Global Network Topology Research Articles

Graph Theory Analysis of Functional Connectivity Combined with Machine Learning Approaches Demonstrates Widespread Network Differences and Predicts Clinical Variables in Temporal Lobe Epilepsy.

Understanding how global brain networks are affected in epilepsy may elucidate the pathogenesis of seizures and its accompanying neurobehavioral comorbidities. We investigated functional changes within neural networks in temporal lobe epilepsy (TLE) using graph theory analysis of resting-state connectivity. Twenty-seven TLE presurgical patients (age 41.0 ± 12.3 years) and 85 age, gender, and handedness equivalent healthy controls (HCs; age 39.7 ± 16.9 years) were enrolled. Eyes-closed resting-state functional magnetic resonance image scans were analyzed to compare network properties and functional connectivity (FC) changes. TLE subjects showed significantly higher global efficiency, lower clustering coefficient ratio, and lower shortest path lengths ratio than HCs, as an indication of a more synchronized, yet less segregated network. A trend of functional reorganization with a shift of network hubs to the contralateral hemisphere was noted in TLE subjects. Support vector machine (SVM) with linear kernel was trained to separate between neural networks in TLE and HC subjects based on graph measurements. SVM analysis allowed separation between TLE and HC networks with 80.66% accuracy using eight features of graph measurements. Support vector regression (SVR) was used to predict neurocognitive performance from graph metrics. An SVR linear predictor showed discriminative prediction accuracy for four key neurocognitive variables in TLE (absolute R value range: 0.61-0.75). Despite TLE, our results showed both local and global network topology differences that reflect widespread alterations in FC in TLE. Network differences are discriminative between TLE and HCs using data-driven analysis and predicted severity of neurocognitive sequelae in our cohort.

Event-Triggered Bipartite Consensus for Multiagent Systems: A Zeno-Free Analysis

In this article, the bipartite consensus of first-order multiagent systems with a connected structurally balanced signed graph is studied. To reduce the communications among agents, a distributed event-triggered control law is proposed, where the event-triggering condition of each agent only uses its own state and the sampled states of its neighbours, and no knowledge of the global network topology is required. By relating to the nonexistence of some finite-time convergence, a novel analysis is given to show that there is no Zeno behavior in the proposed event-triggered multiagent system. Then, from the Lyapunov stability theory and the algebraic graph theory, it is proved that all agents can reach agreement with an identical magnitude but opposite signs. Finally, a numerical example is given to illustrate the efficiency and feasibility of the proposed results.

Reduced structural connectivity in cortico-striatal-thalamic network in neonates with congenital heart disease

Impaired brain development has been observed in newborns with congenital heart disease (CHD). We performed graph theoretical analyses and network-based statistics (NBS) to assess global brain network topology and identify subnetworks of altered connectivity in infants with CHD prior to cardiac surgery. Fifty-eight infants with critical/serious CHD prior to surgery and 116 matched healthy controls as part of the developing Human Connectome Project (dHCP) underwent MRI on a 3T system and high angular resolution diffusion MRI (HARDI) was obtained. Multi-tissue constrained spherical deconvolution, anatomically constrained probabilistic tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT2) was used to construct weighted structural networks. Network topology was assessed and NBS was used to identify structural connectivity differences between CHD and control groups. Structural networks were partitioned into core and peripheral nodes, and edges classed as core, peripheral, or feeder. NBS identified one subnetwork with reduced structural connectivity in CHD infants involving basal ganglia, amygdala, hippocampus, cerebellum, vermis, and temporal and parieto-occipital lobe, primarily affecting core nodes and edges. However, we did not find significantly different global network characteristics in CHD neonates. This locally affected sub-network with reduced connectivity could explain, at least in part, the neurodevelopmental impairments associated with CHD.

Structural brain networks and functional motor outcome after stroke-a prospective cohort study.

The time course of topological reorganization that occurs in the structural connectome after an ischaemic stroke is currently not well understood. We aimed to determine the evolution of structural brain networks in stroke patients with motor deficits and relate changes in their global topology to residual symptom burden and functional impairment. In this prospective cohort study, ischaemic stroke patients with supratentorial infarcts and motor symptoms were assessed longitudinally by advanced diffusion MRI and detailed clinical testing of upper extremity motor function at four time points from the acute to the chronic stage. For each time point, structural connectomes were reconstructed, and whole-hemisphere global network topology was quantified in terms of integration and segregation parameters. Using non-linear joint mixed-effects regression modelling, network evolution was related to lesion volume and clinical outcome. Thirty patients were included for analysis. Graph-theoretical analysis demonstrated that, over time, brain networks became less integrated and more segregated with decreasing global efficiency and increasing modularity. Changes occurred in both stroke and intact hemispheres and, in the latter, were positively associated with lesion volume. Greater change in topology was associated with larger residual symptom burden and greater motor impairment 1, 3 and 12 months after stroke. After ischaemic stroke, brain networks underwent characteristic changes in both ipsi- and contralesional hemispheres. Topological network changes reflect the severity of damage to the structural network and are associated with functional outcome beyond the impact of lesion volume.

Diminished large-scale functional brain networks in absolute pitch during the perception of naturalistic music and audiobooks

Previous studies have reported the effects of absolute pitch (AP) and musical proficiency on the functioning of specific brain regions or distinct subnetworks, but they provided an incomplete account of the effects of AP and musical proficiency on whole-brain networks. In this study, we used EEG to estimate source-space whole-brain functional connectivity in a large sample comprising AP musicians (n ​= ​46), relative pitch (RP) musicians (n ​= ​45), and Non-musicians (n ​= ​34) during resting state, naturalistic music listening, and audiobook listening. First, we assessed the global network density of the participants’ functional networks in these conditions. As revealed by cluster-based permutation testing, AP musicians showed a decreased mean degree compared to Non-musicians whereas RP musicians showed an intermediate mean degree not statistically different from Non-musicians or AP-musicians. This effect was present during naturalistic music and audiobook listening, but, crucially, not during resting state. Second, we identified the subnetworks that drove group differences in global network density using the network-based statistic approach. We found that AP musicians showed decreased functional connectivity between major hubs of the default mode network during both music and audiobook listening compared to Non-musicians. Third, we assessed group differences in global network topology while controlling for network density. We did not find evidence for group differences in the clustering coefficient and characteristic path length. Taken together, we found first evidence of diminished whole-brain functional networks in AP musicians during the perception of naturalistic auditory stimuli. These differences might reflect a complex interplay between AP ability, musical proficiency, music processing, and auditory processing per se.

Disrupted brain structural connectivity in Pediatric Bipolar Disorder with psychosis

Bipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Here we compare the network properties of whole-brain structural connectomes of euthymic PBD patients with psychosis, a variant of PBD, and matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients with psychosis in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed, revealing trends of association between patients’ IQ and affective psychotic symptoms with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD with psychosis is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which show trends of association with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.

Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

What network motifs tell us about resilience and reliability of complex networks

Network motifs are often called the building blocks of networks. Analysis of motifs has been found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. This phenomenon of the impact of local structure has been recently documented in network fragility analysis and classification. At the same time, many studies of networks still tend to focus on global topological measures, often failing to unveil hidden mechanisms behind vulnerability of real networks and their dynamic response to malfunctions. In this paper, a study of motif-based analysis of network resilience and reliability under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of networks. These methods are demonstrated on electricity transmission networks of 4 European countries, and the results are compared with commonly used resilience and reliability measures.

Altered Topology of the Structural Brain Network in Patients With Post-stroke Depression.

There is a pressing need to further our understanding of the mechanisms underlying the depression symptoms in patients with post-stroke depression (PSD) in order to inform targeted therapeutic approaches. While previous research has demonstrated a reorganization in the functional brain network of PSD, it remains uncertain whether, or not it also occurs in the structural brain network. We therefore aim to investigate the structural brain network of patients with PSD as compared to post-stroke non-depression (PSND) patients. In addition, our research considers the relationship between network metrics and functional measurements. Thirty-one PSD patients and twenty-three PSND patients were recruited. All patients underwent MRI and functional assessments, including the Barthel index, mini-mental state examination (MMSE), and Hamilton depression rating scale (HAMD). Diffusion tensor imaging was used to construct the structural brain network and to conduct the subsequent graph theoretical analysis. Network measures were computed and compared between PSD and PSND patients. Associations between functional assessments and network measures were studied as well. We successfully detected increased global and local efficiency in patients with PSD. Regions with disrupted local connections were located primarily in the cognitive and limbic systems. More importantly, PSD patients’ global and regional network measures were associated with depression severity, as measured by HAMD. These findings suggest that disrupted global and local network topologies might contribute to PSD patients’ depression symptoms. Therefore, connectome-based network measures could be potential bio-markers for evaluating stroke patients’ depression levels.

Distributed Link Removal Using Local Estimation of Network Topology

This paper considers the problem of network structure manipulation in the absence of information on the global network topology. In particular, the problem of removing some of links is investigated in order to slow or stop the spread of disease in a network while preserving its connectivity. Existing methods solve this combinatorial problem in a centralized manner and they require the global information of network structure. In this paper, we propose a distributed design algorithm to compute a suboptimal solution to this problem efficiently by mimicking gradient based method, namely by iteratively removing one or multiple links at a time from the network. Specifically, using matrix perturbation analysis we formulate an optimization problem involving the eigenvector associated with the largest eigenvalue of the adjacency matrix and whose solution is equal to a suboptimal solution to the original problem. This strategy also enables us to overcome the combinatorial issue of the problem. Distributed algorithms to estimate the eigenvector and to verify network's connectivity are then proposed which facilitate us to solve the new optimization problem. In addition, topological insights into the proposed algorithm and optimality of its solution are also discussed. Finally, the proposed distributed design method is demonstrated and evaluated via several numerical examples.

Near-Neighbor Interactions in the NS3-4A Protease of HCV Impact Replicative Fitness of Drug-Resistant Viral Variants

A variety of amino acid substitutions in the NS3-4A protease of the hepatitis C virus lead to protease inhibitor (PI) resistance. Many of these significantly impair the replication fitness of the resistant variants in a genotype- and subtype-dependent manner, a critical factor in determining the probability with which resistant variants will persist. However, the underlying molecular mechanisms are unknown. Here, we present a novel residue-interaction network approach to determine how near-neighbor interactions of PI resistance mutations in NS3-4A can impact protease functional sites dependent on their genomic background. We constructed subtype-specific consensus residue networks for subtypes 1a and 1b from protease structure ensembles combined with biological properties of protein residues and evolutionary amino acid conservation. By applying local and global network topology analysis and visual exploration, we characterize PI resistance-associated sites and outline differences in near-neighbor interactions. We find local residue-interaction patterns and features at protease functional sites that are subtype specific. The noncovalent bonding patterns indicate higher fitness costs conferred by PI resistance mutations in a subtype 1b genomic background and explain the prevalence of Q80K and R155K in subtype 1a. Based on local residue interactions, we predict a subtype-specific role for the protease residue NS3–Q80 in molecular mechanisms related to the assembly of infectious virus particles that is supported by experimental data on the capacity of Q80K variants to replicate and produce infectious virus in subtype 1a and 1b cell culture.

Identifying similar networks using structural hierarchy

Comparing structural similarities among complex networks is an important task in several scientific and social science applications. Existing techniques for quantifying network similarity range from network-centric methods that consider global network topology to node-centric methods that consider local node-level sub-structures.In this paper, we address the research gap between computationally expensive network-centric approaches and myopic node-centric network comparison methods by introducing a novel approach to quantify network similarity based on hierarchical graph decomposition. The approach adequately captures both global and local topology and is motivated by the observation that networks from diverse domains such as physical, chemical, biological and social systems exhibit an inherent structural hierarchy that emerges from local dyadic and triadic interactions. The proposed algorithm, Network Similarity via graph Decomposition (NSD), extracts network signatures from hierarchical decomposition of networks and uses Canberra distance to quantify the similarity between signatures. We use two well-known graph decomposition methods to expose network hierarchy resulting in two variations of NSD. We find that our approach groups similar networks better than competing algorithms. Experimentation using 40 real-world networks, 15 massive networks, and 30 large synthetic networks establishes that the proposed methodology is effective, scalable, sensitive and applicable to wide variety of networks.

A PSO based multi-domain virtual network embedding approach

This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.

Impaired Global Network Topology in Residual Connectomes in Stroke-induced Aphasia (P5.1-005)

Impaired Global Network Topology in Residual Connectomes in Stroke-induced Aphasia (P5.1-005)

Cannabis-dependent adolescents show differences in global reward-associated network topology: A functional connectomics approach.

Adolescence may be a period of increased vulnerability to the onset of drug misuse and addiction due to changes in developing brain networks that support cognitive and reward processing. Cannabis is a widely misused illicit drug in adolescence which can lead to dependence and alterations in reward-related neural functioning. Concerns exist that cannabis-related alterations in these reward networks in adolescence may sensitize behaviour towards all forms of reward that increase the risk of further drug use. Taking a functional connectomics approach, we compared an acutely abstinent adolescent cannabis-dependent (CAN) group with adolescent controls (CON) on global measures of network topology associated with anticipation on a monetary incentive delay task. In the presence of overall superior accuracy, the CAN group exhibited superior global connectivity (clustering coefficient, efficiency, characteristic path length) during monetary gain anticipation compared with the CON group. Additional analyses showed that the CAN group exhibited significantly greater connectivity strength during monetary gain anticipation across a subnetwork that included mesocorticolimbic nodes involving both interhemispheric and intrahemispheric connections. We discuss how these differences in reward-associated connectivity may allude to subtle functional alterations in network architecture in adolescent cannabis-dependence that could enhance the motivation for nondrug reward during acute abstinence.

Deteriorated functional and structural brain networks and normally appearing functional-structural coupling in diabetic kidney disease: a graph theory-based magnetic resonance imaging study.

This study was conducted in order to investigate the topological organization of functional and structural brain networks in diabetic kidney disease (DKD) and its potential clinical relevance. Two hundred two subjects (62 DKD patients, 60 diabetes mellitus [DM] patients, and 80 healthy controls) underwent laboratory examination, neuropsychological test, and magnetic resonance imaging (MRI). Large-scale functional and structural brain networks were constructed and graph theoretical network analyses were performed. The effect of renal function on brain functional and structural networks in DKD patients was further evaluated. Correlations were performed between network properties and neuropsychological scores and clinical variables. Progressing deteriorated global and local network topology organizations (especially for functional network) were observed for DKD patients compared with control subjects (all p < 0.05, Bonferroni-corrected), with intermediate values for the patients with DM. DKD patients showed normally appearing functional-structural coupling compared with controls, while DM patients manifested functional-structural decoupling (p < 0.05, Bonferroni-corrected). Impaired kidney function markedly affected functional and structural network organization in DKD patients (all p < 0.05). Urea nitrogen correlated with global and local efficiency in the structural networks (r = - 0.551, p < 0.001; r = - 0.476, p < 0.001, respectively). Global and local efficiency in the structural networks and normalized characteristic path length in the functional networks were associated with information processing speed and/or psychomotor speed. DKD patients showed enhanced functional and structural brain network disruption and normally appearing functional-structural coupling compared with DM patients, which correlated with kidney function, renal toxins, and cognitive performance. • DKD patients showed markedly disrupted functional and structural brain network efficiency measures compared with DM patients and healthy controls. • Reduced kidney function clearly deteriorated functional and structural brain networks in DKD patients. • DKD patients displayed normally appearing functional-structural coupling compared with DM patients.

Cognitive phenotypes in temporal lobe epilepsy are associated with distinct patterns of white matter network abnormalities.

To identify distinct cognitive phenotypes in temporal lobe epilepsy (TLE) and evaluate patterns of white matter (WM) network alterations associated with each phenotype. Seventy patients with TLE were characterized into 4 distinct cognitive phenotypes based on patterns of impairment in language and verbal memory measures (language and memory impaired, memory impaired only, language impaired only, no impairment). Diffusion tensor imaging was obtained in all patients and in 46 healthy controls (HC). Fractional anisotropy (FA) and mean diffusivity (MD) of the WM directly beneath neocortex (i.e., superficial WM [SWM]) and of deep WM tracts associated with memory and language were calculated for each phenotype. Regional and network-based SWM analyses were performed across phenotypes. The language and memory impaired group and the memory impaired group showed distinct patterns of microstructural abnormalities in SWM relative to HC. In addition, the language and memory impaired group showed widespread alterations in WM tracts and altered global SWM network topology. Patients with isolated language impairment exhibited poor network structure within perisylvian cortex, despite relatively intact global SWM network structure, whereas patients with no impairment appeared similar to HC across all measures. These findings demonstrate a differential pattern of WM microstructural abnormalities across distinct cognitive phenotypes in TLE that can be appreciated at both the regional and network levels. These findings not only help to unravel the underlying neurobiology associated with cognitive impairment in TLE, but they could also aid in establishing cognitive taxonomies or in the prediction of cognitive course in TLE.

A connectome-based mechanistic model of focal cortical dysplasia.

Neuroimaging studies have consistently shown distributed brain anomalies in epilepsy syndromes associated with a focal structural lesion, particularly mesiotemporal sclerosis. Conversely, a system-level approach to focal cortical dysplasia has been rarely considered, likely due to methodological difficulties in addressing variable location and topography. Given the known heterogeneity in focal cortical dysplasia histopathology, we hypothesized that lesional connectivity consists of subtypes with distinct structural signatures. Furthermore, in light of mounting evidence for focal anomalies impacting whole-brain systems, we postulated that patterns of focal cortical dysplasia connectivity may exert differential downstream effects on global network topology. We studied a cohort of patients with histologically verified focal cortical dysplasia type II (n = 27), and age- and sex-matched healthy controls (n = 34). We subdivided each lesion into similarly sized parcels and computed their connectivity to large-scale canonical functional networks (or communities). We then dichotomized connectivity profiles of lesional parcels into those belonging to the same functional community as the focal cortical dysplasia (intra-community) and those adhering to other communities (inter-community). Applying hierarchical clustering to community-reconfigured connectome profiles identified three lesional classes with distinct patterns of functional connectivity: decreased intra- and inter-community connectivity, a selective decrease in intra-community connectivity, and increased intra- as well as inter-community connectivity. Hypo-connectivity classes were mainly composed of focal cortical dysplasia type IIB, while the hyperconnected lesions were type IIA. With respect to whole-brain networks, patients with hypoconnected focal cortical dysplasia and marked structural damage showed only mild imbalances, while those with hyperconnected subtle lesions had more pronounced topological alterations. Correcting for interictal epileptic discharges did not impact connectivity patterns. Multivariate structural equation analysis provided a mechanistic model of such complex, diverging interactions, whereby the focal cortical dysplasia structural makeup shapes its functional connectivity, which in turn modulates whole-brain network topology.

Altered global brain network topology as a trait marker in patients with anorexia nervosa.

Resting state functional magnetic resonance imaging studies have identified functional connectivity patterns associated with acute undernutrition in anorexia nervosa (AN), but few have investigated recovered patients. Thus, a trait connectivity profile characteristic of the disorder remains elusive. Using state-of-the-art graph-theoretic methods in acute AN, the authors previously found abnormal global brain network architecture, possibly driven by local network alterations. To disentangle trait from starvation effects, the present study examines network organization in recovered patients. Graph-theoretic metrics were used to assess resting-state network properties in a large sample of female patients recovered from AN (recAN, n = 55) compared with pairwise age-matched healthy controls (HC, n = 55). Indicative of an altered global network structure, recAN showed increased assortativity and reduced global clustering as well as small-worldness compared with HC, while no group differences at an intermediate or local network level were evident. However, using support-vector classifier on local metrics, recAN and HC could be separated with an accuracy of 70.4%. This pattern of results suggests that long-term recovered patients have an aberrant global brain network configuration, similar to acutely underweight patients. While the finding of increased assortativity may represent a trait marker of AN, the remaining findings could be seen as a scar following prolonged undernutrition.

On constructing contention aware connected dominating sets for inter-connectivity among internet of things devices

The heterogeneous IoT devices are often equipped with wireless interfaces capable of creating self-organising multi-hop networks. Any device in such networks often requires broadcasting of packets for data dissemination and route discovery. The straightforward flooding has serious drawback on network throughput due to redundant traffics, contention, and collision. Several methods for creating connected dominant sets (CDS) have been proposed to overcome such bottleneck where only the members of the CDS participate in packet forwarding. However, the state-of-the-art CDS construction algorithms aim at minimising number of forwarding and overlook contention minimisation. In this paper, we provide a novel mathematical analysis of contention and show that contention is mostly dependent on node density and the transmission radius. Then a new centralised algorithm is devised which intellectually selects member nodes while creating a CDS. This helps to reduce contention. Since collecting global network topology information is very difficult to achieve, a distributed algorithm and a hybrid distributed algorithm have also been devised. Finally, the proposed algorithms have been implemented in the state-of-the-art NS-2 simulator where their performance has been captured under realistic settings. The results show a significant reduction in contention. Interestingly, the proposed algorithms' contention and delay performance outperform some other state-of-the-art algorithms' performance although the number of forwarding is increased marginally.