Large-scale Networks Research Articles

Open label pilot of personalized, neuroimaging-guided theta burst stimulation in early-stage Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) is characterized by cerebral amyloid plaques and neurofibrillary tangles and disruption of large-scale brain networks (LSBNs). Transcranial magnetic stimulation (TMS) has emerged as a potential non-invasive AD treatment that may serve as an adjunct therapy with FDA approved medications.MethodsWe conducted a 10-subject open label, single site study evaluating the effect of functional connectivity-resting state functional MRI guided-approach to TMS targeting with dysfunctional LSBNs in subjects with biomarker-confirmed early-stage AD (https://clinicaltrials.gov/study/NCT05292222). Subjects underwent pre-post imaging and testing to assess connectivity dysfunction and cognition. All participants received intermittent theta burst stimulation [(iTBS), (80% motor threshold; 5 sessions per day; 5 days; 3 targets; 18,000 pulses/day)] over 2 weeks. Three Human Connectome Project (HCP) defined parcellations were targeted, with one common right temporal area G dorsal (RTGd) target across all subjects and two personalized.ResultsWe identified the following parcellations to be dysfunctional: RTGd, left area 8A ventral (L8Av), left area 8B lateral (L8BL), and left area 55b (L55b). There were no changes in these parcellations after treatment, but subjects showed improvement on the Repeatable Battery for the Assessment of Neuropsychological Status attention index (9.7; p = 0.01). No subject dropped out of the treatment, though 3 participants were unable to tolerate the RTGd target due to facial twitching (n = 2) and anxiety (n = 1).ConclusionAccelerated iTBS protocol was well-tolerated and personalized target-based treatment is feasible in early-stage AD. Further sham-controlled clinical trials are necessary to determine if this is an effective adjunctive treatment in early-stage AD.

Fuzzy-based Bi-objective Energy Efficient Routing Protocol for Large Scale Mobile Ad-hoc Network

MANET is an infrastructure-less network, which is comprised of a greater number of nodes that are moving in a random manner. Establishing efficient and reliable routes is the crucial design issue in MANET. In addition, providing energy-efficient routes for performing communication operations in MANET is also a challenging goal. Increasing network lifetime and decreasing overall traffic load are the significant considerations while designing the MANET. An energy optimization routing mechanism with a fuzzy basis that is state-based and influenced by COVID-19 is used to prevent and control epidemics. Based on their lifespan and varying mobility speeds, it is dependent on energy optimization yield under various scenarios. Fuzzy-based bi-objective criteria in energy-efficient routing protocol for the mobile ad-hoc network to select the different constraints like link stability, bandwidth, and battery life. The main goal of this paper is to obtain the optimal solution to choose the best route, limit battery life, and change the movement of mobile nodes for large-scale mobile ad-hoc networks. It is demonstrated that the possible solution relates to the domain of selecting the variables by outlining the connection between the energy function’s variables. Researchers optimize a path's link lifespan, resulting in the choice of the shortest path being particularly efficient in COVID pandemic prevention and treatment.

Enterprise Network Design and Deployment Practice based on eNSP

In recent years, with the frequent occurrence of network security incidents, the importance of enterprise network security has become increasingly prominent. The design of enterprise networks should not only meet the requirements of efficient communication, but also prioritize security to ensure the stable operation of the network. This article constructs a large-scale corporate park network, including the DMZ area and connected to the backbone network through dedicated lines. At the same time, with offices, it is necessary to achieve interconnectivity between the two networks. Based on dynamic routing protocol, IPSecVPN, VRRP and other technologies, combined with Huawei network equipment, the company has built a secure and efficient enterprise network architecture. The design content covers wireless network coverage, IPv6 support, traffic optimization, and redundant path selection to ensure stable communication between the company's campus network, offices, and the Internet, ultimately achieving a balance between security, flexibility, and reliability.

The role of acoustic telemetry in assessing fish connectivity within marine seascapes: A global review.

The study of aquatic animal movements is a rapidly growing field of research, with tracking methodology ever developing and refining. Acoustic telemetry is arguably the most popular method used to study the movements of fish. Despite this method being able to elucidate many aspects of movement behavior, including residency, home range, and migration, among others, one aspect that remains challenging is the study and definition of connectivity, particularly within marine seascapes. As such, this review assesses published literature on acoustic telemetry studies, which have specifically assessed some aspect of fish connectivity, and discusses these in terms of study distribution and overall trends, the diversity of taxa and life stage assessed, the role that large-scale acoustic telemetry networks plays in assessing connectivity of marine fishes, how connectivity studies have been used in an applied context, and proposes definitions linked to specific types of connectivity, which will assist future researchers when conceptualizing studies. Further, methods that can be used in conjunction with acoustic telemetry to complement the data are discussed. Given that marine resources and habitats are intricately connected, this review highlights the critical role that acoustic telemetry can play in assessing this link. It is envisaged that our developed framework of connectivity definitions will assist future studies and stakeholders in assessing ecosystem functioning and ultimately contribute to improved conservation and management of marine fish populations and ecosystems.

Alterations in large-scale resting-state network nodes following transcranial focused ultrasound of deep brain structures

BackgroundLow-intensity transcranial focused ultrasound (tFUS) is a brain stimulation approach that holds promise for the treatment of brain-based disorders. Studies in humans have shown that tFUS can successfully modulate perfusion in focal sonication targets, including the amygdala; however, limited research has explored how tFUS impacts large-scale neural networks.ObjectiveThe aim of the current study was to address this gap and examine changes in resting-state connectivity between large-scale network nodes using a randomized, double-blind, within-subjects crossover study design.MethodsHealthy adults (n = 18) completed two tFUS sessions, 14 days apart. Each session included tFUS of either the right amygdala or the left entorhinal cortex (ErC). The inclusion of two active targets allowed for within-subjects comparisons as a function of the locus of sonication. Resting-state functional magnetic resonance imaging was collected before and after each tFUS session.ResultstFUS altered resting-state functional connectivity (rsFC) within and between rs-network nodes. Pre-to-post sonication of the right amygdala modulated connectivity within nodes of the salience network (SAN) and between nodes of the SAN and the default mode network (DMN) and frontoparietal network (FRP). A decrease in SAN to FPN connectivity was specific to the amygdala target. Pre-to-post sonication of the left ErC modulated connectivity between the dorsal attention network (DAN) and FPN and DMN. An increase in DAN to DMN connectivity was specific to the ErC target.ConclusionThese preliminary findings may suggest that tFUS induces neuroplastic changes beyond the immediate sonication target. Additional studies are needed to determine the long-term stability of these effects.

Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning

In the process of the collaborative work of Unmanned Aerial Vehicle (UAV) clusters, the cluster communication node test is often carried out by a single-node test, which leads to poor topology and robustness of the overall network system, an imbalanced communication network load, and high complexity of the communication test, which seriously affects the diversified needs of current users and the efficiency of large-scale task processing. To solve this problem, a distributed method for UAV cluster testing, called UTDR (distributed UAV cluster Testing method by using Deep Reinforcement learning), based on the Deep Deterministic Policy Gradient (DDPG) is proposed in this work. The system management node is used to monitor the status of the UAV testing task execution node and bandwidth resources. By taking advantage of the method of continuous interaction between the agent and the environment, the future state of the node after processing the current task to be assigned is predicted and evaluated from the perspective of interpretability, so as to achieve the effectiveness and stability of the UAV cluster testing task collaborative execution. The experimental results show that our proposed method can ensure the stable operation of the UAV cluster, accurately predict the future state, and reduce the load degree and bandwidth resource consumption of the large-scale test task network system.

Older is order: entropy reduction in cortical spontaneous activity marks healthy aging

BackgroundEntropy trajectories remain unclear for the aging process of human brain system due to the lacking of longitudinal neuroimaging resource.ResultsWe used open data from an accelerated longitudinal cohort (PREVENT-AD) that included 24 healthy aging participants followed by 4 years with 5 visits per participant to establish cortical entropy aging curves and distinguish with the effects of age and cohort. This reveals that global cortical entropy decreased with aging, while a significant cohort effect was detectable that people who were born earlier showed higher cortical entropy. Such entropy reductions were also evident for large-scale cortical networks, although with different rates of reduction for different networks. Specifically, the primary and intermediate networks reduce their entropy faster than the higher-order association networks.ConclusionsOur study confirmed that cortical entropy decreases continually in the aging process, both globally and regionally, and we conclude two specific characteristics of the entropy of the human cortex with aging: the shift of the complexity hierarchy and the diversity of complexity strengthen.

Development of a multidimensional centrality metric for ranking nodes in complex networks

Ranking nodes in complex networks is a fundamental task in network science, with significant applications in fields like social networks, bioinformatics, and information dissemination. Many existing methods struggle to efficiently capture the multifaceted relationships between nodes, particularly in large-scale networks. However, they often ignore the semantic relationships between nodes when gathering information. To address these challenges, this paper introduces SAASP, a Semi-local centrality measure that combines an Augmented graph with Average Shortest Path theory to efficiently identify influential nodes in complex networks. In SAASP, the augmented graph can represent global relationships between nodes as semantic similarities, allowing distant relationships to be considered in node ranking. Additionally, it incorporates an enhanced version of average shortest path theory to handle the computational complexity associated with large networks. By extracting local subgraphs for each node and considering the extended neighborhood concept, SAASP ensures scalability while preserving key structural properties. The integration of the augmented graph and average shortest path theory enables SAASP to accurately and efficiently identify influential nodes in large-scale complex networks with lower complexity. The effectiveness of the proposed metric is demonstrated through extensive experiments using the SIR (Susceptible-Infected-Removed) model and Kendall's coefficient, where it outperforms existing centrality measures on real-world datasets.

A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations

Abstract Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But to prove causation, manipulations of connectivity would have to be studied. However, the fine-grained scale at which non-random trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large-scale network models in SONATA format. In addition to creating or manipulating the connectome of a model, it provides tools to fit parameters of stochastic connectivity models against existing connectomes. This enables rapid replacement of any existing connectome with equivalent connectomes at different levels of complexity, or transplantation of connectivity features from one connectome to another, for systematic study. We employed the framework in a detailed model of rat somatosensory cortex in two exemplary use cases: transplanting interneuron connectivity trends from electron microscopy data and creating simplified connectomes of excitatory connectivity. We ran a series of network simulations and found diverse shifts in the activity of individual neuron populations causally linked to these manipulations.

NeutronSketch: An in-depth exploration of redundancy in large-scale graph neural network training

Graph Neural Networks (GNNs) have achieved notable success in various applications. However, the increasing scale of real-world graphs poses a challenge for efficient GNN training. Recent works propose reducing computations by reducing the scale of the input graph through graph compression or sparsification. However, these methods introduce deep learning models requiring multiple training iterations, which leads to significant additional time overhead. Meanwhile, they fail to consider redundant information that is unhelpful or even harmful for model training in the input graphs. In this work, we propose a universal, one-time redundancy removal method called NeutronSketch to remove the redundant information from the input graph. This method can improve training efficiency while maintaining the model accuracy. In the experiments, we compare NeutronSketch with graph compression, sparsification, and coarsening methods. The results show that NeutronSketch has a faster execution speed and better model accuracy. Additionally, we apply NeutronSketch to the sample-based GNN models. The results show that NeutronSketch reduces input graph scale by an average of 25% compared to the original graph, reducing training time by 10%–90% while maintaining the model accuracy.

Multiyear Maintenance and Rehabilitation Optimization for Large-Scale Infrastructure Networks: An Enhanced Genetic Algorithm Approach

Multiyear Maintenance and Rehabilitation Optimization for Large-Scale Infrastructure Networks: An Enhanced Genetic Algorithm Approach

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

The Dial-a-Ride problem with meeting points: A problem formulation for shared demand–responsive transit

In this paper, a formulation for the Dial-a-Ride Problem with Meeting Points (DARPmp) is introduced. The problem consists of defining routes that satisfy trip requests between pick-up and drop-off points while complying with time window, ride time, vehicle load, and route duration constraints. A set of meeting points is defined, and passengers may be asked to use these meeting points as alternative pickup or drop-off points if this results in routes with lower costs. Incorporating meeting points into the DARP is achieved by formulating a mixed-integer linear program. Two preprocessing steps and three valid inequalities are introduced, which improve the computational performance when solving the DARPmp to global optimality. Two versions of the Tabu Search metaheuristic are proposed to approximate the optimal solution in large-scale networks due to the NP-hardness of DARPmp. Performing numerical experiments with benchmark instances, this study demonstrates the benefits of DARPmp compared to DARP in terms of reducing vehicle running costs.

Key Node Identification Method Based on Multilayer Neighbor Node Gravity and Information Entropy

In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE). The method works as follows: First, the relative gravity of the nodes is calculated based on their weights. Second, the direct gravity of the nodes is calculated by considering the attributes of neighboring nodes, thus capturing interactions within local triangular structures. Finally, the centrality of the nodes is obtained by aggregating the relative and direct gravity of multilayer neighbor nodes using information entropy. To validate the effectiveness of the MNNGE method, we conducted experiments on various real-world network datasets, using evaluation metrics such as the susceptible-infected-recovered (SIR) model, Kendall τ correlation coefficient, Jaccard similarity coefficient, monotonicity, and complementary cumulative distribution function. Our results demonstrate that MNNGE can identify key nodes more accurately than other methods, without requiring parameter settings, and is suitable for large-scale complex networks.

Implication of ICWFPSO as Optimization Neural Network Algorithm on Sales Forecasting System

Predictive systems play a crucial role in a company's operations and strategy by aiding in more informed and data-driven decision-making and more effective planning and budgeting. It is possible to develop an intelligent system to perform forecasting. Neural networks offer significant advantages in forecasting systems due to their flexible modeling capabilities. However, this algorithm's fundamental weakness is the slow convergence rate and being trapped in a local minimum. To overcome it, this research is conducted to optimize the NN algorithm using the ICWFPSO to produce a forecasting algorithm with high accuracy and faster execution time using real e-commerce sales data for the past 7 years. Algorithm performance testing tests the Mean Absolute Error (MAE) value of the forecasting system using three scenarios: the NN forecasting algorithm, the NN optimized with ICWFPSO on the weight value, and the same scheme. Still, the optimized value is the hyperparameter value. ICWPSO has been shown to enhance the performance of PSO by tuning the inertia weight dynamically, which helps balance exploration and exploitation during the optimization process. The best prediction result is obtained when optimizing the hyperparameters using the ICWFPSO optimization technique compared to using traditional NN or optimizing weight value with ICWFPSO with the MAE value of 245.32958984375, and the best performance is obtained at iterations below 100. Further, gradient-based optimization methods might be generally preferred for their efficiency and effectiveness in handling large-scale neural network training.

A Coloring-Based Packet Loss Rate Measurement Scheme on Network Nodes

Network measurement is an efficient way to understand network behavior. Traditional measurement techniques focus on internet protocol (IP) networks, where the processing capacity of network nodes is limited and primarily dedicated to packet forwarding. As a result, these techniques typically rely on end hosts or external systems to analyze traffic and evaluate network performance. This reliance introduces several challenges, such as increased measurement latency and scalability limitations, particularly in large-scale networks. With the emergence of next-generation internet architectures, especially information-centric networking (ICN), network nodes have gained enhanced capabilities, enabling measurement tasks to be performed directly at these nodes. This paper proposes a distributed measurement scheme where network nodes collaborate to monitor the packet loss rate on the intermediate link. By setting an unused bit in the packet header, the upstream node “colors” the packets into different color blocks. The minimum duration of each block is determined by the degree of reordering on the link, and the number of packets in each block must be a power of two. The downstream node recognizes blocks, assigns packets to the right block, and deduces the original number of packets for each block to calculate packet loss. Moreover, the upstream node adjusts the number of packets in each block based on the packet transmission rate on the link, aiming to balance measurement accuracy and frequency. A P4-based implementation on a BMv2 software switch is presented to demonstrate the feasibility of the proposed scheme. Simulations show that this scheme improves measurement accuracy and is more robust against packet reordering. Additionally, the proposed scheme maintains relatively low network overhead and, at higher measurement frequencies, exhibits the lowest overhead compared to existing methods.

The influence of neighbours’ supply network structure on firm’s environmental, social and governance controversies

This study investigates how the structural characteristics of a firm's supply network and its neighbouring firms affect their environmental, social, and governance (ESG) controversies. A secondary dataset comprising 18,943 firms and 103,632 contractual links from the global automotive industry was employed to test the hypotheses. Publicly available ESG controversies data for 268 firms were gathered from the Thomson Reuters Eikon database. The results indicated a negative relationship between the interconnectedness of neighbours’ networks and their ESG controversies. The results further revealed a positive association between the centrality of a firm’s neighbours and their ESG controversies. Furthermore, the study highlighted that a firm occupying a bridging position positively moderates the relationship between neighbours’ interconnectedness and ESG controversies. Drawing on a real-world large-scale supply network, our study extends the emerging debate on the criticality of broader supply networks in firms’ sustainability by investigating the role of neighbours’ structural properties in firms’ ESG controversies.

A domain-general process for theta-rhythmic sampling of either environmental information or internally stored information.

Many everyday tasks, such as shopping for groceries, require the sampling of both environmental information and internally stored information. Selective attention involves the preferential processing and sampling of behaviorally important information from the external environment, while working memory involves the preferential processing and sampling of behaviorally important, internally stored information. These essential cognitive processes share neural resources within a large-scale network that includes frontal, parietal, and sensory cortices, and these shared neural resources can lead to between-domain interactions. Previous research has linked external sampling during selective attention and internal sampling during working memory to theta-rhythmic (3-8 Hz) neural activity in higher-order (e.g., frontal cortices) and sensory regions (e.g., visual cortices). Such theta-rhythmic neural activity might help to resolve the competition for shared neural resources by isolating neural activity associated with different functions over time. Here, we used EEG and a dual-task design (i.e., a task that required both external and internal sampling) to directly compare (i) theta-dependent fluctuations in behavioral performance during external sampling with (ii) theta-dependent fluctuations in behavioral performance during internal sampling. Our findings are consistent with a domain-general, theta-rhythmic process for sampling either external information or internal information. We further demonstrate that interactions between external and internal information-specifically, when to-be-detected information matches to-be-remembered information-are not dependent on theta-band activity (i.e., theta phase). Given that these theta-independent 'match effects' occur during early processing stages (peaking at 75 ms), we propose that theta-rhythmic sampling modulates external and internal information during later processing stages.

Functional connectivity profiles in remitted depression and their relation to ruminative thinking

The triple network model suggests that dysfunction in three major brain networks – the default mode network (DMN), central executive network (CEN), and salience network (SN) – might contribute to cognitive impairments in various psychiatric disorders, including major depressive disorder (MDD). While hyperconnectivity in the DMN, hypoconnectivity in the CEN, and abnormal SN connectivity have been observed in acutely depressed patients, evidence for network alterations during remission is limited. Further, there are few studies examining connectivity in people in remission from MDD (rMDD) during emotional processing tasks, including during affective cognition (i.e., tasks that encompass affective processing in the context of cognitive processes, such as inhibition).To address these literature gaps, this study compared functional connectivity (FC) between resting and task conditions, specifically during the emotional Stroop (eStroop) task, as well as between rMDD and healthy volunteers (HVs), within and between nodes of the three networks. We also explored how FC relates to rumination in the rMDD group, given that rumination tends to persist in rMDD and involves affective and cognitive networks.We unexpectedly found greater FC during the task vs. rest condition within the DMN, and decreased FC during the task vs. rest conditions within the CEN and SN across the groups. Greater FC during the task vs. rest condition between DMN and SN nodes, as well as between CEN and SN nodes were also observed. These effects were more pronounced in the rMDD group as per our exploratory analyses. Additionally, the rMDD vs. HV group showed higher FC between DMN-CEN nodes, regardless of condition. Higher hopeless rumination scores were associated with decreased resting FC within the DMN, while higher active problem-solving scores were associated with increased task FC within the DMN in the rMDD group.These findings suggest that tasks engaging affective cognition processes influence FC within and among the three networks, with this effect more pronounced in the rMDD group. This might indicate potential protective and compensatory mechanisms in rMDD and expands our understanding of large-scale intrinsic network connectivity alterations during remission from depression. However, given the limited sample and the exploratory nature of some of our analyses, replication is necessary.

Sex differences in autism spectrum disorder using class imbalance adjusted functional connectivity

Autism spectrum disorder (ASD) is an atypical neurodevelopmental condition with a diagnostic ratio largely differing between male and female participants. Due to the sex imbalance in participants with ASD, we lack an understanding of the differences in connectome organization of the brain between male and female participants with ASD. In this study, we matched the sex ratio using a Gaussian mixture model-based oversampling technique and investigated the differences in functional connectivity between male and female participants with ASD using low-dimensional principal gradients. Between-group comparisons of the gradient values revealed significant interaction effects of sex in the sensorimotor, attention, and default mode networks. The sex-related differences in the gradients were highly associated with higher-order cognitive control processes. Transcriptomic association analysis provided potential biological underpinnings, specifying gene enrichment in the cortex, thalamus, and striatum during development. Finally, the principal gradients were differentially associated with symptom severity of ASD between sexes, highlighting significant effects in female participants with ASD. Our work proposed an oversampling method to mitigate sex imbalance in ASD and observed significant sex-related differences in functional connectome organization. The findings may advance our knowledge about the sex heterogeneity in large-scale brain networks in ASD.