Dynamic Network Connectivity Research Articles

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

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-task feature learning and multi-kernel learning techniques. Results on 149 subjects with baseline rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) suggest that our method can not only improve the classification performance in comparison with state-of-the-art methods, but also provide insights into the spatio-temporal interaction patterns of brain activity and their changes in brain disorders.

Resting state dynamic functional connectivity: Network topology analysis

Abstract Biologically inspired cognitive architectures are based on the data obtained by studying the mechanisms of the brain’s functional networks operations, the causality of their integration and differentiation into the neurophysiological structures of the cognitive processes of consciousness. One of the critical network involved in maintaining the core level of consciousness at the resting state is the default mode network (DMN). The DMN reduces its activity with an external stimulation and behavioral task and increases with perceived mental states as imagination, internal dialogue, and others. A complete loss of consciousness is characterized by the synchronization of DMN with the anticorrelated with DMN network. When the level of consciousness changes in the processes of cognitive activity, there is a complex picture of the combination of positive and negative connections between different networks and regions of the brain. At the same time, the changes in the intrinsic brain organization during the cognitive process and the resting state still an open question. The primary purpose of this work is studying the dynamics of the different brain networks interactions at the resting state, such as executive and attention networks, cerebellum, DMN, visual and auditory network, brainstem, the somatosensory and motor networks and subcortical network. We used three algorithms for clustering states in neural network connectivity dynamics: direct clustering of the functional network with the k-means algorithm, modularity-based and topological based clustering. We obtained that in the dynamics of functional connectomes there are three expressed states, determined by different types of interactions between DMN networks, attention and other neural networks.

Image-based model of the spectrin cytoskeleton for red blood cell simulation.

We simulate deformable red blood cells in the microcirculation using the immersed boundary method with a cytoskeletal model that incorporates structural details revealed by tomographic images. The elasticity of red blood cells is known to be supplied by both their lipid bilayer membranes, which resist bending and local changes in area, and their cytoskeletons, which resist in-plane shear. The cytoskeleton consists of spectrin tetramers that are tethered to the lipid bilayer by ankyrin and by actin-based junctional complexes. We model the cytoskeleton as a random geometric graph, with nodes corresponding to junctional complexes and with edges corresponding to spectrin tetramers such that the edge lengths are given by the end-to-end distances between nodes. The statistical properties of this graph are based on distributions gathered from three-dimensional tomographic images of the cytoskeleton by a segmentation algorithm. We show that the elastic response of our model cytoskeleton, in which the spectrin polymers are treated as entropic springs, is in good agreement with the experimentally measured shear modulus. By simulating red blood cells in flow with the immersed boundary method, we compare this discrete cytoskeletal model to an existing continuum model and predict the extent to which dynamic spectrin network connectivity can protect against failure in the case of a red cell subjected to an applied strain. The methods presented here could form the basis of disease- and patient-specific computational studies of hereditary diseases affecting the red cell cytoskeleton.

High-Throughput Network Coding Aware Routing in Time-Varying Multihop Networks

Network coding aware routing (NCAR) has been emerging as an effective approach to create coding opportunities actively in multihop networks. To the best of our knowledge, most of the existing methods focus on a “static” design that considers coding opportunities based on fixed routes and invariable packet flows. This “static” way results in the optimality of routes suffering from a high sensitive behavior on both network topology and flow dynamic and, hence, reduces the throughput of routes in time-varying networks. To solve this problem, we propose a high-throughput NCAR scheme that supports dynamic multihop network connectivity and achieves throughput optimization, even with multiple variable flows. In the proposed scheme, a back-pressure-based NCAR algorithm is designed to push the flows dynamically into the nodes that have better network coding opportunities with no knowledge of network topology and flow dynamic. As a byproduct, the proposed method also exhibits a significantly improved delay performance relative to the traditional back-pressure algorithm. Simulation results reveal that the proposed scheme improves network capacity and alleviates end-to-end delay in heavy load networks.

The Impact of Treatments for Depression on the Dynamic Network Structure of Mental States: Two Randomized Controlled Trials

Evidence is growing that vulnerability to depression may be characterized by strong negative feedback loops between mental states. It is unknown whether such dynamics between mental states can be altered by treatment. This study examined whether treatment with imipramine or treatment with Mindfulness-Based Cognitive Therapy (MBCT) reduces the connectivity within dynamic networks of mental states in individuals with depressive symptoms. In the Imipramine trial, individuals diagnosed with major depression were randomized to imipramine treatment or placebo-pill treatment (n = 50). In the Mind-Maastricht trial, individuals with residual depressive symptoms were randomized to Mindfulness-Based Cognitive Therapy (MBCT) or to a waiting-list control condition (n = 119). Lagged associations among mental states, as assessed with the Experience Sampling Method (ESM), were estimated at baseline and post-intervention. The results show that few of the dynamic network connections changed significantly over time and few of the changes after MBCT and imipramine treatment differed significantly from the control groups. The decrease in average node connectivity after MBCT did not differ from the decrease observed in the waiting-list control group. Our findings suggest that imipramine treatment and MBCT do not greatly change the dynamic network structure of mental states, even though they do reduce depressive symptomatology.

Multi-Channel Distributed DSA Networks: Connectivity and Rendezvous Tradeoffs

In this paper, we investigate and characterize the effects of multi-channel and rendezvous protocols on the connectivity of dynamic spectrum access networks using percolation theory. In particular, we focus on the scenario where the secondary nodes have plenty of vacant channels to choose from a phenomenon which we define as channel abundance. To cope with the existence of multi-channel, we use two types of rendezvous protocols: naive ones which do not guarantee a common channel and advanced ones which do. We show that, with more channel abundance, even with the use of either type of rendezvous protocols, it becomes difficult for two nodes to agree on a common channel, thereby, potentially remaining invisible to each other. We model this invisibility as a Poisson thinning process and show that invisibility is even more pronounced with channel abundance. Following the disk graph model, we represent the multiple channels as parallel edges in a graph and build a multi-layered graph (MLG) in $\boldsymbol {\mathcal {R}}^{\mathbf{2}}$ . In order to study the connectivity, we show how percolation occurs in the MLG by coupling it with a typical discrete percolation. Using a Boolean model and the MLG, we study both cases of primaries’ absence and presence. For both cases, we define and characterize connectivity of the secondary network in terms of the available number of channels, deployment densities, number of simultaneous transmissions per node, and communication range. When primary users are absent, we derive the critical number of channels which maintains super-criticality of the secondary network. When primary users are present, we characterize and analyze the connectivity for all the regions: channel abundance, optimal, and channel deprivation. For each region we show the requirement and the outcome of using either type of rendezvous techniques. Moreover, we find the tradeoff between deployment-density versus rendezvous probability which results in a connected network. Our results can be used to decide on the goodness of any channel rendezvous algorithm by computing the expected resultant connectivity. They also provide a guideline for achieving connectivity using minimal resources.

Critical journey evolving graphs

End-to-end connectivity in dynamic networks may not hold at a given point of time. Nevertheless, temporal paths (or journeys) may still exist over time and space due to the inherent evolution of networks. Large amount of work has been done to examine foremost journeys and connectivity of dynamic networks. However, as far as we know, there is still no work which can address foremost journeys calculation in continuous time under scenarios where contacts have arbitrary durations and edge traversal time is non-negligible. To this end, we propose a model named critical journey evolving graphs (CJEGs) to effectively characterize the temporal connectivity of dynamic networks. By CJEGs, a foremost journey between any pair of nodes starting at any time can be inferred directly. Accordingly, temporal connectivity metrics such as velocity, efficiency and density can be figured out. To construct CJEGs, a distributed algorithm called CJEG-PERST is developed, which can update CJEGs online adaptively with the evolution of networks, thus avoiding the hassle of heavy computation and memory overhead. We conduct extensive experiments and analysis to explore the temporal connectivity of dynamic networks by applying CJEGs to synthetic and realistic datasets, as well as making comparison with other models such as temporal reachability graphs (TRGs). Experimental results show CJEGs yield many fresh and enlightening insights into connectivity of dynamic networks. Especially, two important applications of CJEGs are further discussed, namely how to derive TRGs and calculate theoretical performance benchmark for opportunistic routing. These explorations preliminarily demonstrate the promising potentials of the CJEGs model.

Molecular Dynamics Simulations Reveal Isoform Specific Contact Dynamics between the Plexin Rho GTPase Binding Domain (RBD) and Small Rho GTPases Rac1 and Rnd1.

The Plexin family of transmembrane receptors are unique in that their intracellular region interacts directly with small GTPases of the Rho family. The Rho GTPase binding domain of plexin (RBD)-which is responsible for these interactions-can bind with Rac1 as well as Rnd1 GTPases. GTPase complexes have been crystallized with the RBDs of plexinA1, -A2, and -B1. The protein association is thought to elicit different functional responses in a GTPase and plexin isoform specific manner, but the origin of this is unknown. In this project, we investigated complexes between several RBD and Rac1/Rnd1 GTPases using multimicrosecond length all atom molecular dynamics simulations, also with reference to the free forms of the RBDs and GTPases. In accord with the crystallographic data, the RBDs experience more structural changes than Rho-GTPases upon complex formation. Changes in protein dynamics and networks of correlated motions are revealed by analyzing dihedral angle fluctuations in the proteins. The extent of these changes differs between the different RBDs and also between the Rac1 and Rnd1 GTPases. While the RBDs in the free and bound states have similar-if not decreased-correlations, correlations within the GTPases are increased upon binding. Mapping highly correlated residues to the structures, it is found that the plexinA1, -B1, and -A2 RBDs all have similar communication pathways within the ubiquitin fold, but that different residues are involved. Dynamic network analyses indicate that plexinA1 and -B1 RBDs interact with small GTPases in a similar manner, whereas complexes with the plexinA2 RBD display different features. Importantly complexes with Rnd1 have a considerable number of dynamic correlations and network connections between the proteins, whereas such features are missing in the RBD-Rac1 complexes. Overall, the simulations suggest mechanisms that are consistent with the experimental data on plexinB1 and indicate RBD and GTPase isoform specific changes in protein dynamics upon complex formation.

Risperidone Effects on Brain Dynamic Connectivity-A Prospective Resting-State fMRI Study in Schizophrenia.

Resting-state functional connectivity studies in schizophrenia evaluating average connectivity over the entire experiment have reported aberrant network integration, but findings are variable. Examining time-varying (dynamic) functional connectivity may help explain some inconsistencies. We assessed dynamic network connectivity using resting-state functional MRI in patients with schizophrenia, while unmedicated (n = 34), after 1 week (n = 29) and 6 weeks of treatment with risperidone (n = 24), as well as matched controls at baseline (n = 35) and after 6 weeks (n = 19). After identifying 41 independent components (ICs) comprising resting-state networks, sliding window analysis was performed on IC timecourses using an optimal window size validated with linear support vector machines. Windowed correlation matrices were then clustered into three discrete connectivity states (a relatively sparsely connected state, a relatively abundantly connected state, and an intermediately connected state). In unmedicated patients, static connectivity was increased between five pairs of ICs and decreased between two pairs of ICs when compared to controls, dynamic connectivity showed increased connectivity between the thalamus and somatomotor network in one of the three states. State statistics indicated that, in comparison to controls, unmedicated patients had shorter mean dwell times and fraction of time spent in the sparsely connected state, and longer dwell times and fraction of time spent in the intermediately connected state. Risperidone appeared to normalize mean dwell times after 6 weeks, but not fraction of time. Results suggest that static connectivity abnormalities in schizophrenia may partly be related to altered brain network temporal dynamics rather than consistent dysconnectivity within and between functional networks and demonstrate the importance of implementing complementary data analysis techniques.

Preparation of Slice Cultures from Rodent Hippocampus.

This protocol describes the preparation of hippocampal slice cultures from rat or mouse pups using sterile conditions that do not require the use of antibiotics or antimycotics. Combining very good optical and electrophysiological accessibility with a lifetime approaching that of the intact animal, many fundamental questions about synaptic plasticity and long-term dynamics of network connectivity can be addressed with this preparation.

Dynamics in the neural network of an in vitro epilepsy model

Epilepsy is growingly considered as a brain network disorder. In this study, epileptiform discharges induced by low-Mg2+ were recorded with a microelectrode array. Dynamic effective network connectivity was constructed by calculating the time-variant partial directed coherence (tvPDC) of signals. We proposed a novel approach to track the state transitions of epileptic networks, and characterised the network topology by using graphical measures. We found that the network hub nodes coincided with the epileptogenic zone in previous electrophysiological findings. Two network states with distinct topologies were identified during the ictal-like discharges. The small-worldness significantly increased at the second state. Our results indicate the ability of tvPDC to capture the causal interaction between multi-channel signals important in identifying the epileptogenetic zone. Moreover, the evolution of network states extends our knowledge of the network drivers for the initiation and maintenance of ical activity, and suggests the practical value of our network clustering approach.

Dynamic Virtual Network Connectivity Services to Support C-RAN Backhauling

Traditional distributed radio access networks (RANs) are evolving toward centralized architectures, with the aim of satisfying in a cost-effective manner the forecast traffic growth that future mobile networks will need to support. Specifically, the cloud RAN (C-RAN) architecture has shown that it can alleviate to some extent the ever-increasing total cost of ownership in mobile networks. The current trend in C-RAN is to separate remote radio heads with radio frequency (RF) functions and baseband unit (BBU) gathering baseband processing. This functional split allows the RF modules to be kept close to the antennas while placing BBUs at centralized locations so they can be shared among different sites and even be virtualized. However, some issues still need to be addressed in future mobile networks, especially due to the dynamicity of services and the strict constraints imposed by the interfaces needed. In fact, connectivity reconfiguration for X2 and S1 backhaul interfaces needs to be provided as an all-or-nothing request to enable mobile resource reconfiguration in a geographical area. In view of this, in this paper we propose dynamic customer virtual network (CVN) reconfiguration to be supported in metro and core network segments. Such CVN requests must include quality of service constraints to ensure specific delay constraints, as well as bitrate guarantees to avoid service interruption. A mathematical formulation and heuristic algorithm are presented for the CVN reconfiguration problem, and exhaustive simulation results study its performance in realistic scenarios.

Dynamic Network Connectivity Analysis to Identify Epileptogenic Zones Based on Stereo-Electroencephalography.

Objectives: Accurate localization of epileptogenic zones (EZs) is essential for successful surgical treatment of refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG) signals is effective in localizing EZs.Methods: SEEG data were recorded from seven patients who underwent presurgical evaluation for the treatment of refractory focal epilepsy and for whom the subsequent resective surgery gave a good outcome. A time-variant multivariate autoregressive model was constructed using a Kalman filter, and the time-variant partial directed coherence was computed. This was then used to construct a dynamic directed network model of the epileptic brain. Three graph measures (in-degree, out-degree, and betweenness centrality) were used to analyze the characteristics of the dynamic network and to find the important nodes in it.Results: In all seven patients, the indicative EZs localized by the in-degree and the betweenness centrality were highly consistent with the clinically diagnosed EZs. However, the out-degree did not indicate any significant differences between nodes in the network.Conclusions: In this work, a method based on ictal SEEG signals and effective connectivity analysis localized EZs accurately. The results suggest that the in-degree and betweenness centrality may be better network characteristics to localize EZs than the out-degree.

A Method for Intertemporal Functional-Domain Connectivity Analysis: Application to Schizophrenia Reveals Distorted Directional Information Flow.

We introduce a method for analyzing dynamically changing functional magnetic resonance imaging (fMRI) network connectivity estimates as they vary within and between broad functional domains. The method captures evidence of intertemporal directionality incross joint functional-domaininfluence and extends standard whole-brain dynamic network connectivity approaches into additional functionally meaningful dimensions by evaluating transition probabilities between clustered intradomain and interdomain connectivity patterns. Results: In applying this method to a large (N = 314) multisite resting-state fMRI dataset balanced between schizophrenia patients and healthy controls, we find evidence of joint functional domains that areglobal catalyzers, broadly shaping downstream functional relationships throughout the brain. Multiple interesting differences between patients and controls in both time-varying joint functional-domain connectivity patterns and in cross joint functional-domain intertemporal information flow were identified.Conclusion and Significance:Our proposed approach, thus, unifies the concepts of brain connectivity and interdomain connectivity and provides a powerful new way to evaluate functional connectivity data in the context of both the healthy and diseased brain.

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences.

Recent theoretical and empirical work has highlighted the role of domain-general, large-scale brain networks in generating emotional experiences. These networks are hypothesized to process aspects of emotional experiences that are not unique to a specific emotional category (e.g., "sadness," "happiness"), but rather that generalize across categories. In this article, we examined the dynamic interactions (i.e., changing cohesiveness) between specific domain-general networks across time while participants experienced various instances of sadness, fear, and anger. We used a novel method for probing the network connectivity dynamics between two salience networks and three amygdala-based networks. We hypothesized, and found, that the functional connectivity between these networks covaried with the intensity of different emotional experiences. Stronger connectivity between the dorsal salience network and the medial amygdala network was associated with more intense ratings of emotional experience across six different instances of the three emotion categories examined. Also, stronger connectivity between the dorsal salience network and the ventrolateral amygdala network was associated with more intense ratings of emotional experience across five out of the six different instances. Our findings demonstrate that a variety of emotional experiences are associated with dynamic interactions of domain-general neural systems.

Backpressure Delay Enhancement for Encounter-Based Mobile Networks While Sustaining Throughput Optimality

Backpressure routing, in which packets are preferentially transmitted over links with high queue differentials, offers the promise of throughput-optimal operation for a wide range of communication networks. However, when traffic load is low, backpressure methods suffer from long delays. This is of particular concern in intermittent encounter-based mobile networks which are already delay-limited due to the sparse and highly dynamic network connectivity. While state of the art mechanisms for such networks have proposed the use of redundant transmissions to improve delay, they do not work well when traffic load is high. In this paper we propose backpressure with adaptive redundancy (BWAR), a novel hybrid approach that provides the best of both worlds. This approach is robust, distributed, and does not require any prior knowledge of network load conditions. We also present variants of BWAR that remove redundant packets via a timeout mechanism, and that improve energy use. These algorithms are evaluated by mathematical analysis and by simulations of real traces of taxis in Beijing, China. The simulations confirm that BWAR outperforms traditional backpressure at low load, while outperforming encounter-routing schemes (Spray and Wait and Spray and Focus) at high load.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Predicting the Dynamics of Network Connectivity in the Neocortex

Dynamic remodeling of connectivity is a fundamental feature of neocortical circuits. Unraveling the principles underlying these dynamics is essential for the understanding of how neuronal circuits give rise to computations. Moreover, as complete descriptions of the wiring diagram in cortical tissues are becoming available, deciphering the dynamic elements in these diagrams is crucial for relating them to cortical function. Here, we used chronic in vivo two-photon imaging to longitudinally follow a few thousand dendritic spines in the mouse auditory cortex to study the determinants of these spines' lifetimes. We applied nonlinear regression to quantify the independent contribution of spine age and several morphological parameters to the prediction of the future survival of a spine. We show that spine age, size, and geometry are parameters that can provide independent contributions to the prediction of the longevity of a synaptic connection. In addition, we use this framework to emulate a serial sectioning electron microscopy experiment and demonstrate how incorporation of morphological information of dendritic spines from a single time-point allows estimation of future connectivity states. The distinction between predictable and nonpredictable connectivity changes may be used in the future to identify the specific adaptations of neuronal circuits to environmental changes. The full dataset is publicly available for further analysis. Significance statement: The neural architecture in the neocortex exhibits constant remodeling. The functional consequences of these modifications are poorly understood, in particular because the determinants of these changes are largely unknown. Here, we aimed to identify those modifications that are predictable from current network state. To that goal, we repeatedly imaged thousands of dendritic spines in the auditory cortex of mice to assess the morphology and lifetimes of synaptic connections. We developed models based on morphological features of dendritic spines that allow predicting future turnover of synaptic connections. The dynamic models presented in this paper provide a quantitative framework for adding putative temporal dynamics to the static description of a neuronal circuit from single time-point connectomics experiments.

Exploring influence of subliminal interoception on whole-brain functional network connectivity dynamics.

Recent fMRI studies have highlighted a dynamic relation across large-scale intrinsic connectivity networks (ICNs) of human brain. The origin of such temporal variations in functional connectivity especially during the task-free (resting-state) fMRI is still a matter of debate and ongoing investigation. In this exploratory study, we sought to determine whether subliminal differences in interoception (e.g., distention pressure on the viscera) can influence the dynamics of whole-brain functional network connectivity. A group of healthy right-handed female subjects, close in age (n = 15, mean age ± SD = 30.33 ± 8.7 years) underwent a series of eyes-open resting-state fMRI scans under different interoceptive conditions including catheterization and partial bladder filling. Using a high-dimensional independent component analysis, the functional imaging data were parcellated into 75 components, out of which 33 were identified as non-artifactual ICNs. Changes in dynamic functional network connectivity (dFNC) were evaluated using the sliding-time window approach and k-means clustering algorithm. We used subject medians for each cluster state and compared differences in dFNC correlations using a paired t-test. Following a false discovery rate multiple comparison correction threshold of p<;0.05, no significant differences in dFNC were found. However, different dwell times for each (pseudo-)resting-state were observed. More liberal statistical criteria (uncorrected p<;0.005) also indicated differences in dFNC between ICN pairs especially involving the salience, subcortical, sensorimotor, cerebellar and brainstem networks. Further investigations of the effect of internal (bodily) sensations on the time-varying aspects of functional connectivity can improve our understanding of the nature of temporal fluctuations in interrelations between intrinsic brain networks.

An Adaptative Energy Efficient Routing Protocol for MANET

The dynamic nature of Mobile ad hoc network has several constraints like scalability, robust connectivity and limited power constraints. So an efficient routing protocol is required. It should be adaptable and able to maintain stable routes in spite of the dynamic network connectivity. This paper proposes a hybrid protocol, called Poly-Meshed routing protocol (PMRP). It is a cluster-based routing protocol. It deploys the concept of mesh tree. It minimized the control overhead while maintaining robust connectivity and scalability. PMRP uniquely addresses the issues of dynamic adaptation. It has a feature that enables an enhanced connectivity through a hierarchical cluster-based approach. It deploys virtual identifiers. The main advantage of PMRP over the various other MANET protocols lies in simplicity and low message complexity. The simulation results show that the PMRP perform better in terms of Packet Delivery Ratio, Routing Overhead, End to End delay, Effects of Non-Optimality, Configurability and Energy Consumption when compared with AODV (Ad hoc On demand Distance Vector) protocol