested, which provides adequate wave speed v( that saturates with network degree , in agreement with intuitive expectations and CA simulations. We further show that the maximum length of connection is a much better predictor of the wave speed than the mean length. When tested in networks with various degree distributions, wave speeds are found to strongly depend on the ratio of network moments / rather than on mean degree , which is explained by general network theory. The wave speeds are strikingly similar in a diverse set of networks, including regular, Poisson, exponential and power law distributions, supporting our theory for various network topologies. Our results suggest practical predictions for networks of electrically coupled neurons, and our mean field method can be readily applied for a wide class of similar problems, such as spread of epidemics through spatial networks.

Aberrant orbitofrontal connectivity in marijuana smoking adolescents

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Melissa Patricia Lopez-Larson

2015-12-01

Discussion: Findings indicate atypical OFC functional connectivity patterns in attentional/executive, motor and reward networks in adolescents with heavy MJ use. These anomalies may be related to suboptimal decision making capacities and increased impulsivity. Results also suggest different OFC connectivity patterns may be present in adolescents with early onset of MJ use and high lifetime exposure to MJ.

Just-in-time connectivity for large spiking networks.

Science.gov (United States)

Lytton, William W; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

2008-11-01

The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.

Genes2FANs: connecting genes through functional association networks

Science.gov (United States)

2012-01-01

Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in

Hidden Connectivity in Networks with Vulnerable Classes of Nodes

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Sebastian M. Krause

2016-10-01

Full Text Available In many complex systems representable as networks, nodes can be separated into different classes. Often these classes can be linked to a mutually shared vulnerability. Shared vulnerabilities may be due to a shared eavesdropper or correlated failures. In this paper, we show the impact of shared vulnerabilities on robust connectivity and how the heterogeneity of node classes can be exploited to maintain functionality by utilizing multiple paths. Percolation is the field of statistical physics that is generally used to analyze connectivity in complex networks, but in its existing forms, it cannot treat the heterogeneity of multiple vulnerable classes. To analyze the connectivity under these constraints, we describe each class as a color and develop a “color-avoiding” percolation. We present an analytic theory for random networks and a numerical algorithm for all networks, with which we can determine which nodes are color-avoiding connected and whether the maximal set percolates in the system. We find that the interaction of topology and color distribution implies a rich critical behavior, with critical values and critical exponents depending both on the topology and on the color distribution. Applying our physics-based theory to the Internet, we show how color-avoiding percolation can be used as the basis for new topologically aware secure communication protocols. Beyond applications to cybersecurity, our framework reveals a new layer of hidden structure in a wide range of natural and technological systems.

Altered Functional Connectivity of the Default Mode Network in Low-Empathy Subjects.

Science.gov (United States)

Kim, Seung Jun; Kim, Sung Eun; Kim, Hyo Eun; Han, Kiwan; Jeong, Bumseok; Kim, Jae Jin; Namkoong, Kee; Kim, Ji Woong

2017-09-01

Empathy is the ability to identify with or make a vicariously experience of another person's feelings or thoughts based on memory and/or self-referential mental simulation. The default mode network in particular is related to self-referential empathy. In order to elucidate the possible neural mechanisms underlying empathy, we investigated the functional connectivity of the default mode network in subjects from a general population. Resting state functional magnetic resonance imaging data were acquired from 19 low-empathy subjects and 18 medium-empathy subjects. An independent component analysis was used to identify the default mode network, and differences in functional connectivity strength were compared between the two groups. The low-empathy group showed lower functional connectivity of the medial prefrontal cortex and anterior cingulate cortex (Brodmann areas 9 and 32) within the default mode network, compared to the medium-empathy group. The results of the present study suggest that empathy is related to functional connectivity of the medial prefrontal cortex/anterior cingulate cortex within the default mode network. Functional decreases in connectivity among low-empathy subjects may reflect an impairment of self-referential mental simulation. © Copyright: Yonsei University College of Medicine 2017.

Abnormal Functional Connectivity Between Default and Salience Networks in Pediatric Bipolar Disorder.

Science.gov (United States)

Lopez-Larson, Melissa P; Shah, Lubdha M; Weeks, Howard R; King, Jace B; Mallik, Atul K; Yurgelun-Todd, Deborah A; Anderson, Jeffrey S

2017-01-01

Pediatric bipolar disorder (PBD) (occurring prior to 18 years of age) is a developmental brain disorder that is among the most severe and disabling psychiatric conditions affecting youth. Despite increasing evidence that brain connectivity is atypical in adults with bipolar disorder, it is not clear how brain connectivity may be altered in youths with PBD. This cross-sectional resting-state functional magnetic resonance imaging study included 80 participants recruited over 4 years: 32 youths with PBD, currently euthymic (13 males; 15.1 years old), and 48 healthy control (HC) subjects (27 males; 14.5 years old). Functional connectivity between eight major intrinsic connectivity networks, along with connectivity measurements between 333 brain regions, was compared between PBD and HC subjects. Additionally, connectivity differences were evaluated between PBD and HC samples in negatively correlated connections, as defined by 839 subjects of the Human Connectome Project dataset. We found increased inter- but not intranetwork functional connectivity in PBD between the default mode and salience networks (p = .0017). Throughout the brain, atypical connections showed failure to develop anticorrelation with age during adolescence in PBD but not HC samples among connections that exhibit negative correlation in adulthood. Youths with PBD demonstrate reduced anticorrelation between default mode and salience networks. Further evaluation of the interaction between these networks is needed in development and with other mood states such as depression and mania to clarify if this atypical connectivity is a PBD trait biomarker. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

Science.gov (United States)

Jie, Biao; Liu, Mingxia; Shen, Dinggang

2018-07-01

Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic brain disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi

Connecting Mobile Users Through Mobile Social Networks

Directory of Open Access Journals (Sweden)

Faisal Alkhateeb

2012-10-01

Full Text Available Nowadays, social networks become popular with the emerging of web-based social networking services. Recently, several mobile services are developed to connect users to their favourite social networks such as Facebook, Twitter, Flickr, etc. However, these services depends upon the existing web-based social networks. In this paper, we present a mobile service for joining groups across communities. The originality of the work is that the framework of the service allows creating and joining social networks that are self-contained for mobile company servers. The service consists of several sub-services such as users invitation, group finding and others. Users, regardless of their disability, can use the service and its sub-services without the need to create their own accounts on social web sites and thus their own groups. We also propose a privacy control policy for mobile social networks.

Muscle networks: Connectivity analysis of EMG activity during postural control

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Boonstra, Tjeerd W.; Danna-Dos-Santos, Alessander; Xie, Hong-Bo; Roerdink, Melvyn; Stins, John F.; Breakspear, Michael

2015-12-01

Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

Acoustic Pattern Recognition on Android Devices

DEFF Research Database (Denmark)

Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild

2013-01-01

an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....

Connectivity trajectory across lifespan differentiates the precuneus from the default network.

Science.gov (United States)

Yang, Zhi; Chang, Catie; Xu, Ting; Jiang, Lili; Handwerker, Daniel A; Castellanos, F Xavier; Milham, Michael P; Bandettini, Peter A; Zuo, Xi-Nian

2014-04-01

The default network of the human brain has drawn much attention due to its relevance to various brain disorders, cognition, and behavior. However, its functional components and boundaries have not been precisely defined. There is no consensus as to whether the precuneus, a hub in the functional connectome, acts as part of the default network. This discrepancy is more critical for brain development and aging studies: it is not clear whether age has a stronger impact on the default network or precuneus, or both. We used Generalized Ranking and Averaging Independent Component Analysis by Reproducibility (gRAICAR) to investigate the lifespan trajectories of intrinsic functional networks. By estimating individual-specific spatial components and aligning them across subjects, gRAICAR measures the spatial variation of component maps across a population without constraining the same components to appear in every subject. In a cross-lifespan fMRI dataset (N=126, 7-85years old), we observed stronger age dependence in the spatial pattern of a precuneus-dorsal posterior cingulate cortex network compared to the default network, despite the fact that the two networks exhibit considerable spatial overlap and temporal correlation. These results remained even when analyses were restricted to a subpopulation with very similar head motion across age. Our analyses further showed that the two networks tend to merge with increasing age. Post-hoc analyses of functional connectivity confirmed the distinguishable cross-lifespan trajectories between the two networks. Based on these observations, we proposed a dynamic model of cross-lifespan functional segregation and integration between the two networks, suggesting that the precuneus network may have a different functional role than the default network, which declines with age. These findings have implications for understanding the functional roles of the default network, gaining insight into its dynamics throughout life, and guiding

Measurements of brain microstructure and connectivity with diffusion MRI

Directory of Open Access Journals (Sweden)

Ching-Po Lin

2011-12-01

Full Text Available By probing direction-dependent diffusivity of water molecules, diffusion MRI has shown its capability to reflect the microstructural tissue status and to estimate the neural orientation and pathways in the living brain. This approach has supplied novel insights into in-vivo human brain connections. By detecting the connection patterns, anatomical architecture and structural integrity between cortical regions or subcortical nuclei in the living human brain can be easily identified. It thus opens a new window on brain connectivity studies and disease processes. During the past years, there is a growing interest in exploring the connectivity patterns of the human brain. Specifically, the utilities of noninvasive neuroimaging data and graph theoretical analysis have provided important insights into the anatomical connections and topological pattern of human brain structural networks in vivo. Here, we review the progress of this important technique and the recent methodological and application studies utilizing graph theoretical approaches on brain structural networks with structural MRI and diffusion MRI.

Stimulus number, duration and intensity encoding in randomly connected attractor networks with synaptic depression

Directory of Open Access Journals (Sweden)

Paul eMiller

2013-05-01

Full Text Available Randomly connected recurrent networks of excitatory groups of neurons can possess a multitude of attractor states. When the internal excitatory synapses of these networks are depressing, the attractor states can be destabilized with increasing input. This leads to an itinerancy, where with either repeated transient stimuli, or increasing duration of a single stimulus, the network activity advances through sequences of attractor states. We find that the resulting network state, which persists beyond stimulus offset, can encode the number of stimuli presented via a distributed representation of neural activity with non-monotonic tuning curves for most neurons. Increased duration of a single stimulus is encoded via different distributed representations, so unlike an integrator, the network distinguishes separate successive presentations of a short stimulus from a single presentation of a longer stimulus with equal total duration. Moreover, different amplitudes of stimulus cause new, distinct activity patterns, such that changes in stimulus number, duration and amplitude can be distinguished from each other. These properties of the network depend on dynamic depressing synapses, as they disappear if synapses are static. Thus short-term synaptic depression allows a network to store separately the different dynamic properties of a spatially constant stimulus.

Disrupted functional connectivity in dorsal and ventral attention networks during attention orienting in autism spectrum disorders.

Science.gov (United States)

Fitzgerald, Jacqueline; Johnson, Katherine; Kehoe, Elizabeth; Bokde, Arun L W; Garavan, Hugh; Gallagher, Louise; McGrath, Jane

2015-04-01

Attention orienting is a cognitive process that facilitates the movement of attention focus from one location to another: this may be impaired in autism spectrum disorder (ASD). Dorsal and ventral attention networks (DAN and VAN) sub-serve the process of attention orienting. This study investigated the functional connectivity of attention orienting in these networks in ASD using the Posner Cueing Task. Twenty-one adolescents with ASD and 21 age and IQ matched controls underwent functional magnetic resonance imaging. A psychophysical interaction (PPI) analysis was implemented to investigate task-dependent functional connectivity, measuring synchronicity of brain regions during the task. Regions of interest (ROI) were selected to explore functional connectivity in the DAN during cue-only conditions and in the VAN during invalid and valid trials. Behaviourally, the ASD and control groups performed the task in a similar manner. Functional MRI results indicated that the ASD and control groups activated similar brain regions. During invalid trials (VAN), the ASD group showed significant positive functional connectivity to multiple brain regions, whilst the control group demonstrated negative connectivity. During valid trials (VAN), the two groups also showed contrasting patterns of connectivity. In the cue-only conditions (DAN), the ASD group showed weaker functional connectivity. The DAN analysis suggests that the ASD group has weaker coherence between brain areas involved in goal-driven, endogenous attention control. The strong positive functional connectivity exhibited by the ASD group in the VAN during the invalid trials suggests that individuals with ASD may generate compensatory mechanisms to achieve neurotypical behaviour. These results support the theory of abnormal cortical connectivity in autism. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

Viewing socio-affective stimuli increases connectivity within an extended default mode network.

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Göttlich, Martin; Ye, Zheng; Rodriguez-Fornells, Antoni; Münte, Thomas F; Krämer, Ulrike M

2017-03-01

Empathy is an essential ability for prosocial behavior. Previous imaging studies identified a number of brain regions implicated in affective and cognitive aspects of empathy. In this study, we investigated the neural correlates of empathy from a network perspective using graph theory and beta-series correlations. Two independent data sets were acquired using the same paradigm that elicited empathic responses to socio-affective stimuli. One data set was used to define the network nodes and modular structure, the other data set was used to investigate the effects of emotional versus neutral stimuli on network connectivity. Emotional relative to neutral stimuli increased connectivity between 74 nodes belonging to different networks. Most of these nodes belonged to an extended default mode network (eDMN). The other nodes belonged to a cognitive control network or visual networks. Within the eDMN, posterior STG/TPJ regions were identified as provincial hubs. The eDMN also showed stronger connectivity to the cognitive control network encompassing lateral PFC regions. Connector hubs between the two networks were posterior cingulate cortex and ventrolateral PFC. This stresses the advantage of a network approach as regions similarly modulated by task conditions can be dissociated into distinct networks and regions crucial for network integration can be identified. Copyright © 2017 Elsevier Inc. All rights reserved.

Fabrication of microstamps and patterned cell network

International Nuclear Information System (INIS)

Seong, Nak Seon; Pak, James Jung Ho; Choi, Ju Hee; Ahn, Dong June; Hwang, Seong Min; Lee, Kyung J.

2002-01-01

Elastomeric stamps with micrometer-sized grids are fabricated for building biological cell networks by design. Polymerized polydimethyl-siloxane (PDMS) stamps are cast in a variety of different molds prepared by micro-electro mechanical systems (MEMS) technology. Micro square-grid patterns of 3-aminopropyl triethoxy silane (APS) are successfully imprinted on glass plates, and patterned networks of cardiac cells are obtained as designed. The resulting cellular networks clearly demonstrate that cell attachment and growth are greatly favored on APS-treated thin tracks. Here, we report the technical details related to the fabrication of microstamps, to the stamping procedure, and to the culture method. The potential applications of patterned cellular networks are also discussed

Measures for brain connectivity analysis: nodes centrality and their invariant patterns

Science.gov (United States)

da Silva, Laysa Mayra Uchôa; Baltazar, Carlos Arruda; Silva, Camila Aquemi; Ribeiro, Mauricio Watanabe; de Aratanha, Maria Adelia Albano; Deolindo, Camila Sardeto; Rodrigues, Abner Cardoso; Machado, Birajara Soares

2017-07-01

The high dynamical complexity of the brain is related to its small-world topology, which enable both segregated and integrated information processing capabilities. Several measures of connectivity estimation have already been employed to characterize functional brain networks from multivariate electrophysiological data. However, understanding the properties of each measure that lead to a better description of the real topology and capture the complex phenomena present in the brain remains challenging. In this work we compared four nonlinear connectivity measures and show that each method characterizes distinct features of brain interactions. The results suggest an invariance of global network parameters from different behavioral states and that more complete description may be reached considering local features, independently of the connectivity measure employed. Our findings also point to future perspectives in connectivity studies that combine distinct and complementary dependence measures in assembling higher dimensions manifolds.

Quantifying the connectivity of scale-free and biological networks

Energy Technology Data Exchange (ETDEWEB)

Shiner, J.S. E-mail: shiner@alumni.duke.edu; Davison, Matt E-mail: mdavison@uwo.ca

2004-07-01

Scale-free and biological networks follow a power law distribution p{sub k}{proportional_to}k{sup -{alpha}} for the probability that a node is connected to k other nodes; the corresponding ranges for {alpha} (biological: 1<{alpha}<2; scale-free: 2<{alpha}{<=}3) yield a diverging variance for the connectivity k and lack of predictability for the average connectivity. Predictability can be achieved with the Renyi, Tsallis and Landsberg-Vedral extended entropies and corresponding 'disorders' for correctly chosen values of the entropy index q. Escort distributions p{sub k}{proportional_to}k{sup -{alpha}}{sup q} with q>3/{alpha} also yield a nondiverging variance and predictability. It is argued that the Tsallis entropies may be the appropriate quantities for the study of scale-free and biological networks.

Working Memory Modulation of Frontoparietal Network Connectivity in First-Episode Schizophrenia

DEFF Research Database (Denmark)

Nielsen, Jesper Duemose; Madsen, Kristoffer Hougaard; Wang, Zheng

2017-01-01

Working memory (WM) impairment is regarded as a core aspect of schizophrenia. However, the neural mechanisms behind this cognitive deficit remain unclear. The connectivity of a frontoparietal network is known to be important for subserving WM. Using functional magnetic resonance imaging, the curr......Working memory (WM) impairment is regarded as a core aspect of schizophrenia. However, the neural mechanisms behind this cognitive deficit remain unclear. The connectivity of a frontoparietal network is known to be important for subserving WM. Using functional magnetic resonance imaging......, the current study investigated whether WM-dependent modulation of effective connectivity in this network is affected in a group of first-episode schizophrenia (FES) patients compared with similarly performing healthy participants during a verbal n-back task. Dynamic causal modeling (DCM) of the coupling...... between regions (left inferior frontal gyrus (IFG), left inferior parietal lobe (IPL), and primary visual area) identified in a psychophysiological interaction (PPI) analysis was performed to characterize effective connectivity during the n-back task. The PPI analysis revealed that the connectivity...

Resting-state brain networks revealed by granger causal connectivity in frogs.

Science.gov (United States)

Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

2016-10-15

Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Anti-correlated cortical networks of intrinsic connectivity in the rat brain.

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Schwarz, Adam J; Gass, Natalia; Sartorius, Alexander; Risterucci, Celine; Spedding, Michael; Schenker, Esther; Meyer-Lindenberg, Andreas; Weber-Fahr, Wolfgang

2013-01-01

In humans, resting-state blood oxygen level-dependent (BOLD) signals in the default mode network (DMN) are temporally anti-correlated with those from a lateral cortical network involving the frontal eye fields, secondary somatosensory and posterior insular cortices. Here, we demonstrate the existence of an analogous lateral cortical network in the rat brain, extending laterally from anterior secondary sensorimotor regions to the insular cortex and exhibiting low-frequency BOLD fluctuations that are temporally anti-correlated with a midline "DMN-like" network comprising posterior/anterior cingulate and prefrontal cortices. The primary nexus for this anti-correlation relationship was the anterior secondary motor cortex, close to regions that have been identified with frontal eye fields in the rat brain. The anti-correlation relationship was corroborated after global signal removal, underscoring this finding as a robust property of the functional connectivity signature in the rat brain. These anti-correlated networks demonstrate strong anatomical homology to networks identified in human and monkey connectivity studies, extend the known preserved functional connectivity relationships between rodent and primates, and support the use of resting-state functional magnetic resonance imaging as a translational imaging method between rat models and humans.

Inferring the physical connectivity of complex networks from their functional dynamics

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Holm Liisa

2010-05-01

Full Text Available Abstract Background Biological networks, such as protein-protein interactions, metabolic, signalling, transcription-regulatory networks and neural synapses, are representations of large-scale dynamic systems. The relationship between the network structure and functions remains one of the central problems in current multidisciplinary research. Significant progress has been made toward understanding the implication of topological features for the network dynamics and functions, especially in biological networks. Given observations of a network system's behaviours or measurements of its functional dynamics, what can we conclude of the details of physical connectivity of the underlying structure? Results We modelled the network system by employing a scale-free network of coupled phase oscillators. Pairwise phase coherence (PPC was calculated for all the pairs of oscillators to present functional dynamics induced by the system. At the regime of global incoherence, we observed a Significant pairwise synchronization only between two nodes that are physically connected. Right after the onset of global synchronization, disconnected nodes begin to oscillate in a correlated fashion and the PPC of two nodes, either connected or disconnected, depends on their degrees. Based on the observation of PPCs, we built a weighted network of synchronization (WNS, an all-to-all functionally connected network where each link is weighted by the PPC of two oscillators at the ends of the link. In the regime of strong coupling, we observed a Significant similarity in the organization of WNSs induced by systems sharing the same substrate network but different configurations of initial phases and intrinsic frequencies of oscillators. We reconstruct physical network from the WNS by choosing the links whose weights are higher than a given threshold. We observed an optimal reconstruction just before the onset of global synchronization. Finally, we correlated the topology of the

Network connection of distributed electricity production - a preliminary study

International Nuclear Information System (INIS)

Pleym, Anngjerd; Mogstad, Olve

2002-01-01

It will be necessary to lower the barriers for utilisation of distributed energy sources in order to increase the use of such sources in Norway. A relatively extensive R and D activity would be required for reaching this goal. Available Norwegian and international guidelines and technical requirements with respect to network connection of the distributed energy sources are studied with the aim of exposing needs for further R and D initiatives. A limited monitor is also carried out among the Norwegian network businesses with distributed units in their networks. The results show that the main focus in the R and D activities has drifted away from establishing guidelines for technical requirements for network coupling. Some verification work remains in investigating the usefulness of the existing international and the specific commercial network guidelines. For the network industry the main focus must be on the two following areas: 1) How will large concentrations of distributed production units connected to the same network influence the voltage quality and the delivery reliability in the networks. 2) How can the network businesses employ the distributed production units in their networks. A Nordic project (Finland, Sweden, Norway) which will study these problems is being established. Large national scientific institutions will be involved. The executive committee will consist of representatives from Finenergy, Elforsk and EBL Kompetanse and other financing institutions and industries. A Finnish business Merinova, is to be appointed to the project leadership

Linear Approach for Synchronous State Stability in Fully Connected PLL Networks

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José R. C. Piqueira

2008-01-01

Full Text Available Synchronization is an essential feature for the use of digital systems in telecommunication networks, integrated circuits, and manufacturing automation. Formerly, master-slave (MS architectures, with precise master clock generators sending signals to phase-locked loops (PLLs working as slave oscillators, were considered the best solution. Nowadays, the development of wireless networks with dynamical connectivity and the increase of the size and the operation frequency of integrated circuits suggest that the distribution of clock signals could be more efficient if distributed solutions with fully connected oscillators are used. Here, fully connected networks with second-order PLLs as nodes are considered. In previous work, how the synchronous state frequency for this type of network depends on the node parameters and delays was studied and an expression for the long-term frequency was derived (Piqueira, 2006. Here, by taking the first term of the Taylor series expansion for the dynamical system description, it is shown that for a generic network with N nodes, the synchronous state is locally asymptotically stable.

Method of Geometric Connected Disk Cover Problem for UAV realy network deployment

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Chuang Liu

2017-01-01

Full Text Available Aiming at the problem of the effective connectivity of a large number of mobile combat units in the future aeronautic swarm operation, this paper proposes an idea of using UAV(Unmanned Aerial Vehicle to build, and studies the deployment of the network. User coverage and network connectivity are important for a relay network planning which are studied separately in traditional ways. In order to effectively combine these two factors while the network’s survivability is taken into account. Firstly, the concept of node aggregation degree is proposed. Secondly, a performance evaluation parameter for UAV relay network is proposed based on node aggregation degree, then analyzes the lack of deterministic deployment and presents one a PSO (VFA-PSO deployment algorithm based on virtual force. Finally, compared with the existing algorithms, the validity and stability of the algorithm are verified. The experimental results show that the VFA-PSO algorithm can effectively improve the network coverage and the survivability of the network under the premise of ensuring the network connectivity, and has better deployment effect.

Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

DEFF Research Database (Denmark)

Hillmann, Robert; Trier, Matthias

2012-01-01

. The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks...... with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social...... networks exhibits a strong tendency towards reciprocity, followed by the dominance of hierarchy as an intermediate step leading to social clustering with hubs and transitivity effects for both positive and negative sentiments to the same extend. Sentiments embedded in exchanged textual messages do only...

Analysis of changes in farm pond network connectivity in the peri-urban landscape of the Taoyuan area, Taiwan.

Science.gov (United States)

Huang, Shu-Li; Lee, Ying-Chieh; Budd, William W; Yang, Min-Chia

2012-04-01

The farm pond system for irrigation is the most prominent feature in the Taoyuan area, Taiwan, giving the region a unique landscape and hydrological character. Although this area had more than 3,290 ponds in the 1970s, fewer than 1,800 now remain. This study analyzes changes in irrigation farm ponds and the canal network landscape in the Taoyuan area. The spatial and temporal changes to ponds and the canal network on the Taoyuan plain were examined graphically for each spatial unit (2,765 m × 2,525 m) using aerial photographs for 1979 and 2005. Landscape metrics were calculated to analyze landscape change associated with increased urbanization. Landscape indices of connectivity and circuitry were utilized to describe changes in the configuration of ponds and canal networks. The total length of canals and total number of ponds in the study area decreased significantly during 1979-2005. The average values of connectivity indices (γ- and α-index) also decreased during 1979-2005, reflecting degradation of canal networks due to urban sprawl. A multivariate technique was applied to portion the study area into three zones according to changes to land cover, ponds, and canal networks. The effects of urban sprawl on the spatial pattern of ponds and canal networks are discussed.

Pattern-Oriented Reengineering of a Network System

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Chung-Horng Lung

2004-08-01

Full Text Available Reengineering is to reorganize and modify existing systems to enhance them or to make them more maintainable. Reengineering is usually necessary as systems evolve due to changes in requirements, technologies, and/or personnel. Design patterns capture recurring structures and dynamics among software participants to facilitate reuse of successful designs. Design patterns are common and well studied in network systems. In this project, we reengineer part of a network system with some design patterns to support future evolution and performance improvement. We start with reverse engineering effort to understand the system and recover its high level architecture. Then we apply concurrent and networked design patterns to restructure the main sub-system. Those patterns include Half-Sync/Half-Async, Monitor Object, and Scoped Locking idiom. The resulting system is more maintainable and has better performance.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia

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Van Snellenberg, Jared X.; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa

2016-01-01

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during an n-back working-memory task) and positron emission tomography using the radiotracer [11C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

Science.gov (United States)

Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad; Slifstein, Mark; Wang, Zhishun; Moore, Holly; Abi-Dargham, Anissa; Horga, Guillermo

2016-04-13

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. It is unclear

Stomach-brain synchrony reveals a novel, delayed-connectivity resting-state network in humans.

Science.gov (United States)

Rebollo, Ignacio; Devauchelle, Anne-Dominique; Béranger, Benoît; Tallon-Baudry, Catherine

2018-03-21

Resting-state networks offer a unique window into the brain's functional architecture, but their characterization remains limited to instantaneous connectivity thus far. Here, we describe a novel resting-state network based on the delayed connectivity between the brain and the slow electrical rhythm (0.05 Hz) generated in the stomach. The gastric network cuts across classical resting-state networks with partial overlap with autonomic regulation areas. This network is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. The network is characterized by a precise temporal sequence of activations within a gastric cycle, beginning with somato-motor cortices and ending with the extrastriate body area and dorsal precuneus. Our results demonstrate that canonical resting-state networks based on instantaneous connectivity represent only one of the possible partitions of the brain into coherent networks based on temporal dynamics. © 2018, Rebollo et al.

Connection Management and Recovery Strategies under Epidemic Network Failures in Optical Transport Networks

DEFF Research Database (Denmark)

Fagertun, Anna Manolova; Ruepp, Sarah Renée

2014-01-01

The current trend in deploying automatic control plane solutions for increased flexibility in the optical transport layer leads to numerous advantages for both the operators and the customers, but also pose challenges related to the stability of the network and its ability to operate in a robust...... manner under attacks. This work proposes four policies for failure handling in a connection-oriented optical transport network, under Generalized MultiProtocol Label Switching control plane, and evaluates their performance under multiple correlated large-scale failures. We employ the Susceptible...... of their transport infrastructures. Applying proactive methods for avoiding areas where epidemic failures spread results in 50% less connections requiring recovery, which translates in improved quality of service to customers....

Insecure Network, Unknown Connection: Understanding Wi-Fi Privacy Assumptions of Mobile Device Users

Directory of Open Access Journals (Sweden)

Bram Bonné

2017-07-01

Full Text Available Smartphones and other mobile devices have proliferated in the past five years. The expectation of mobile device users to always be online has led to Wi-Fi networks being offered by a variety of providers. Using these networks introduces multiple security risks. In this work, we assess to what extent the privacy stance of mobile device users corresponds with their actual behavior by conducting a study with 108 participants. Our methodology consists of monitoring Wi-Fi networks that the participants’ devices connect to and the connections made by apps on these devices, for a period of 30 days. Afterwards, participants are surveyed about their awareness and privacy sensitiveness. We show that while a higher expertise in computer networks corresponds to more awareness about the connections made by apps, neither this expertise nor the actual privacy stance of the participant translates to better security habits. Moreover, participants in general were unaware about a significant part of connections made by apps on their devices, a matter that is worsened by the fact that one third of Wi-Fi networks that participants connect to do not have any security enabled. Based on our results, we provide recommendations to network providers, developers and users on how to improve Wi-Fi security for mobile devices.

Altered Functional Connectivity of Insular Subregions in Alzheimer’s Disease

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Xingyun Liu

2018-04-01

Full Text Available Recent researches have demonstrated that the insula is the crucial hub of the human brain networks and most vulnerable region of Alzheimer’s disease (AD. However, little is known about the changes of functional connectivity of insular subregions in the AD patients. In this study, we collected resting-state functional magnetic resonance imaging (fMRI data including 32 AD patients and 38 healthy controls (HCs. By defining three subregions of insula, we mapped whole-brain resting-state functional connectivity (RSFC and identified several distinct RSFC patterns of the insular subregions: For positive connectivity, three cognitive-related RSFC patterns were identified within insula that suggest anterior-to-posterior functional subdivisions: (1 an dorsal anterior zone of the insula that exhibits RSFC with executive control network (ECN; (2 a ventral anterior zone of insula, exhibits functional connectivity with the salience network (SN; (3 a posterior zone along the insula exhibits functional connectivity with the sensorimotor network (SMN. In addition, we found significant negative connectivities between the each insular subregion and several special default mode network (DMN regions. Compared with controls, the AD patients demonstrated distinct disruption of positive RSFCs in the different network (ECN and SMN, suggesting the impairment of the functional integrity. There were no differences of the positive RSFCs in the SN between the two groups. On the other hand, several DMN regions showed increased negative RSFCs to the sub-region of insula in the AD patients, indicating compensatory plasticity. Furthermore, these abnormal insular subregions RSFCs are closely correlated with cognitive performances in the AD patients. Our findings suggested that different insular subregions presented distinct RSFC patterns with various functional networks, which are differently affected in the AD patients.

Correspondence Between Aberrant Intrinsic Network Connectivity and Gray-Matter Volume in the Ventral Brain of Preterm Born Adults.

Science.gov (United States)

Bäuml, Josef G; Daamen, Marcel; Meng, Chun; Neitzel, Julia; Scheef, Lukas; Jaekel, Julia; Busch, Barbara; Baumann, Nicole; Bartmann, Peter; Wolke, Dieter; Boecker, Henning; Wohlschläger, Afra M; Sorg, Christian

2015-11-01

Widespread brain changes are present in preterm born infants, adolescents, and even adults. While neurobiological models of prematurity facilitate powerful explanations for the adverse effects of preterm birth on the developing brain at microscale, convincing linking principles at large-scale level to explain the widespread nature of brain changes are still missing. We investigated effects of preterm birth on the brain's large-scale intrinsic networks and their relation to brain structure in preterm born adults. In 95 preterm and 83 full-term born adults, structural and functional magnetic resonance imaging at-rest was used to analyze both voxel-based morphometry and spatial patterns of functional connectivity in ongoing blood oxygenation level-dependent activity. Differences in intrinsic functional connectivity (iFC) were found in cortical and subcortical networks. Structural differences were located in subcortical, temporal, and cingulate areas. Critically, for preterm born adults, iFC-network differences were overlapping and correlating with aberrant regional gray-matter (GM) volume specifically in subcortical and temporal areas. Overlapping changes were predicted by prematurity and in particular by neonatal medical complications. These results provide evidence that preterm birth has long-lasting effects on functional connectivity of intrinsic networks, and these changes are specifically related to structural alterations in ventral brain GM. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Global patterns of fragmentation and connectivity of mammalian carnivore habitat

OpenAIRE

Crooks, Kevin R.; Burdett, Christopher L.; Theobald, David M.; Rondinini, Carlo; Boitani, Luigi

2011-01-01

Although mammalian carnivores are vulnerable to habitat fragmentation and require landscape connectivity, their global patterns of fragmentation and connectivity have not been examined. We use recently developed high-resolution habitat suitability models to conduct comparative analyses and to identify global hotspots of fragmentation and connectivity for the world's terrestrial carnivores. Species with less fragmentation (i.e. more interior high-quality habitat) had larger geographical ranges...

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

Science.gov (United States)

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks.

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Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

2016-09-01

In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

Brain Connectivity and Visual Attention

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Parks, Emily L.

2013-01-01

Abstract Emerging hypotheses suggest that efficient cognitive functioning requires the integration of separate, but interconnected cortical networks in the brain. Although task-related measures of brain activity suggest that a frontoparietal network is associated with the control of attention, little is known regarding how components within this distributed network act together or with other networks to achieve various attentional functions. This review considers both functional and structural studies of brain connectivity, as complemented by behavioral and task-related neuroimaging data. These studies show converging results: The frontal and parietal cortical regions are active together, over time, and identifiable frontoparietal networks are active in relation to specific task demands. However, the spontaneous, low-frequency fluctuations of brain activity that occur in the resting state, without specific task demands, also exhibit patterns of connectivity that closely resemble the task-related, frontoparietal attention networks. Both task-related and resting-state networks exhibit consistent relations to behavioral measures of attention. Further, anatomical structure, particularly white matter pathways as defined by diffusion tensor imaging, places constraints on intrinsic functional connectivity. Lastly, connectivity analyses applied to investigate cognitive differences across individuals in both healthy and diseased states suggest that disconnection of attentional networks is linked to deficits in cognitive functioning, and in extreme cases, to disorders of attention. Thus, comprehensive theories of visual attention and their clinical translation depend on the continued integration of behavioral, task-related neuroimaging, and brain connectivity measures. PMID:23597177

Differential deactivation during mentalizing and classification of autism based on default mode network connectivity.

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Donna L Murdaugh

Full Text Available The default mode network (DMN is a collection of brain areas found to be consistently deactivated during task performance. Previous neuroimaging studies of resting state have revealed reduced task-related deactivation of this network in autism. We investigated the DMN in 13 high-functioning adults with autism spectrum disorders (ASD and 14 typically developing control participants during three fMRI studies (two language tasks and a Theory-of-Mind (ToM task. Each study had separate blocks of fixation/resting baseline. The data from the task blocks and fixation blocks were collated to examine deactivation and functional connectivity. Deficits in the deactivation of the DMN in individuals with ASD were specific only to the ToM task, with no group differences in deactivation during the language tasks or a combined language and self-other discrimination task. During rest blocks following the ToM task, the ASD group showed less deactivation than the control group in a number of DMN regions, including medial prefrontal cortex (MPFC, anterior cingulate cortex, and posterior cingulate gyrus/precuneus. In addition, we found weaker functional connectivity of the MPFC in individuals with ASD compared to controls. Furthermore, we were able to reliably classify participants into ASD or typically developing control groups based on both the whole-brain and seed-based connectivity patterns with accuracy up to 96.3%. These findings indicate that deactivation and connectivity of the DMN were altered in individuals with ASD. In addition, these findings suggest that the deficits in DMN connectivity could be a neural signature that can be used for classifying an individual as belonging to the ASD group.

Restoration of lost connectivity of partitioned wireless sensor networks

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Virender Ranga

2016-05-01

Full Text Available The lost connectivity due to failure of large scale nodes plays major role to degrade the system performance by generating unnecessary overhead or sometimes totally collapse the active network. There are many issues and challenges to restore the lost connectivity in an unattended scenario, i.e. how many recovery nodes will be sufficient and on which locations these recovery nodes have to be placed. A very few centralized and distributed approaches have been proposed till now. The centralized approaches are good for a scenario where information about the disjoint network, i.e. number of disjoint segments and their locations are well known in advance. However, for a scenario where such information is unknown due to the unattended harsh environment, a distributed approach is a better solution to restore the partitioned network. In this paper, we have proposed and implemented a semi-distributed approach called Relay node Placement using Fermat Point (RPFP. The proposed approach is capable of restoring lost connectivity with small number of recovery relay nodes and it works for any number of disjoint segments. The simulation experiment results show effectiveness of our approach as compared to existing benchmark approaches.

Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment.

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Bai, Feng; Shu, Ni; Yuan, Yonggui; Shi, Yongmei; Yu, Hui; Wu, Di; Wang, Jinhui; Xia, Mingrui; He, Yong; Zhang, Zhijun

2012-03-21

Alzheimer's disease (AD) can be conceptualized as a disconnection syndrome. Both remitted geriatric depression (RGD) and amnestic mild cognitive impairment (aMCI) are associated with a high risk for developing AD. However, little is known about the similarities and differences in the topological patterns of white matter (WM) structural networks between RGD and aMCI. In this study, diffusion tensor imaging and deterministic tractography were used to map the human WM networks of 35 RGD patients, 38 aMCI patients, and 30 healthy subjects. Furthermore, graph theoretical methods were applied to investigate the alterations in the global and regional properties of the WM network in these patients. First, both the RGD and aMCI patients showed abnormal global topology in their WM networks (i.e., reduced network strength, reduced global efficiency, and increased absolute path length) compared with the controls, and there were no significant differences in these global network properties between the patient groups. Second, similar deficits of the regional and connectivity characteristics in the WM networks were primarily found in the frontal brain regions of RGD and aMCI patients compared with the controls, while a different nodal efficiency of the posterior cingulate cortex and several prefrontal brain regions were also observed between the patient groups. Together, our study provides direct evidence for the association of a great majority of convergent and a minority of divergent connectivity of WM structural networks between RGD and aMCI patients, which may lead to increasing attention in defining a population at risk of AD.

Connecting to young adults: an online social network survey of beliefs and attitudes associated with prescription opioid misuse among college students.

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Lord, Sarah; Brevard, Julie; Budman, Simon

2011-01-01

A survey of motives and attitudes associated with patterns of nonmedical prescription opioid medication use among college students was conducted on Facebook, a popular online social networking Web site. Response metrics for a 2-week random advertisement post, targeting students who had misused prescription medications, surpassed typical benchmarks for online marketing campaigns and yielded 527 valid surveys. Respondent characteristics, substance use patterns, and use motives were consistent with other surveys of prescription opioid use among college populations. Results support the potential of online social networks to serve as powerful vehicles to connect with college-aged populations about their drug use. Limitations of the study are noted.

Connectivity, flow and transport in network models of fractured media

International Nuclear Information System (INIS)

Robinson, P.C.

1984-10-01

In order to evaluate the safety of radioactive waste disposal underground it is important to understand the way in which radioactive material is transported through the rock to the surface. If the rock is fractured the usual models may not be applicable. In this work we look at three aspects of fracture networks: connectivity, flow and transport. These are studied numerically by generating fracture networks in a computer and modelling the processes which occur. Connectivity relates to percolation theory, and critical densities for fracture systems are found in two and three dimensions. The permeability of two-dimensional networks is studied. The way that permeability depends on fracture density, network size and spread of fracture length can be predicted using a cut lattice model. Transport through the fracture network by convection through the fractures and mixing at the intersections is studied. The Fickian dispersion equation does not describe the resulting hydrodynamic dispersion. Extensions to the techniques to three dimensions and to include other processes are discussed. (author)

Graph Analysis and Modularity of Brain Functional Connectivity Networks: Searching for the Optimal Threshold

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Cécile Bordier

2017-08-01

Full Text Available Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.

Introspection-based Periodicity Awareness Model for Intermittently Connected Mobile Networks

NARCIS (Netherlands)

Türkes, Okan; Scholten, Johan; Havinga, Paul J.M.

Recently, context awareness in Intermittently Connected Mobile Networks (ICMNs) has gained popularity in order to discover social similarities among mobile entities. Nevertheless, most of the contextual methods depend on network knowledge obtained with unrealistic scenarios. Mobile entities should

Intrinsic connectivity of neural networks in the awake rabbit.

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Schroeder, Matthew P; Weiss, Craig; Procissi, Daniel; Disterhoft, John F; Wang, Lei

2016-04-01

The way in which the brain is functionally connected into different networks has emerged as an important research topic in order to understand normal neural processing and signaling. Since some experimental manipulations are difficult or unethical to perform in humans, animal models are better suited to investigate this topic. Rabbits are a species that can undergo MRI scanning in an awake and conscious state with minimal preparation and habituation. In this study, we characterized the intrinsic functional networks of the resting New Zealand White rabbit brain using BOLD fMRI data. Group independent component analysis revealed seven networks similar to those previously found in humans, non-human primates and/or rodents including the hippocampus, default mode, cerebellum, thalamus, and visual, somatosensory, and parietal cortices. For the first time, the intrinsic functional networks of the resting rabbit brain have been elucidated demonstrating the rabbit's applicability as a translational animal model. Without the confounding effects of anesthetics or sedatives, future experiments may employ rabbits to understand changes in neural connectivity and brain functioning as a result of experimental manipulation (e.g., temporary or permanent network disruption, learning-related changes, and drug administration). Copyright © 2016 Elsevier Inc. All rights reserved.

Complex networks of functional connectivity in a wetland reconnected to its floodplain

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Larsen, Laurel G.; Newman, Susan; Saunders, Colin; Harvey, Judson

2017-01-01

Disturbances such as fire or flood, in addition to changing the local magnitude of ecological, hydrological, or biogeochemical processes, can also change their functional connectivity—how those processes interact in space. Complex networks offer promise for quantifying functional connectivity in watersheds. The approach resolves connections between nodes in space based on statistical similarities in perturbation signals (derived from solute time series) and is sensitive to a wider range of timescales than traditional mass-balance modeling. We use this approach to test hypotheses about how fire and flood impact ecological and biogeochemical dynamics in a wetland (Everglades, FL, USA) that was reconnected to its floodplain. Reintroduction of flow pulses after decades of separation by levees fundamentally reconfigured functional connectivity networks. The most pronounced expansion was that of the calcium network, which reflects periphyton dynamics and may represent an indirect influence of elevated nutrients, despite the comparatively smaller observed expansion of phosphorus networks. With respect to several solutes, periphyton acted as a “biotic filter,” shifting perturbations in water-quality signals to different timescales through slow but persistent transformations of the biotic community. The complex-networks approach also revealed portions of the landscape that operate in fundamentally different regimes with respect to dissolved oxygen, separated by a threshold in flow velocity of 1.2 cm/s, and suggested that complete removal of canals may be needed to restore connectivity with respect to biogeochemical processes. Fire reconfigured functional connectivity networks in a manner that reflected localized burn severity, but had a larger effect on the magnitude of solute concentrations.

Discovery of spatio-temporal patterns from location-based social networks

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Béjar, J.; Álvarez, S.; García, D.; Gómez, I.; Oliva, L.; Tejeda, A.; Vázquez-Salceda, J.

2016-03-01

Location-based social networks (LBSNs) such as Twitter or Instagram are a good source for user spatio-temporal behaviour. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains such as mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyse the frequent spatio-temporal patterns that users share when living and visiting a city. This behaviour is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyse how the spatio-temporal behaviour of a large number of users can be segmented in different profiles. These behavioural profiles are obtained by means of clustering algorithms that show the different patterns of behaviour of visitors and citizens. The data analysed were obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kinds of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.

Altered Network Oscillations and Functional Connectivity Dynamics in Children Born Very Preterm.

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Moiseev, Alexander; Doesburg, Sam M; Herdman, Anthony T; Ribary, Urs; Grunau, Ruth E

2015-09-01

Structural brain connections develop atypically in very preterm children, and altered functional connectivity is also evident in fMRI studies. Such alterations in brain network connectivity are associated with cognitive difficulties in this population. Little is known, however, about electrophysiological interactions among specific brain networks in children born very preterm. In the present study, we recorded magnetoencephalography while very preterm children and full-term controls performed a visual short-term memory task. Regions expressing task-dependent activity changes were identified using beamformer analysis, and inter-regional phase synchrony was calculated. Very preterm children expressed altered regional recruitment in distributed networks of brain areas, across standard physiological frequency ranges including the theta, alpha, beta and gamma bands. Reduced oscillatory synchrony was observed among task-activated brain regions in very preterm children, particularly for connections involving areas critical for executive abilities, including middle frontal gyrus. These findings suggest that inability to recruit neurophysiological activity and interactions in distributed networks including frontal regions may contribute to difficulties in cognitive development in children born very preterm.

Intermittent Theta-Burst Stimulation of the Lateral Cerebellum Increases Functional Connectivity of the Default Network

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Farzan, Faranak; Eldaief, Mark C.; Schmahmann, Jeremy D.; Pascual-Leone, Alvaro

2014-01-01

Cerebral cortical intrinsic connectivity networks share topographically arranged functional connectivity with the cerebellum. However, the contribution of cerebellar nodes to distributed network organization and function remains poorly understood. In humans, we applied theta-burst transcranial magnetic stimulation, guided by subject-specific connectivity, to regions of the cerebellum to evaluate the functional relevance of connections between cerebellar and cerebral cortical nodes in different networks. We demonstrate that changing activity in the human lateral cerebellar Crus I/II modulates the cerebral default mode network, whereas vermal lobule VII stimulation influences the cerebral dorsal attention system. These results provide novel insights into the distributed, but anatomically specific, modulatory impact of cerebellar effects on large-scale neural network function. PMID:25186750

RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks

Energy Technology Data Exchange (ETDEWEB)

Balasundaram, Balabhaskar [Oklahoma State Univ., Stillwater, OK (United States); Butenko, Sergiy [Texas A & M Univ., College Station, TX (United States); Boginski, Vladimir [Univ. of Florida, Gainesville, FL (United States); Uryasev, Stan [Univ. of Florida, Gainesville, FL (United States)

2013-12-25

The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need

Mapping a Careflow Network to assess the connectedness of Connected Health.

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Carroll, Noel; Richardson, Ita

2017-04-01

Connected Health is an emerging and rapidly developing field which has the potential to transform healthcare service systems by increasing its safety, quality and overall efficiency. From a healthcare perspective, process improvement models have mainly focused on the static workflow viewpoint. The objective of this article is to study and model the dynamic nature of healthcare delivery, allowing us to identify where potential issues exist within the service system and to examine how Connected Health technological solutions may support service efficiencies. We explore the application of social network analysis (SNA) as a modelling technique which captures the dynamic nature of a healthcare service. We demonstrate how it can be used to map the 'Careflow Network' and guide Connected Health innovators to examine specific opportunities within the healthcare service. Our results indicate that healthcare technology must be correctly identified and implemented within the Careflow Network to enjoy improvements in service delivery. Oftentimes, prior to making the transformation to Connected Health, researchers use various modelling techniques that fail to identify where Connected Health innovation is best placed in a healthcare service network. Using SNA allows us to develop an understanding of the current operation of healthcare system within which they can effect change. It is important to identify and model the resource exchanges to ensure that the quality and safety of care are enhanced, efficiencies are increased and the overall healthcare service system is improved. We have shown that dynamic models allow us to study the exchange of resources. These are often intertwined within a socio-technical context in an informal manner and not accounted for in static models, yet capture a truer insight on the operations of a Careflow Network.

Incongruent genetic connectivity patterns for VME indicator taxa: implications for the management of New Zealand's vulnerable marine ecosystems

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Clark, M. R.; Gardner, J.; Holland, L.; Zeng, C.; Hamilton, J. S.; Rowden, A. A.

2016-02-01

In the New Zealand region vulnerable marine ecosystems (VMEs) are at risk from commercial fishing activity and future seabed mining. Understanding connectivity among VMEs is important for the design of effective spatial management strategies, i.e. a network of protected areas. To date however, genetic connectivity in the New Zealand region has rarely been documented. As part of a project developing habitat suitability models and spatial management options for VMEs we used DNA sequence data and microsatellite genotyping to assess genetic connectivity for a range of VME indicator taxa, including the coral Desmophyllum dianthus, and the sponges Poecilastra laminaris and Penares palmatoclada. Overall, patterns of connectivity were inconsistent amonst taxa. Nonetheless, genetic data from each taxon were relevant to inform management at a variety of spatial scales. D. dianthus populations in the Kermadec volcanic arc and the Louisville Seamount Chain were indistinguishable, highlighting the importance of considering source-sink dynamics between populations beyond the EEZ in conservation planning. Poecilastra laminaris populations showed significant divergence across the Chatham Rise, in contrast to P. palmatoclada, which had a uniform haplotypic distribution. However, both sponge species exhibited the highest genetic diversity on the Chatham Rise, suggesting that this area is a genetic hotspot. The spatial heterogeneity of genetic patterns of structure suggest that inclusion of several taxa is necessary to facilitate understanding of regional connectivity patterns, variation in which may be attributed to alternate life history strategies, local hydrodynamic regimes, or in some cases, suboptimal sample sizes. Our findings provide important information for use by environmental managers, including summary maps of genetic diversity and barriers to gene flow, which will be used in spatial management decision-support tools.

Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

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Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

2017-08-17

Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

Asymmetric network connectivity using weighted harmonic averages

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Morrison, Greg; Mahadevan, L.

2011-02-01

We propose a non-metric measure of the "closeness" felt between two nodes in an undirected, weighted graph using a simple weighted harmonic average of connectivity, that is a real-valued Generalized Erdös Number (GEN). While our measure is developed with a collaborative network in mind, the approach can be of use in a variety of artificial and real-world networks. We are able to distinguish between network topologies that standard distance metrics view as identical, and use our measure to study some simple analytically tractable networks. We show how this might be used to look at asymmetry in authorship networks such as those that inspired the integer Erdös numbers in mathematical coauthorships. We also show the utility of our approach to devise a ratings scheme that we apply to the data from the NetFlix prize, and find a significant improvement using our method over a baseline.

A New Delay Connection for Long Short-Term Memory Networks.

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Wang, Jianyong; Zhang, Lei; Chen, Yuanyuan; Yi, Zhang

2017-12-17

Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is absent in the LSTM unit. Second, the proposed delay connection helps to bridge the error signals to previous time steps and allows it to be back-propagated across several layers without vanishing too quickly. To evaluate the performance of the proposed delay connections, the DCLSTM model with and without peephole connections was compared with four state-of-the-art recurrent model on two sequence classification tasks. DCLSTM model outperformed the other models with higher accuracy and F1[Formula: see text]score. Furthermore, the networks with multiple stacked DCLSTM layers and the standard LSTM layer were evaluated on Penn Treebank (PTB) language modeling. The DCLSTM model achieved lower perplexity (PPL)/bit-per-character (BPC) than the standard LSTM model. The experiments demonstrate that the learning of the DCLSTM models is more stable and efficient.

Anticipating changes to future connectivity within a network of marine protected areas.

Science.gov (United States)

Coleman, Melinda A; Cetina-Heredia, Paulina; Roughan, Moninya; Feng, Ming; van Sebille, Erik; Kelaher, Brendan P

2017-09-01

Continental boundary currents are projected to be altered under future scenarios of climate change. As these currents often influence dispersal and connectivity among populations of many marine organisms, changes to boundary currents may have dramatic implications for population persistence. Networks of marine protected areas (MPAs) often aim to maintain connectivity, but anticipation of the scale and extent of climatic impacts on connectivity are required to achieve this critical conservation goal in a future of climate change. For two key marine species (kelp and sea urchins), we use oceanographic modelling to predict how continental boundary currents are likely to change connectivity among a network of MPAs spanning over 1000 km of coastline off the coast of eastern Australia. Overall change in predicted connectivity among pairs of MPAs within the network did not change significantly over and above temporal variation within climatic scenarios, highlighting the need for future studies to incorporate temporal variation in dispersal to robustly anticipate likely change. However, the intricacies of connectivity between different pairs of MPAs were noteworthy. For kelp, poleward connectivity among pairs of MPAs tended to increase in the future, whereas equatorward connectivity tended to decrease. In contrast, for sea urchins, connectivity among pairs of MPAs generally decreased in both directions. Self-seeding within higher-latitude MPAs tended to increase, and the role of low-latitude MPAs as a sink for urchins changed significantly in contrasting ways. These projected changes have the potential to alter important genetic parameters with implications for adaptation and ecosystem vulnerability to climate change. Considering such changes, in the context of managing and designing MPA networks, may ensure that conservation goals are achieved into the future. © 2017 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Brain Connectivity Networks and the Aesthetic Experience of Music.

Science.gov (United States)

Reybrouck, Mark; Vuust, Peter; Brattico, Elvira

2018-06-12

Listening to music is above all a human experience, which becomes an aesthetic experience when an individual immerses himself/herself in the music, dedicating attention to perceptual-cognitive-affective interpretation and evaluation. The study of these processes where the individual perceives, understands, enjoys and evaluates a set of auditory stimuli has mainly been focused on the effect of music on specific brain structures, as measured with neurophysiology and neuroimaging techniques. The very recent application of network science algorithms to brain research allows an insight into the functional connectivity between brain regions. These studies in network neuroscience have identified distinct circuits that function during goal-directed tasks and resting states. We review recent neuroimaging findings which indicate that music listening is traceable in terms of network connectivity and activations of target regions in the brain, in particular between the auditory cortex, the reward brain system and brain regions active during mind wandering.

Aberrant Resting-State Functional Connectivity in the Salience Network of Adolescent Chronic Fatigue Syndrome.

Directory of Open Access Journals (Sweden)

Laura Anne Wortinger

Full Text Available Neural network investigations are currently absent in adolescent chronic fatigue syndrome (CFS. In this study, we examine whether the core intrinsic connectivity networks (ICNs are altered in adolescent CFS patients. Eighteen adolescent patients with CFS and 18 aged matched healthy adolescent control subjects underwent resting-state functional magnetic resonance imaging (rfMRI. Data was analyzed using dual-regression independent components analysis, which is a data-driven approach for the identification of independent brain networks. Intrinsic connectivity was evaluated in the default mode network (DMN, salience network (SN, and central executive network (CEN. Associations between network characteristics and symptoms of CFS were also explored. Adolescent CFS patients displayed a significant decrease in SN functional connectivity to the right posterior insula compared to healthy comparison participants, which was related to fatigue symptoms. Additionally, there was an association between pain intensity and SN functional connectivity to the left middle insula and caudate that differed between adolescent patients and healthy comparison participants. Our findings of insula dysfunction and its association with fatigue severity and pain intensity in adolescent CFS demonstrate an aberration of the salience network which might play a role in CFS pathophysiology.

Data for default network reduced functional connectivity in meditators, negatively correlated with meditation expertise

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Aviva Berkovich-Ohana

2016-09-01

Full Text Available FMRI data described here was recorded during resting-state in Mindfulness Meditators (MM and control participants (see “Task-induced activity and resting-state fluctuations undergo similar alterations in visual and DMN areas of long-term meditators” Berkovich-Ohana et al. (2016 [1] for details. MM participants were also scanned during meditation. Analyses focused on functional connectivity within and between the default mode network (DMN and visual network (Vis. Here we show data demonstrating that: 1 Functional connectivity within the DMN and the Visual networks were higher in the control group than in the meditators; 2 Data show an increase for the functional connectivity between the DMN and the Visual networks in the meditators compared to controls; 3 Data demonstrate that functional connectivity both within and between networks reduces during meditation, compared to the resting-state; and 4 A significant negative correlation was found between DMN functional connectivity and meditation expertise. The reader is referred to Berkovich-Ohana et al. (2016 [1] for further interpretation and discussion.

Optimal topology to minimizing congestion in connected communication complex network

Science.gov (United States)

Benyoussef, M.; Ez-Zahraouy, H.; Benyoussef, A.

In this paper, a new model of the interdependent complex network is proposed, based on two assumptions that (i) the capacity of a node depends on its degree, and (ii) the traffic load depends on the distribution of the links in the network. Based on these assumptions, the presented model proposes a method of connection not based on the node having a higher degree but on the region containing hubs. It is found that the final network exhibits two kinds of degree distribution behavior, depending on the kind and the way of the connection. This study reveals a direct relation between network structure and traffic flow. It is found that pc the point of transition between the free flow and the congested phase depends on the network structure and the degree distribution. Moreover, this new model provides an improvement in the traffic compared to the results found in a single network. The same behavior of degree distribution found in a BA network and observed in the real world is obtained; except for this model, the transition point between the free phase and congested phase is much higher than the one observed in a network of BA, for both static and dynamic protocols.

Coverage and Connectivity Issue in Wireless Sensor Networks

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Rachit Trivedi

2013-04-01

Full Text Available Wireless sensor networks (WSNs are an emerging area of interest in research and development. It finds use in military surveillance, health care, environmental monitoring, forest fire detection and smart environments. An important research issue in WSNs is the coverage since cost, area and lifetime are directly validated to it.In this paper we present an overview of WSNs and try to refine the coverage and connectivity issues in wireless sensor networks.

MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

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Artur Popko

2013-06-01

Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

Interaction patterns of nurturant support exchanged in online health social networking.

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Chuang, Katherine Y; Yang, Christopher C

2012-05-03

Expressing emotion in online support communities is an important aspect of enabling e-patients to connect with each other and expand their social resources. Indirectly it increases the amount of support for coping with health issues. Exploring the supportive interaction patterns in online health social networking would help us better understand how technology features impacts user behavior in this context. To build on previous research that identified different types of social support in online support communities by delving into patterns of supportive behavior across multiple computer-mediated communication formats. Each format combines different architectural elements, affecting the resulting social spaces. Our research question compared communication across different formats of text-based computer-mediated communication provided on the MedHelp.org health social networking environment. We identified messages with nurturant support (emotional, esteem, and network) across three different computer-mediated communication formats (forums, journals, and notes) of an online support community for alcoholism using content analysis. Our sample consisted of 493 forum messages, 423 journal messages, and 1180 notes. Nurturant support types occurred frequently among messages offering support (forum comments: 276/412 messages, 67.0%; journal posts: 65/88 messages, 74%; journal comments: 275/335 messages, 82.1%; and notes: 1002/1180 messages, 84.92%), but less often among messages requesting support. Of all the nurturing supports, emotional (ie, encouragement) appeared most frequently, with network and esteem support appearing in patterns of varying combinations. Members of the Alcoholism Community appeared to adapt some traditional face-to-face forms of support to their needs in becoming sober, such as provision of encouragement, understanding, and empathy to one another. The computer-mediated communication format may have the greatest influence on the supportive interactions

Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

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Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

2016-01-01

With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

More consistently altered connectivity patterns for cerebellum and medial temporal lobes than for amygdala and striatum in schizophrenia

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Henning ePeters

2016-02-01

Full Text Available Background: Brain architecture can be divided into a cortico-thalamic system and modulatory ‘subcortical-cerebellar’ systems containing key structures such as striatum, medial temporal lobes (MTLs, amygdala, and cerebellum. Subcortical-cerebellar systems are known to be altered in schizophrenia. In particular, intrinsic functional brain connectivity (iFC between these systems has been consistently demonstrated in patients. While altered connectivity is known for each subcortical-cerebellar system separately, it is unknown whether subcortical-cerebellar systems’ connectivity patterns with the cortico-thalamic system are comparably altered across systems, i.e., if separate subcortical-cerebellar systems’ connectivity patterns are consistent across patients. Methods: To investigate this question, 18 patients with schizophrenia (3 unmedicated, 15 medicated with atypical antipsychotics and 18 healthy controls were assessed by resting-state functional magnetic resonance imaging (fMRI. Independent component analysis of fMRI data revealed cortical intrinsic brain networks (NWs with time courses representing proxies for cortico-thalamic system activity. Subcortical-cerebellar systems’ activity was represented by fMRI-based time courses of selected regions-of-interest (ROIs (i.e., striatum, MTL, amygdala, cerebellum. Correlation analysis among ROI- and NWs-time courses yielded individual connectivity matrices (i.e. connectivity between NW and ROIs (allROIs-NW, separateROI-NW, only NWs (NWs-NWs, and only ROIs (allROIs-allROIs as main outcome measures, which were classified by support-vector-machine-based leave-one-out cross-validation. Differences in classification accuracy were statistically evaluated for consistency across subjects and systems. Results: Correlation matrices based on allROIs-NWs yielded 91% classification accuracy, which was significantly superior to allROIs-allROIs and NWs-NWs (56% and 74%, respectively. Considering separate

A multiscale network analysis of protected-area connectivity for mammals in the United States.

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Minor, Emily S; Lookingbill, Todd R

2010-12-01

Protected areas must be close, or connected, enough to allow for the preservation of large-scale ecological and evolutionary processes, such as gene flow, migration, and range shifts in response to climate change. Nevertheless, it is unknown whether the network of protected areas in the United States is connected in a way that will preserve biodiversity over large temporal and spatial scales. It is also unclear whether protected-area networks that function for larger species will function for smaller species. We assessed the connectivity of protected areas in the three largest biomes in the United States. With methods from graph theory--a branch of mathematics that deals with connectivity and flow--we identified and measured networks of protected areas for three different groups of mammals. We also examined the value of using umbrella species (typically large-bodied, far-ranging mammals) in designing large-scale networks of protected areas. Although the total amount of protected land varied greatly among biomes in the United States, overall connectivity did not. In general, protected-area networks were well connected for large mammals but not for smaller mammals. Additionally, it was not possible to predict connectivity for small mammals on the basis of connectivity for large mammals, which suggests the umbrella species approach may not be an appropriate design strategy for conservation networks intended to protect many species. Our findings indicate different strategies should be used to increase the likelihood of persistence for different groups of species. Strategic linkages of existing lands should be a conservation priority for smaller mammals, whereas conservation of larger mammals would benefit most from the protection of more land. © 2010 Society for Conservation Biology.

Subregions of the human superior frontal gyrus and their connections.

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Li, Wei; Qin, Wen; Liu, Huaigui; Fan, Lingzhong; Wang, Jiaojian; Jiang, Tianzi; Yu, Chunshui

2013-09-01

The superior frontal gyrus (SFG) is located at the superior part of the prefrontal cortex and is involved in a variety of functions, suggesting the existence of functional subregions. However, parcellation schemes of the human SFG and the connection patterns of each subregion remain unclear. We firstly parcellated the human SFG into the anteromedial (SFGam), dorsolateral (SFGdl), and posterior (SFGp) subregions based on diffusion tensor tractography. The SFGam was anatomically connected with the anterior and mid-cingulate cortices, which are critical nodes of the cognitive control network and the default mode network (DMN). The SFGdl was connected with the middle and inferior frontal gyri, which are involved in the cognitive execution network. The SFGp was connected with the precentral gyrus, caudate, thalamus, and frontal operculum, which are nodes of the motor control network. Resting-state functional connectivity analysis further revealed that the SFGam was mainly correlated with the cognitive control network and the DMN; the SFGdl was correlated with the cognitive execution network and the DMN; and the SFGp was correlated with the sensorimotor-related brain regions. The SFGam and SFGdl were further parcellated into three and two subclusters that are well corresponding to Brodmann areas. These findings suggest that the human SFG consists of multiple dissociable subregions that have distinct connection patterns and that these subregions are involved in different functional networks and serve different functions. These results may improve our understanding on the functional complexity of the SFG and provide us an approach to investigate the SFG at the subregional level. Copyright © 2013 Elsevier Inc. All rights reserved.

Resting-State Network Topology Differentiates Task Signals across the Adult Life Span.

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Chan, Micaela Y; Alhazmi, Fahd H; Park, Denise C; Savalia, Neil K; Wig, Gagan S

2017-03-08

Brain network connectivity differs across individuals. For example, older adults exhibit less segregated resting-state subnetworks relative to younger adults (Chan et al., 2014). It has been hypothesized that individual differences in network connectivity impact the recruitment of brain areas during task execution. While recent studies have described the spatial overlap between resting-state functional correlation (RSFC) subnetworks and task-evoked activity, it is unclear whether individual variations in the connectivity pattern of a brain area (topology) relates to its activity during task execution. We report data from 238 cognitively normal participants (humans), sampled across the adult life span (20-89 years), to reveal that RSFC-based network organization systematically relates to the recruitment of brain areas across two functionally distinct tasks (visual and semantic). The functional activity of brain areas (network nodes) were characterized according to their patterns of RSFC: nodes with relatively greater connections to nodes in their own functional system ("non-connector" nodes) exhibited greater activity than nodes with relatively greater connections to nodes in other systems ("connector" nodes). This "activation selectivity" was specific to those brain systems that were central to each of the tasks. Increasing age was accompanied by less differentiated network topology and a corresponding reduction in activation selectivity (or differentiation) across relevant network nodes. The results provide evidence that connectional topology of brain areas quantified at rest relates to the functional activity of those areas during task. Based on these findings, we propose a novel network-based theory for previous reports of the "dedifferentiation" in brain activity observed in aging. SIGNIFICANCE STATEMENT Similar to other real-world networks, the organization of brain networks impacts their function. As brain network connectivity patterns differ across

Power Quality Improvement Using an Enhanced Network-Side-Shunt-Connected Dynamic Voltage Restorer

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Fereidouni, Alireza; Masoum, Mohammad A. S.; Moghbel, Moayed

2015-10-01

Among the four basic dynamic voltage restorer (DVR) topologies, the network-side shunt-connected DVR (NSSC-DVR) has a relatively poor performance and is investigated in this paper. A new configuration is proposed and implemented for NSSC-DVR to enhance its performance in compensating (un)symmetrical deep and long voltage sags and mitigate voltage harmonics. The enhanced NSSC-DVR model includes a three-phase half-bridge semi-controlled network-side-shunt-connected rectifier and a three-phase full-bridge series-connected inverter implemented with a back-to-back configuration through a bidirectional buck-boost converter. The network-side-shunt-connected rectifier is employed to inject/draw the required energy by NSSC-DVR to restore the load voltage to its pre-fault value under sag/swell conditions. The buck-boost converter is responsible for maintaining the DC-link voltage of the series-connected inverter at its designated value in order to improve the NSSC-DVR capability in compensating deep and long voltage sags/swells. The full-bridge series-connected inverter permits to compensate unbalance voltage sags containing zero-sequence component. The harmonic compensation of the load voltage is achieved by extracting harmonics from the distorted network voltage using an artificial neural network (ANN) method called adaptive linear neuron (Adaline) strategy. Detailed simulations are performed by SIMULINK/MATLAB software for six case studies to verify the highly robustness of the proposed NSSC-DVR model under various conditions.

The patterns of organisation and structure of interactions in a fish-parasite network of a neotropical river.

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Bellay, Sybelle; Oliveira, Edson F de; Almeida-Neto, Mário; Abdallah, Vanessa D; Azevedo, Rodney K de; Takemoto, Ricardo M; Luque, José L

2015-07-01

The use of the complex network approach to study host-parasite interactions has helped to improve the understanding of the structure and dynamics of ecological communities. In this study, this network approach is applied to evaluate the patterns of organisation and structure of interactions in a fish-parasite network of a neotropical Atlantic Forest river. The network includes 20 fish species and 73 metazoan parasite species collected from the Guandu River, Rio de Janeiro State, Brazil. According to the usual measures in studies of networks, the organisation of the network was evaluated using measures of host susceptibility, parasite dependence, interaction asymmetry, species strength and complementary specialisation of each species as well as the network. The network structure was evaluated using connectance, nestedness and modularity measures. Host susceptibility typically presented low values, whereas parasite dependence was high. The asymmetry and species strength were correlated with host taxonomy but not with parasite taxonomy. Differences among parasite taxonomic groups in the complementary specialisation of each species on hosts were also observed. However, the complementary specialisation and species strength values were not correlated. The network had a high complementary specialisation, low connectance and nestedness, and high modularity, thus indicating variability in the roles of species in the network organisation and the expected presence of many specialist species. Copyright © 2015 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury

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Stavros I. Dimitriadis

2015-01-01

Full Text Available Mild traumatic brain injury (mTBI may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI.

Developmental Reorganization of the Core and Extended Face Networks Revealed by Global Functional Connectivity.

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Wang, Xu; Zhu, Qi; Song, Yiying; Liu, Jia

2017-08-28

Prior studies on development of functional specialization in human brain mainly focus on age-related increases in regional activation and connectivity among regions. However, a few recent studies on the face network demonstrate age-related decrease in face-specialized activation in the extended face network (EFN), in addition to increase in activation in the core face network (CFN). Here we used a voxel-based global brain connectivity approach to investigate whether development of the face network exhibited both increase and decrease in network connectivity. We found the voxel-wise resting-state functional connectivity (FC) within the CFN increased with age in bilateral posterior superior temporal sulcus, suggesting the integration of the CFN during development. Interestingly, the FC of the voxels in the EFN to the right fusiform face area and occipital face area decreased with age, suggesting that the CFN segregated from the EFN during development. Moreover, the age-related connectivity in the CFN was related to behavioral performance in face processing. Overall, our study demonstrated developmental reorganization of the face network achieved by both integration within the CFN and segregation of the CFN from the EFN, which may account for the simultaneous increases and decreases in neural activation during the development of the face network. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The Influence of Water Conservancy Projects on River Network Connectivity, A Case of Luanhe River Basin

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Li, Z.; Li, C.

2017-12-01

Connectivity is one of the most important characteristics of a river, which is derived from the natural water cycle and determine the renewability of river water. The water conservancy project can change the connectivity of natural river networks, and directly threaten the health and stability of the river ecosystem. Based on the method of Dendritic Connectivity Index (DCI), the impacts from sluices and dams on the connectivity of river network are deeply discussed herein. DCI quantitatively evaluate the connectivity of river networks based on the number of water conservancy facilities, the connectivity of fish and geographical location. The results show that the number of water conservancy facilities and their location in the river basin have a great influence on the connectivity of the river network. With the increase of the number of sluices and dams, DCI is decreasing gradually, but its decreasing range is becoming smaller and smaller. The dam located in the middle of the river network cuts the upper and lower parts of the whole river network, and destroys the connectivity of the river network more seriously. Therefore, this method can be widely applied to the comparison of different alternatives during planning of river basins and then provide a reference for the site selection and design of the water conservancy project and facility concerned.

Knowledge Access in Rural Inter-connected Areas Network ...

International Development Research Centre (IDRC) Digital Library (Canada)

Knowledge Access in Rural Inter-connected Areas Network (KariaNet) - Phase II ... and indigenous knowledge using information and communication technologies (ICTs) ... for research proposals on the aforementioned topics, action-research projects, ... Evaluating knowledge-sharing methods to improve land utilization and ...

Discerning connectivity from dynamics in climate networks

Czech Academy of Sciences Publication Activity Database

Paluš, Milan; Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin

2011-01-01

Roč. 18, č. 5 (2011), s. 751-763 ISSN 1023-5809 R&D Projects: GA ČR GCP103/11/J068 Institutional research plan: CEZ:AV0Z10300504 Keywords : complex networks * climate dynamics * connectivity * North Atlantic Oscillation * solar activity Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.597, year: 2011

Recruitment of local inhibitory networks by horizontal connections in layer 2/3 of ferret visual cortex.

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Tucker, Thomas R; Katz, Lawrence C

2003-01-01

To investigate how neurons in cortical layer 2/3 integrate horizontal inputs arising from widely distributed sites, we combined intracellular recording and voltage-sensitive dye imaging to visualize the spatiotemporal dynamics of neuronal activity evoked by electrical stimulation of multiple sites in visual cortex. Individual stimuli evoked characteristic patterns of optical activity, while delivering stimuli at multiple sites generated interacting patterns in the regions of overlap. We observed that neurons in overlapping regions received convergent horizontal activation that generated nonlinear responses due to the emergence of large inhibitory potentials. The results indicate that co-activation of multiple sets of horizontal connections recruit strong inhibition from local inhibitory networks, causing marked deviations from simple linear integration.

Independent functional connectivity networks underpin food and monetary reward sensitivity in excess weight.

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Verdejo-Román, Juan; Fornito, Alex; Soriano-Mas, Carles; Vilar-López, Raquel; Verdejo-García, Antonio

2017-02-01

Overvaluation of palatable food is a primary driver of obesity, and is associated with brain regions of the reward system. However, it remains unclear if this network is specialized in food reward, or generally involved in reward processing. We used functional magnetic resonance imaging (fMRI) to characterize functional connectivity during processing of food and monetary rewards. Thirty-nine adults with excess weight and 37 adults with normal weight performed the Willingness to Pay for Food task and the Monetary Incentive Delay task in the fMRI scanner. A data-driven graph approach was applied to compare whole-brain, task-related functional connectivity between groups. Excess weight was associated with decreased functional connectivity during the processing of food rewards in a network involving primarily frontal and striatal areas, and increased functional connectivity during the processing of monetary rewards in a network involving principally frontal and parietal areas. These two networks were topologically and anatomically distinct, and were independently associated with BMI. The processing of food and monetary rewards involve segregated neural networks, and both are altered in individuals with excess weight. Copyright © 2016 Elsevier Inc. All rights reserved.

Preclinical cerebral network connectivity evidence of deficits in mild white matter lesions

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Ying eLiang

2016-02-01

Full Text Available White matter lesions (WMLs are notable for their high prevalence and have been demonstrated to be a potential neuroimaging biomarker of early diagnosis of Alzheimer’s disease. This study aimed to identify the brain functional and structural mechanisms underlying cognitive decline observed in mild WMLs. Multi-domain cognitive tests, as well as resting-state, diffusion tensor and structural images were obtained on 42 mild WMLs and 42 age/sex-matched healthy controls. For each participant, we examined the functional connectivity of three resting-state networks related to the changed cognitive domains: the default mode network (DMN and the bilateral fronto-parietal network (FPN. We also performed voxel-based morphometry analysis to compare whole-brain gray matter volume, atlas-based quantification of the white matter tracts interconnecting the RSNs, and the relationship between functional connectivity and structural connectivity. We observed functional connectivity alterations in the DMN and the right FPN combined with related white matter integrity disruption in mild WMLs. However, no significant gray matter atrophy difference was found. Furthermore, the right precuneus functional connectivity in the DMN exhibited a significantly negative correlation with the memory test scores. Our study suggests that in mild WMLs, dysfunction of RSNs might be a consequence of decreased white matter structural connectivity, which further affects cognitive performance.

Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

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Xu Lei

Full Text Available EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs are extracted using spatial independent component analysis (ICA in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA. Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI. Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.

Beyond ectomycorrhizal bipartite networks: projected networks demonstrate contrasted patterns between early- and late-successional plants in Corsica.

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Adrien eTaudiere

2015-10-01

Full Text Available The ectomycorrhizal (ECM symbiosis connects mutualistic plants and fungal species into bipartite networks. While links between one focal ECM plant and its fungal symbionts have been widely documented, systemic views of ECM networks are lacking, in particular, concerning the ability of fungal species to mediate indirect ecological interactions between ECM plant species (projected-ECM networks. We assembled a large dataset of plant-fungi associations at the species level and at the scale of Corsica using molecular data and unambiguously host-assigned records to: (i examine the correlation between the number of fungal symbionts of a plant species and the average specialization of these fungal species, (ii explore the structure of the plant-plant projected network and (iii compare plant association patterns in regard to their position along the ecological succession. Our analysis reveals no trade-off between specialization of plants and specialization of their partners and a saturation of the plant projected network. Moreover, there is a significantly lower-than-expected sharing of partners between early- and late-successional plant species, with fewer fungal partners for early-successional ones and similar average specialization of symbionts of early- and late-successional plants. Our work paves the way for ecological readings of Mediterranean landscapes that include the astonishing diversity of below-ground interactions.

Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

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Wang, Fang-Fang; Su, Chao-Ton

2014-11-01

The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

Heteroassociative storage of hippocampal pattern sequences in the CA3 subregion

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Raphael Y. de Camargo

2018-01-01

Full Text Available Background Recent research suggests that the CA3 subregion of the hippocampus has properties of both autoassociative network, due to its ability to complete partial cues, tolerate noise, and store associations between memories, and heteroassociative one, due to its ability to store and retrieve sequences of patterns. Although there are several computational models of the CA3 as an autoassociative network, more detailed evaluations of its heteroassociative properties are missing. Methods We developed a model of the CA3 subregion containing 10,000 integrate-and-fire neurons with both recurrent excitatory and inhibitory connections, and which exhibits coupled oscillations in the gamma and theta ranges. We stored thousands of pattern sequences using a heteroassociative learning rule with competitive synaptic scaling. Results We showed that a purely heteroassociative network model can (i retrieve pattern sequences from partial cues with external noise and incomplete connectivity, (ii achieve homeostasis regarding the number of connections per neuron when many patterns are stored when using synaptic scaling, (iii continuously update the set of retrievable patterns, guaranteeing that the last stored patterns can be retrieved and older ones can be forgotten. Discussion Heteroassociative networks with synaptic scaling rules seem sufficient to achieve many desirable features regarding connectivity homeostasis, pattern sequence retrieval, noise tolerance and updating of the set of retrievable patterns.

Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.

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Felipe Gerhard

Full Text Available Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.

Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory.

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Galeano Weber, Elena M; Hahn, Tim; Hilger, Kirsten; Fiebach, Christian J

2017-02-01

Limitations in visual working memory (WM) quality (i.e., WM precision) may depend on perceptual and attentional limitations during stimulus encoding, thereby affecting WM capacity. WM encoding relies on the interaction between sensory processing systems and fronto-parietal 'control' regions, and differences in the quality of this interaction are a plausible source of individual differences in WM capacity. Accordingly, we hypothesized that the coupling between perceptual and attentional systems affects the quality of WM encoding. We combined fMRI connectivity analysis with behavioral modeling by fitting a variable precision and fixed capacity model to the performance data obtained while participants performed a visual delayed continuous response WM task. We quantified functional connectivity during WM encoding between occipital and parietal brain regions activated during both perception and WM encoding, as determined using a conjunction of two independent experiments. The multivariate pattern of voxel-wise inter-areal functional connectivity significantly predicted WM performance, most specifically the mean of WM precision but not the individual number of items that could be stored in memory. In particular, higher occipito-parietal connectivity was associated with higher behavioral mean precision. These results are consistent with a network perspective of WM capacity, suggesting that the efficiency of information flow between perceptual and attentional neural systems is a critical determinant of limitations in WM quality. Copyright © 2016 Elsevier Inc. All rights reserved.

Magnetoencephalography Reveals a Widespread Increase in Network Connectivity in Idiopathic/Genetic Generalized Epilepsy.

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Adham Elshahabi

Full Text Available Idiopathic/genetic generalized epilepsy (IGE/GGE is characterized by seizures, which start and rapidly engage widely distributed networks, and result in symptoms such as absences, generalized myoclonic and primary generalized tonic-clonic seizures. Although routine magnetic resonance imaging is apparently normal, many studies have reported structural alterations in IGE/GGE patients using diffusion tensor imaging and voxel-based morphometry. Changes have also been reported in functional networks during generalized spike wave discharges. However, network function in the resting-state without epileptiforme discharges has been less well studied. We hypothesize that resting-state networks are more representative of the underlying pathophysiology and abnormal network synchrony. We studied functional network connectivity derived from whole-brain magnetoencephalography recordings in thirteen IGE/GGE and nineteen healthy controls. Using graph theoretical network analysis, we found a widespread increase in connectivity in patients compared to controls. These changes were most pronounced in the motor network, the mesio-frontal and temporal cortex. We did not, however, find any significant difference between the normalized clustering coefficients, indicating preserved gross network architecture. Our findings suggest that increased resting state connectivity could be an important factor for seizure spread and/or generation in IGE/GGE, and could serve as a biomarker for the disease.

Group-ICA model order highlights patterns of functional brain connectivity

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Ahmed eAbou Elseoud

2011-06-01

Full Text Available Resting-state networks (RSNs can be reliably and reproducibly detected using independent component analysis (ICA at both individual subject and group levels. Altering ICA dimensionality (model order estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual-regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.

Altered causal connectivity of resting state brain networks in amnesic MCI.

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Peipeng Liang

Full Text Available Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI have concentrated on functional connectivity (FC based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data--default mode network (DMN, hippocampal cortical memory network (HCMN, dorsal attention network (DAN and fronto-parietal control network (FPCN. Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE, and California verbal learning test (CVLT scores. When the whole-brain signal averaged over the entire brain was used as a nuisance co-variate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.

Connecting Social Networks with Ecosystem Services for Watershed Governance: a Social-Ecological Network Perspective Highlights the Critical Role of Bridging Organizations

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Kaitlyn J. Rathwell

2012-06-01

Full Text Available In many densely settled agricultural watersheds, water quality is a point of conflict between amenity and agricultural activities because of the varied demands and impacts on shared water resources. Successful governance of these watersheds requires coordination among different activities. Recent research has highlighted the role that social networks between management entities can play to facilitate cross-scale interaction in watershed governance. For example, bridging organizations can be positioned in social networks to bridge local initiatives done by single municipalities across whole watersheds. To better understand the role of social networks in social-ecological system dynamics, we combine a social network analysis of the water quality management networks held by local governments with a social-ecological analysis of variation in water management and ecosystem services across the Montérégie, an agricultural landscape near Montréal, Québec, Canada. We analyze municipal water management networks by using one-mode networks to represent direct collaboration between municipalities, and two-mode networks to capture how bridging organizations indirectly connect municipalities. We find that municipalities do not collaborate directly with one another but instead are connected via bridging organizations that span the water quality management network. We also discovered that more connected municipalities engaged in more water management activities. However, bridging organizations preferentially connected with municipalities that used more tourism related ecosystem services rather than those that used more agricultural ecosystem services. Many agricultural municipalities were relatively isolated, despite being the main producers of water quality problems. In combination, these findings suggest that further strengthening the water management network in the Montérégie will contribute to improving water quality in the region. However, such

Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data.

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Sharaev, Maksim G; Zavyalova, Viktoria V; Ushakov, Vadim L; Kartashov, Sergey I; Velichkovsky, Boris M

2016-01-01

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state.

Patterns of precipitation and soil moisture extremes in Texas, US: A complex network analysis

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Sun, Alexander Y.; Xia, Youlong; Caldwell, Todd G.; Hao, Zengchao

2018-02-01

Understanding of the spatial and temporal dynamics of extreme precipitation not only improves prediction skills, but also helps to prioritize hazard mitigation efforts. This study seeks to enhance the understanding of spatiotemporal covariation patterns embedded in precipitation (P) and soil moisture (SM) by using an event-based, complex-network-theoretic approach. Events concurrences are quantified using a nonparametric event synchronization measure, and spatial patterns of hydroclimate variables are analyzed by using several network measures and a community detection algorithm. SM-P coupling is examined using a directional event coincidence analysis measure that takes the order of event occurrences into account. The complex network approach is demonstrated for Texas, US, a region possessing a rich set of hydroclimate features and is frequented by catastrophic flooding. Gridded daily observed P data and simulated SM data are used to create complex networks of P and SM extremes. The uncovered high degree centrality regions and community structures are qualitatively in agreement with the overall existing knowledge of hydroclimate extremes in the study region. Our analyses provide new visual insights on the propagation, connectivity, and synchronicity of P extremes, as well as the SM-P coupling, in this flood-prone region, and can be readily used as a basis for event-driven predictive analytics for other regions.

Resting-State Brain and the FTO Obesity Risk Allele: Default Mode, Sensorimotor, and Salience Network Connectivity Underlying Different Somatosensory Integration and Reward Processing between Genotypes.

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Olivo, Gaia; Wiemerslage, Lyle; Nilsson, Emil K; Solstrand Dahlberg, Linda; Larsen, Anna L; Olaya Búcaro, Marcela; Gustafsson, Veronica P; Titova, Olga E; Bandstein, Marcus; Larsson, Elna-Marie; Benedict, Christian; Brooks, Samantha J; Schiöth, Helgi B

2016-01-01

Single-nucleotide polymorphisms (SNPs) of the fat mass and obesity associated (FTO) gene are linked to obesity, but how these SNPs influence resting-state neural activation is unknown. Few brain-imaging studies have investigated the influence of obesity-related SNPs on neural activity, and no study has investigated resting-state connectivity patterns. We tested connectivity within three, main resting-state networks: default mode (DMN), sensorimotor (SMN), and salience network (SN) in 30 male participants, grouped based on genotype for the rs9939609 FTO SNP, as well as punishment and reward sensitivity measured by the Behavioral Inhibition (BIS) and Behavioral Activation System (BAS) questionnaires. Because obesity is associated with anomalies in both systems, we calculated a BIS/BAS ratio (BBr) accounting for features of both scores. A prominence of BIS over BAS (higher BBr) resulted in increased connectivity in frontal and paralimbic regions. These alterations were more evident in the obesity-associated AA genotype, where a high BBr was also associated with increased SN connectivity in dopaminergic circuitries, and in a subnetwork involved in somatosensory integration regarding food. Participants with AA genotype and high BBr, compared to corresponding participants in the TT genotype, also showed greater DMN connectivity in regions involved in the processing of food cues, and in the SMN for regions involved in visceral perception and reward-based learning. These findings suggest that neural connectivity patterns influence the sensitivity toward punishment and reward more closely in the AA carriers, predisposing them to developing obesity. Our work explains a complex interaction between genetics, neural patterns, and behavioral measures in determining the risk for obesity and may help develop individually-tailored strategies for obesity prevention.

A triple network connectivity study of large-scale brain systems in cognitively normal APOE4 carriers

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Xia Wu

2016-09-01

Full Text Available The triple network model, consisting of the central executive network, salience network and default mode network, has been recently employed to understand dysfunction in core networks across various disorders. Here we used the triple network model to investigate the large-scale brain networks in cognitively normal APOE4 carriers who are at risk of Alzheimer’s disease (AD. To explore the functional connectivity for each of the three networks and the effective connectivity among them, we evaluated 17 cognitively normal individuals with a family history of AD and at least one copy of the apolipoprotein e4 (APOE4 allele and compared the findings to those of 12 individuals who did not carry the APOE4 gene or have a family history of AD, using independent component analysis and Bayesian network approach. Our findings indicated altered within-network connectivity that suggests future cognitive decline risk, and preserved between-network connectivity that may support their current preserved cognition in the cognitively normal APOE4 allele carries. The study provides novel sights into our understanding of the risk factors for AD and their influence on the triple network model of major psychopathology.

Effective connectivity within the default mode network: dynamic causal modeling of resting-state fMRI data

Directory of Open Access Journals (Sweden)

Maksim eSharaev

2016-02-01

Full Text Available The Default Mode Network (DMN is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of BOLD (Blood-oxygen-level dependent activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e. effective connectivity, however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex mPFC, the posterior cingulate cortex PCC, left and right intraparietal cortex LIPC and RIPC. For this purpose fMRI (functional magnetic resonance imaging data from 30 healthy subjects (1000 time points from each one was acquired and spectral dynamic causal modeling (DCM on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078–0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p<0.05. Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain’s functioning at resting state.

A fully connected network of Bernoulli units with correlation learning

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Dente, J. A.; Vilela Mendes, R.

1996-02-01

Biological evidence suggests that pattern recognition and associative memory in the mammalian nervous system operates through the establishment of spatio-temporal patterns of activity and not by the evolution towards an equilibrium point as in attractor neural networks. Information is carried by the space-time correlation of the activity intensities rather than by the details of individual neuron signals. Furthermore the fast recognition times that are achieved with relatively slow biological neurons seem to be associated to the chaotic nature of the basal nervous activity. To copy the biology hardware may not be technologically sound, but to look for inspiration in the efficient biological information processing methods is an idea that deserves consideration. Inspired by the mechanisms at work in the mammalian olfactory system we study a network where, in the absence of external inputs, the units have a dynamics of the Bernoulli shift type. When an external signal is presented, the pattern of excitation bursts depends on the learning history of the network. Association and pattern identification in the network operates by the selection, by the external stimulus, of distinct invariant measures in the chaotic system. The simplicity of the node dynamics, that is chosen, allows a reasonable analytical control of the network behavior.

Thalamocortical functional connectivity in Lennox-Gastaut syndrome is abnormally enhanced in executive-control and default-mode networks.

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Warren, Aaron E L; Abbott, David F; Jackson, Graeme D; Archer, John S

2017-12-01

To identify abnormal thalamocortical circuits in the severe epilepsy of Lennox-Gastaut syndrome (LGS) that may explain the shared electroclinical phenotype and provide potential treatment targets. Twenty patients with a diagnosis of LGS (mean age = 28.5 years) and 26 healthy controls (mean age = 27.6 years) were compared using task-free functional magnetic resonance imaging (MRI). The thalamus was parcellated according to functional connectivity with 10 cortical networks derived using group-level independent component analysis. For each cortical network, we assessed between-group differences in thalamic functional connectivity strength using nonparametric permutation-based tests. Anatomical locations were identified by quantifying spatial overlap with a histologically informed thalamic MRI atlas. In both groups, posterior thalamic regions showed functional connectivity with visual, auditory, and sensorimotor networks, whereas anterior, medial, and dorsal thalamic regions were connected with networks of distributed association cortex (including the default-mode, anterior-salience, and executive-control networks). Four cortical networks (left and right executive-control network; ventral and dorsal default-mode network) showed significantly enhanced thalamic functional connectivity strength in patients relative to controls. Abnormal connectivity was maximal in mediodorsal and ventrolateral thalamic nuclei. Specific thalamocortical circuits are affected in LGS. Functional connectivity is abnormally enhanced between the mediodorsal and ventrolateral thalamus and the default-mode and executive-control networks, thalamocortical circuits that normally support diverse cognitive processes. In contrast, thalamic regions connecting with primary and sensory cortical networks appear to be less affected. Our previous neuroimaging studies show that epileptic activity in LGS is expressed via the default-mode and executive-control networks. Results of the present study suggest that

Exponential stability of neural networks with asymmetric connection weights

International Nuclear Information System (INIS)

Yang Jinxiang; Zhong Shouming

2007-01-01

This paper investigates the exponential stability of a class of neural networks with asymmetric connection weights. By dividing the network state variables into various parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Lyapunov function and using the method of the variation of constant. The new conditions are associated with the initial values and are described by some blocks of the interconnection matrix, and do not depend on other blocks. Examples are given to further illustrate the theory

Motif statistics and spike correlations in neuronal networks