Graph Theoretical Approach Research Articles

Altered Resting-State Functional Networks in Nondialysis Patients with Stage 5 Chronic Kidney Disease: A Graph–Theoretical Analysis

This study aimed to investigate the topological characteristics of the resting-state functional network and the underlying pathological mechanism in nondialysis patients with stage 5 chronic kidney disease (CKD5 ND). Eighty-five subjects (21 patients with CKD5 ND, 32 patients with CKD on maintenance hemodialysis (HD), and 32 healthy controls (HCs)) underwent laboratory examinations, neuropsychological tests, and brain magnetic resonance imaging. The topological characteristics of networks were compared with a graph-theoretical approach, and correlations between neuropsychological scores and network properties were analyzed. All participants exhibited networks with small-world attributes, and global topological attributes were impaired in both groups of patients with CKD 5 (ND and HD) compared with HCs (p < 0.05); these impairments were more severe in the CKD5 ND group than in the HD group (p < 0.05). Compared with the HC group, the degree centrality of the CKD5 ND group decreased mainly in the basal ganglia and increased in the bilateral orbitofrontal gyrus, bilateral precuneus, and right cuneus. Correlation analysis showed that the degree of small-worldness, normalized clustering coefficients, and Montreal Cognitive Assessment (MoCA) scores were positively correlated and that characteristic path length was negatively correlated with these variables in patients with CKD5 ND. The nodal efficiency of the bilateral putamen (r = 0.53, p < 0.001 and r = 0.47, p < 0.001), left thalamus (r = 0.37, p < 0.001), and right caudate nucleus (r = 0.28, p = 0.01) was positively correlated with MoCA scores. In conclusion, all CKD5 ND patients exhibited changes in functional network topological properties and were closely associated with mild cognitive impairment. More interestingly, the topological property changes in CKD5 ND patients were dominated by basal ganglia areas, which may be more helpful to understand and possibly reveal the underlying pathological mechanisms of cognitive impairment in CKD5 ND.

Intra- and interbrain synchrony and hyperbrain network dynamics of a guitarist quartet and its audience during a concert.

Playing music in a concert represents a multilevel interaction between musicians and the audience, where interbrain synchronization might play an essential role. Here, we simultaneously recorded electroencephalographs (EEGs) from the brains of eight people during a concert: a quartet of professional guitarists and four participants in the audience. Using phase synchronization analyses between EEG signals within and between brains, we constructed hyperbrain networks, comprising synchronized brain activity across the eight brains, and analyzed them using a graph-theoretical approach. We found that strengths within and between brains in the delta band were higher in the quartet than in the public. Within-brain strengths were higher and between-brain strengths were lower in the music than in the applause condition, both particularly in the quartet group. These changes in coupling strength were accompanied by corresponding changes in the hyperbrain network topology, which were also frequency-specific. Moreover, the network topology and the dynamical structure of guitar sounds showed specific guitar-brain, guitar-guitar, and brain-brain directional associations, indicating multilevel dynamics with upward and downward causation. Finally, the hyperbrain networks exhibit modular structures that were more stable during music performance than during applause. Our findings illustrate complex hyperbrain network interactions in a quartet and its audience during a concert.

Dynamic simulation of functional connectivity and identification of conservation priorities for grassland in China’s Poyang Lake considering ecological processes

Analysing dynamics of landscape functional connectivity and evaluating conservation priority of habitat patches are of great significance for biodiversity conservation. Taking the grassland of Poyang Lake as a case, and comprehensively considering the ecological processes of water level change and species dispersal, this study firstly identified the range and patch composition of grassland under different water levels. Then, the graph theoretical approach was applied to analyse the dynamics of functional connectivity of grassland with different water levels and dispersal distances. Finally, the delta value for probability of connectivity (dPC) and betweenness connectivity (BC) were respectively used to identify the most important grassland patches and key grassland nodes. The results showed as follows: (1) The grassland presented the characteristics of “expanding when water falls while shrinking when water rises”. The grassland patches faced both the segmentation effect and the inundation effect of water. With the increase of water level, the distribution of grassland was gradually shrinking, the distribution of grassland decreased from 1813.61 km2 at the water level of 7.54 m to 182.36 km2 at the water level of 17.17 m, and the fragmentation of large patches and the disappearance of small patches coexisted. (2) From the perspective of water level change, the functional connectivity of grassland has progressively decreased with the increase of water level. From the perspective of species dispersal, the species dispersal distance had an absolutely positive impact on landscape functional connectivity. With the increase of dispersal distance, the functional connectivity of grassland increased dramatically. (3) The 124 important grassland patches were identified and mapped. They were all larger ones in area and primarily distributed in the center of Poyang Lake, especially concentrated in the two national nature reserves. The 16 key grassland nodes were determined and mapped. They were very small in size and mainly scattered in the central and southern margin of Poyang Lake. Giant patches and large patches play an extremely vital role in maintaining the grassland connectivity. However, small patches in pivotal locations can not be ignored as stepping stones or connecting elements. Our study provides a scientific reference for the protection and management of natural habitats in lake areas.

Integrated analysis of smRNAome, transcriptome, and degradome data to decipher microRNAs regulating costunolide biosynthesis in Saussurea lappa

Saussurea lappa (S. lappa) has been known to synthesize medicinally important, costunolide. Due to its immense therapeutic importance, understanding of regulatory mechanism associated with its biosynthesis is crucial. The identification of genes and transcription factors (TFs) in S. lappa, created a clear picture of costunolide biosynthesis pathways. Further to understand the regulation of costunolide biosynthesis by miRNAs, an integrated study of transcriptome, miRNAs, and degradome was performed. Identified candidate miRNAs and associated feed-forward loops (FFLs) illustrates their regulatory role in secondary metabolite biosynthesis. Small RNA and degradome sequencing were performed for leaf and root tissues to determine miRNAs-targets pairs. A total of 711 and 525 such targets were obtained for novel and known miRNAs respectively. This data was used to generate costunolide-specific miRNA-TF-gene interactome to perform systematic analyses through graph theoretical approach. Interestingly, miR171c.1 and sla-miR121 were identified as key regulators to connect and co-regulate both mevalonate and sesquiterpenoid pathways to bio-synthesize costunolide. Tissue-specific FFLs were identified to be involved in costunolide biosynthesis which further suggests the evolutionary co-relation of root-specific networks in synthesis of secondary metabolites in addition to leaf-specific networks. This integrative approach allowed us to determine candidate miRNAs and associated tissue-specific motifs involved in the diversification of secondary metabolites. MiRNAs identified in present study can provide alternatives for bioengineering tool to enhance the synthesis of costunolide and other secondary metabolites in S. lappa.

A novel graph-theoretical clustering approach to find a reduced set with extreme solutions of Pareto optimal solutions for multi-objective optimization problems

Multi-objective optimization problems and their solution algorithms are of great importance as single-objective optimization problems are not usually a true representation of many real-world problems. In general, multi-objective optimization problems result in a large set of Pareto optimal solutions. Each solution in this set is optimal with some trade-offs. Therefore, it is difficult for the decision-maker to select a solution, especially in the absence of subjective or judgmental information. Moreover, an analysis of all the solutions is computationally expensive and, hence, not practical. Thus, researchers have proposed several techniques such as clustering and ranking of Pareto optimal solutions to reduce the number of solutions. The ranking methods are often used to obtain a single solution, which is not a good representation of the entire Pareto set. This paper deviates from the common approach and proposes a novel graph-theoretical clustering method. The quality of the clustering based on the Silhouette score is used to determine the number of clusters. The connectivity in the objective space is used to find representative solutions for clusters. One step forward, we identify ‘extreme solutions’. Hence, the reduced set contains both extreme solutions and representative solutions. We demonstrate the performance of the proposed method by using different 3D and 8D benchmark Pareto fronts as well as Pareto fronts from a case study in Royal Australian Navy. Results revealed that the reduced set obtained from the proposed method outperforms that from the K-means clustering, which is the most popular traditional clustering approach in Pareto pruning.

Integrating geospatial information in the analysis of network disruptions

Disruptions in airline operations are common, especially during extreme weather events, such as hurricanes. These phenomena can and do interrupt smooth and efficient passenger transportation, and severely affect and halt airline operations. Airport network topology plays a crucial role in the development of tools to implement efficient and effective recovery actions for different stakeholders during a disruption. Hence, in this research, we propose a graph theoretical approach that studies the flight/airport network from a topology perspective, but that also incorporates a geospatial component. In our approach, we present two mathematical frameworks to help classify airports in an airport network from two perspectives: their extent of getting disrupted and being a subject to the effect of disruption. We employ simple paths of certain lengths in three different ways to reveal the weather-related impact based on airport position as a direct destination, an intermediate connection, or due to its geographical location in relation to the extreme weather event. Additionally, we develop a rerouting method to identify potential airports for redirecting passengers from a hurricane impacted airport. The computational results show the significance of incorporating geospatial element in identifying the most affected airports during a weather disruption. Moreover, our experimental analysis shows that the forecast track of a hurricane and its impact distance affect the airport network disruption. All of our codes and data are publicly available at https://github.com/harshithameda/AnalysisNetworkDisruptions.

Task Offloading Optimization in NOMA-Enabled Dual-Hop Mobile Edge Computing System Using Conflict Graph

Resource allocation is investigated for offloading computational-intensive tasks in dual-hop mobile edge computing (MEC) system. The envisioned system has both the cooperative access points (APs) with the computing capability and the MEC servers. A user-device (UD), therefore, first uploads a computing task to the nearest AP, and the AP can either locally process the received task or offload to MEC server. To utilize the radio resource blocks (RRBs) in the APs efficiently, we exploit the non-orthogonal multiple access (NOMA) for offloading the tasks from the UDs to the AP(s). In order to investigate the trade-off between latency and energy consumption, this work considers minimizing a weighted-sum that consists of latency and energy consumption, subject to UDs’ rate threshold, tasks’ time-delay, computational frequency scaling, and transmit power allocation constraints. With a joint consideration of all such factors, the problem is NP-hard and its global optimal solution is computationally intractable. A graph-theoretical approach is employed to solve the problem efficiently. Specifically, a novel joint MEC graph-based approach is devised, which solves the scheduling among the UDs, APs, and RRBs, the transmit power control, and the local computational frequency scaling problem(s) jointly. The joint MEC approach achieves near-optimal performance with high computational complexity. To strike a suitable balance between the performance and computational complexity of the resource allocation, a low complexity, yet efficient, pruning graph approach is also devised. The efficiency of the proposed graph-based approaches over several benchmark schemes is verified via extensive simulations.

Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review.

(1) Background: Multiple sclerosis (MS) is an immune system disease in which myelin in the nervous system is affected. This abnormal immune system mechanism causes physical disabilities and cognitive impairment. Functional magnetic resonance imaging (fMRI) is a common neuroimaging technique used in studying MS. Computational methods have recently been applied for disease detection, notably graph theory, which helps researchers understand the entire brain network and functional connectivity. (2) Methods: Relevant databases were searched to identify articles published since 2000 that applied graph theory to study functional brain connectivity in patients with MS based on fMRI. (3) Results: A total of 24 articles were included in the review. In recent years, the application of graph theory in the MS field received increased attention from computational scientists. The graph-theoretical approach was frequently combined with fMRI in studies of functional brain connectivity in MS. Lower EDSSs of MS stage were the criteria for most of the studies (4) Conclusions: This review provides insights into the role of graph theory as a computational method for studying functional brain connectivity in MS. Graph theory is useful in the detection and prediction of MS and can play a significant role in identifying cognitive impairment associated with MS.

Neurological soft signs and structural network changes: a longitudinal analysis in first-episode schizophrenia

BackgroundNeurological soft signs (NSS) are often reported in patients with schizophrenia and may vary with psychopathological symptoms during the course of disease. Many cross-sectional neuroimaging studies have shown that NSS are associated with disturbed network connectivity in schizophrenia. However, it remains unclear how these associations change over time during the course of disorder.MethodsIn present study, 20 patients with first-episode schizophrenia and 20 controls underwent baseline structural magnetic resonance imaging (MRI) scan and at one-year follow-up. Structural network characteristics of patients and controls were analyzed using graph theoretical approach based on MRI data. NSS were assessed using the Heidelberg scale.ResultsAt baseline, patients demonstrated significant changes of the local network properties mainly involving regions of the cortical-subcortical-cerebellar circuits compared to healthy controls. For further analysis, the whole patient group was dichotomized into a NSS-persisting and NSS-decreasing subgroup. After one-year follow-up, the NSS-persisting subgroup showed decreased betweenness in right inferior opercular frontal cortex, left superior medial frontal cortex, left superior temporal cortex, right putamen and cerebellum vermis and increased betweenness in right lingual cortex. However, the NSS-decreasing subgroup exhibited only localized changes in right middle temporal cortex, right insula and right fusiform with decreased betweenness, and in left lingual cortex with increased betweenness.ConclusionsThese findings provide evidence for brain network reorganization subsequent to clinical disease manifestation in patients with first-episode schizophrenia, and support the hypothesis that persisting NSS refer to progressive brain network abnormalities in patients with schizophrenia. Therefore, NSS could help to establish a better prognosis in first-episode schizophrenia patients.

Graph theoretical approach to brain remodeling in multiple sclerosis.

Multiple sclerosis (MS) is a neuroinflammatory disorder damaging structural connectivity. Natural remodeling processes of the nervous system can, to some extent, restore the damage caused. However, there is a lack of biomarkers to evaluate remodeling in MS. Our objective is to evaluate graph theory metrics (especially modularity) as a biomarker of remodeling and cognition in MS. We recruited 60 relapsing-remitting MS and 26 healthy controls. Structural and diffusion MRI, plus cognitive and disability evaluations, were done. We calculated modularity and global efficiency from the tractography-derived connectivity matrices. Association of graph metrics with T2 lesion load, cognition, and disability was evaluated using general linear models adjusting for age, gender, and disease duration wherever applicable. We showed that MS subjects had higher modularity and lower global efficiency compared with controls. In the MS group, modularity was inversely associated with cognitive performance but positively associated with T2 lesion load. Our results indicate that modularity increase is due to the disruption of intermodular connections in MS because of the lesions, with no improvement or preserving of cognitive functions.

Thermophysical modelling of transport and optical properties of 1-propanol+1,3-diaminopropane or 1,2-diaminopropane or 1-amino-2-propanol binary liquid mixture at T=298.15-318.15 K: Molecular interaction analysis by density functional theory (DFT) and graph theoretical approach (GTA)

BackgroundPure liquid properties cannot always satisfy the need of chemical processes. Therefore, liquid mixtures are preferred in different compositions, and the thermo-physical properties of liquid mixtures are highly significant for different processes. The present report involves thermo-physical of amines and 1-propanol. Amines are utilized for CO2 absorption processes. MethodsThe viscosity (η) and refractive index (nD) of 1P (1-propanol) (1) + Amines (1,3-DAP(1,3-diaminopropane), 1,2-DAP(1,2-diaminopropane) and 1A2P(1-amino-2-propanol)) (2) binary mixture were measured at T = 298.15-318.15 K under the atmospheric pressure 0.1 MPa. Experimental data of η and nD were further used to determine deviation in viscosity (Δη), excess Gibbs free energy of activation (G*E) and deviation in refractive index (ΔnD). Δη were analyzed by Bloomfield and Dewan model (BFD) and also examined in term of Graph theoretical approach (GTA). The η and nD data were further correlated by various relevant correlations Significant findingsThe derived thermo-physical properties were well correlated with Redlich-Kister polynomial equation. DFT studies indicated the strength of molecular interactions and supported the graph theory in predicting probable associative molecular species in the liquid mixtures. Further, in 1P + amines binary liquid mixtures, the dominant interaction is OH—–NH.

Adjacency-Based Facility Layout Optimization for Shipyards: A Case Study

_ A shipyard located in Yalova, Turkey, with an annual processing capacity of 50,000 tons of steel, is studied to improve the layout to increase the production efficiency. The material and personnel traffic inside the shipyard is complex, considering the nature of the shipyards. Therefore, an adjacency-based optimization procedure has been adopted in this study since this procedure allows quantitative evaluation of these aspects. Systematic layout planning (SLP) and graph-theoretical approach were used to generate 12 alternative layouts. Then, the best alternative layout was selected using the efficiency rate method. This study demonstrates the use of SLP and graphic-theoretical approach in a maritime context and utilizes the efficiency rate method to compare the alternative layouts, which are between 48.91% and 73.91% efficiencies, respectively. This study is a novel contribution to the literature in terms of demonstrating this methodology for shipbuilding applications, and practical applications for the industry can improve the industry to improve the efficiency of their operations. Introduction In parallel with the increase in global economic growth and technological developments, new ships are needed for marine transportation, energy, security, fishing, etc. This need triggers a globally competitive environment for the production of vessels in a cheap and efficient manner, which directly affects the shipbuilding industry and shipyards (Odabasi 1993). The alignment of the production departments in the shipyard is critical for productivity. The shipyard should be optimized and designed as an efficient system to minimize unnecessary material and personnel traffic. On the other hand, the majority of shipyards are poorly designed. Facility layout deals with the placement of the production departments based on their relative relationship, and facility layout design aims to streamline the workflow and increase productivity (Muther &amp; Hales 2015). Dixit et al. (2020) describe the facility layout as a physical arrangement of departments with a focus on workflow across the system to achieve highest operational efficiency at the lowest cost. Facilities layout is fundamental to shipyard efficiency. To address this gap in the literature, this paper examines the application of systematic layout planning (SLP) and graph-theoretical approach to the optimization of a specific facility layout.

Graph-Theory-Based Derivation, Modeling, and Control of Power Converter Systems

Graph-theoretical approaches have been widely applied in many disciplines, however, their implementation in power electronics converters and systems is still in the exploring stage. In this overview, we summarize the milestones and general applications of this powerful mathematical tool and illustrate its unique benefits in some emerging/challenging power electronics research topics, for example, systematic converter derivation and modeling, advanced control, and so on. Given the multidiscipline nature of power electronics, graph theory can serve as a bridge for experts in different disciplines with a universal framework for complex problems. With solid mathematical foundations, it can stimulate research of topological aspects in both converter and systematic levels, fits well for nowadays power electronics converter. It is hoped that this article can serve as a timely and handy summary for engineers/students who want to enter the field and promote the innovation of power-electronics-enabled applications.

Detection of d 1 ⨂ d 2 dimensional bipartite entangled state: a graph theoretical approach

Braunstein et al have started the study of entanglement properties of the quantum states through graph theoretical approach. Their idea was to start from a simple unweighted graph G and then they have defined the quantum state from the Laplacian of the graph G. A lot of research had already been done using the similar idea. We ask here the opposite one i.e can we generate a graph from the density matrix? To investigate this question, we have constructed a unital map ϕ such that ϕ(ρ) = L ρ + ρ, where the quantum state is described by the density operator ρ. The entries of L ρ depends on the entries of the quantum state ρ and the entries are taken in such a way that L ρ satisfies all the properties of the Laplacian. This make possible to design a simple connected weighted graph from the Laplacian L ρ . We show that the constructed unital map ϕ characterize the quantum state with respect to its purity by showing that if the determinant of the matrix ϕ(ρ) − I is positive then the quantum state ρ represent a mixed state. Moreover, we study the positive partial transpose (PPT) criterion in terms of the spectrum of the density matrix under investigation and the spectrum of the Laplacian associated with the given density matrix. Furthermore, we derive the inequality between the minimum eigenvalue of the density matrix and the weight of the edges of the connected subgraph of a simple weighted graph to detect the entanglement of d 1 ⨂ d 2 dimensional bipartite quantum states. Lastly, we have illustrated our results with few examples.

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach.

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

Longitudinal alterations of modular functional-metabolic coupling in first-episode schizophrenia

Altered network organization and aberrant neurometabolic levels have been associated with schizophrenia. However, modular alterations of functional-neurometabolic coupling in various stages of schizophrenia remain unclear. This longitudinal study enrolled 34 drug-naïve first-episode schizophrenia (FES) patients and 30 healthy controls (HC). The FES patients underwent resting-state functional magnetic resonance imaging (rs-fMRI) and proton magnetic resonance spectroscopy (1H-MRS) at baseline, 2 months, and 6 months of treatment. For 1H-MRS, the concentrations of γ-aminobutyric acid (GABA), N-acetylaspartate (NAA) and glutamate + glutamine in the ventromedial prefrontal cortex region were measured. A graph theoretical approach was applied for functional connectivity-based modular parcellation. We found that intra-default mode network (DMN) connectivity, inter-modular connectivity between the DMN and the hippocampus, and inter-modular connectivity between the DMN and the frontoparietal module were significantly different across 6-month treatment in the FES patients. The inter-module connectivity of the DMN and hippocampus correlated positively with NAA concentration in the HC group, while this correlation was absent in FES patients. This exploratory study suggests an altered modular connectivity in association with neurometabolite concentrations in FES patients and provides insights into multimodal neuroimaging biomarkers in schizophrenia. Future studies with larger sample sizes are needed to consolidate our findings.

Hydrogen-bonded networks in alcohol-acetone binary mixtures: molecular dynamics study.

Our previous studies on various hydrogen-bonded binary systems have shown anomalous physico-chemical properties at lower (10-30%) volume concentrations of either one or both of the components. In order to have a better understanding of this phenomenon, a systematic molecular dynamics study of binary mixtures of acetone with eight primary alcohols (R-OH, with R = 1 to 8) was undertaken. The structure of the binary systems is studied using radial distribution function, hydrogen bond statistics, and graph theoretical approach. Two distinct features are observed. Firstly, the bunching of R = (1, 2), R = (3, 4, 6), and R = (5, 7, 8)-acetone mixture in their hydrogen bond characteristics. Secondly, the number of alcohol-acetone hydrogen bonds is more for R = (3, 4, 6) and the alcohol-alcohol hydrogen bonds for the rest, indicating a preferential bonding of R = (3, 4, 6) alcohols with acetone when compared to the rest. With an increase in acetone concentration, the average degree of association decreases for all systems, showing an overall decrease in hydrogen bond multimer structures. The hydrogen bond networks are visualized using graph theory.

Analysis of thermo-physical properties and qualitative investigation of molecular interactions in terms of Graph Theoretical Approach in binary liquid mixtures containing 1-propanol + Amines (1,3-diaminopropane or 1,2-diaminopropane or 1-amino-2-propanol) at T = 298.15–318.15 K

In the present report, the molecular interactions between 1P (1-propanol) and Amines (1,3-DAP (1,3-diaminopropane) or 1,2-DAP (1,2-diaminopropane) or 1A2P (1-amino-2-propanol)) are examined by insight of thermo-physical properties of binary liquid mixtures. Density (ρ) and speed of sound (u) were measured for 1P, Amines and their binary mixture at T = 298.15–318.15 K at 0.1 MPa. Experimental data of ρ and u were used to evaluate VmE (excess molar volume), ksE (excess molar isentropic compressibility), VfE (excess free volume), uE (excess ultrasonic speed), LfE (excess intermolecular free length) and VaE (excess available volume). Further, these calculated properties were correlated with Redlich-Kister polynomial (R.K.). VmE data is also examined in term of GTA (Graph theoretical approach) and PFP (Prigogine-Flory-Patterson) theory. FT-IR spectroscopic studies supported the results and clearly indicated that a strong intermolecular interactions exist between 1P and Amines components in the binary mixture which is also recognized by GTA. The u data were examined by different correlations like Nomoto, Impedance dependence, Van Dael, JFLT (Jacobson’s free length theory) and CFT (Schaaff’s collision factor theory).

Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach.

ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC.ResultsIn patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model.ConclusionFunctional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM.

CP‐SyNet: A tool for generating customised cyber‐power synthetic network for distribution systems with distributed energy resources

The integration of distributed energy resources and advancement in information technology has enabled the transition of traditional power distribution systems to active cyber-physical distribution systems. A growing amount of research has been done on the modelling, analysis, and optimisation of power distribution system behaviour. However, existing publicly available distribution test feeders are limited in numbers and have minimal features. Furthermore, these test feeders do not include cyber models and are not customisable. To bridge this gap, we propose and develop Cyber-physical synthetic distribution system network (CP-SyNet), a tool for generating customisable cyber-physical synthetic distribution test feeders. CP-SyNet generates three-phase unbalanced test feeders according to users' requirements, while simultaneously considering both the cyber side and the physical side of the network for cyber-physical analysis. The physical test network is developed using a graph-theoretical approach that employs information from existing test feeders. The cyber side considers an equivalent communication network by transforming the physical topology into possible and feasible simulated network. Two examples are presented to demonstrate the feasibility of the proposed framework to generate cyber-physical test feeders.