Weighted Degree Centrality Research Articles

Multilayer asset association network, green technical cooperation innovation, and default risk: Evidence from China's high energy-consuming industries

High energy-consuming industries are expected to play a significant role in energy conservation and emissions reduction. This study collects data on China's high energy-consuming listed companies from 2009 to 2022. Using the complex network theory, we construct a multilayer asset association network for high energy-consuming enterprises and examine how different layers and totality affect default risk. The results revealed that node-weighted degree centrality in the asset association networks of high energy-consuming enterprises, particularly in the guarantee association network, significantly increased default risk. Further mechanistic analysis reveals that the level of green technical cooperation innovation mitigates the impact of increased weighted degree centrality on the default risk of high energy-consuming enterprises. For high energy-consuming enterprises located in the eastern region, central region or non-heavily polluting industries, the influence of weighted degree centrality in the asset association network on default risk is more significant. This study enriches complex network research and provides guidance for managing default risk in high energy-consuming enterprises. Additionally, these studies align with global trends toward enhanced asset transparency, sustainability, and financial resilience, and contribute emerging markets to the transition toward more sustainable energy practices.

Who has dominated information spreading on social media during the early stage of COVID-19 pandemic in China? A temporal network analysis

In times of crisis, social media emerges as a pivotal channel for government-public communication. This study delves into the dynamics of a temporal weighted network on Weibo, analyzing 39,567,227 reposted data points related to the COVID-19 pandemic, specifically during its critical early stage. We examine the information dissemination roles of eight distinct user types: state-run news media, non-state-run news media, influential we media, normal we media, celebrities, organizations, governmental departments, and normal users. This is achieved through the application of three centrality metrics: temporal weighted degree centrality (TWDC), temporal weighted closeness centrality (TWCC), and temporal weighted betweenness centrality (TWBC), focusing on three cases (Top 10, Top 20, and Top 50) based on ranking results. Our findings reveal that while state-run news media were prolific in information publishing, their roles as central and mediator agents in information spreading were comparatively weaker than those of non-state-run news media and we media, especially influential we media. This pattern underscores potential risks in false information propagation and highlights gaps in the government's information dissemination strategy during the early stages of the pandemic. The study offers targeted theoretical and practical implications, suggesting how governments might more effectively leverage social media for crisis communication, thereby enhancing their role in combating misinformation and guiding public perception. These insights are crucial for framing more responsive and adaptive communication strategies in the initial phases of a crisis, providing a global blueprint for effective information management in public health emergencies.

Mapping the landscape of e-learning research: a bibliometric analysis

This research conducts a comprehensive bibliometric analysis of academic publications on e-learning, aiming to gain perspectives on the state of the field, and the emergence of patterns within this field of study. A systematic search was conducted in the Scopus database, yielding 1983 documents related to e-learning. The network analysis focused on keyword distribution, country representation, and co-authorship using VOSViewer software and weighted degree centrality. The keyword analysis revealed “e-learning” as the dominant and central theme, highlighting its significance in the research domain. The emergence of “Covid-19” as a widely used keyword for searches implied that the pandemic affected e-learning activities. Moreover, the representation of institutions from diverse countries showcased the global interest in e-learning research, fostering potential cross-cultural collaborations. Additionally, the increasing trend in e-learning research publications over the years indicated the growing importance of digital education. The interdisciplinary nature of e-learning research, spanning Computer Science, Social Sciences, Engineering, and Medicine, underscored the field’s diverse perspectives. Furthermore, the Social Network Analysis unveiled collaborative networks of co-cited authors, regions, and keywords, illuminating the interconnectedness within the e-learning research community. The findings contribute to a better understanding of the e-learning research landscape, facilitating informed decision-making for researchers, educators, policymakers, and institutions. Likewise, the analysis provides valuable insights for future research directions, enabling advancements in e-learning practices and fostering innovation in the digital age.

Evaluating social network metrics as indicators of tail injury caused by tail biting in growing-finishing pigs (Sus scrofa domesticus).

Tail biting is a multifactorial behavior that causes welfare and economic challenges in swine production. As of 2024, research exploring the influence of pig social structure on the development of tail biting is limited. The objective of this study was to explore whether social structures of pigs from different litter origins can impact tail biting and, ultimately, tail damage. Pigs (n = 96) were grouped (eight pigs/pen) based on their litter origin: non-littermates (NLM), half-littermates, and littermates (LM). Tail injury scores were assessed twice weekly from 10 to 24 weeks of age, with a maximal tail injury score (MTS) over the study period being used to evaluate victimization by tail biting. Pig behavior was video-recorded at 15, 19, and 23 weeks of age. Association networks based on lying behavior and tail biting interaction networks were evaluated at pen-and pig-levels using social network analysis. Pigs in LM pens experienced higher median MTS compared to pigs in NLM pens (Median = 1.5; Interquartile range = 1-2; p = 0.009). Within association networks, NLM pens had lower degree centralization measures than other pens at both 15 (Estimated marginal mean [EMM] = 0.07; 95% CI = 0.02-0.12; p = 0.003) and 23 weeks (EMM = 0.09; 95% CI = 0.04-0.14; p = 0.01) and pigs in NLM pens had higher weighted degree centrality than those in other pens (EMM = 1.00; 95% CI = 0.90-1.11; p = 0.002), suggesting pigs in NLM pens had more uniform, stronger, and more connections with their pen-mates. In tail biting networks, increased weighted in-degree centrality was associated with increased odds of pigs receiving a more severe MTS (OR = 1.56; 95% CI = 1.08-2.27; p = 0.02). Pigs with increased weighted out-degree centrality tended to have increased odds of receiving a more severe MTS (OR = 1.19; 95% CI = 0.97-1.48; p = 0.09). These preliminary data suggest a potential relationship between social structures and tail biting in growing-finishing pigs.

Influence of Intercity Network on Land Comprehensive Carrying Capacity: A Perspective of Population Flow

The world is experiencing the largest wave of urban growth in history. Maintaining the rapid growth of cities without causing land and resource shortages is a severe problem that must be solved urgently. With the rapid development of globalization and information technology, the meaning of land comprehensive carrying capacity presents new changes. It is no longer entirely dependent on local resources and is likely to benefit from intercity connections beyond urban boundaries. However, can an inter-city network be a non-local solution to sustain urban growth without increasing land pressure? To address this question, this study adopted 287 cities in China as the research object to describe the spatial carrying characteristics of land at the national level by constructing an evaluation index system for land comprehensive carrying capacity. Furthermore, we constructed a population flow network model through social network analysis to explore the influence of intercity network on land comprehensive carrying capacity. Our findings are as follows: (1) The regional differentiation characteristics of land comprehensive carrying capacity at the national scale are evident, and reveal a spatial pattern significantly related to the urban economic development level. (2) The weighted in-degree, weighted degree centrality, and betweenness centrality in the intercity network positively impact the land comprehensive carrying capacity, and land use efficiency has a partial mediating effect. (3) Land comprehensive carrying capacity can be determined by non-local factors rather than local factors. As an effective non-local channel, strengthening intercity population flow and network integration can flexibly manage urban land scarcity.

How to determine city hierarchies and spatial structure of a megaregion?

ABSTRACT Megaregion has emerged as a global urban form, typically based on the polycentric strategy to enhance regional development. How to measure megaregional spatial structure and discriminate different roles of cities has become increasingly important to enrich the knowledge of the formation of a megaregion. Meanwhile, various indices have been used to identify vital nodes in the field of complex network. Which indices, however, are suitable for megaregion analysis remain unsolved. To address this requirement, this study first reviewed the typical indices for identifying vital nodes in the complex network theory, and pointed out that in a weighted city network scenario, weighted degree centrality, hub & authority score, and S-core decomposition (which represent network centrality, connectivity, and structures, respectively) are suitable for analyzing megaregional spatial structures. Then, we explored the city hierarchies and spatial structure in Guangdong Province, China, using the three indices. The hierarchical structure of the weighted city network in Guangdong Province had been identified using S-core decomposition. From the perspective of polycentric structure, Guangzhou and Shenzhen have the strongest node degrees and strength of mobility flows, while the Guangzhou-Dongguan-Shenzhen corridor has been identified via the hub & authority score which is designed to evaluate the connectivity in a weighted network. Moreover, we conducted a comparison analysis of three indices. The findings of this study not only enrich the understanding of city hierarchies and the structure of a megaregion, but also highlight that although various indices are available, they should be applied selectively in accordance with the study context.

Association between Pregestational Vaginal Dysbiosis and Incident Hypertensive Disorders of Pregnancy Risk: a Nested Case-Control Study.

A balanced vaginal microbiome dominated by Lactobacillus can help promote women's reproductive health, with Lactobacillus crispatus showing the most beneficial effect. However, the potential role of vaginal microbiomes in hypertensive disorders of pregnancy (HDP) development is not thoroughly explored. In this nested case-control study based on an assisted reproductive technology follow-up cohort, we prospectively assessed the association between pregestational vaginal microbiomes with HDP by collecting vaginal swabs from 75 HDP cases (HDP group) and 150 controls (NP group) and using 16S amplicon sequencing for bacterial identification. The vaginal microbial composition of the HDP group significantly differed from that of the NP group. The abundance of L. crispatus was significantly lower, and the abundances of Gardnerella vaginalis was significantly higher, in the HDP group than in the NP group. Of note, L. crispatus-dominated vaginal community state type was associated with a decreased risk for HDP (odds ratio = 0.436; 95% confidence interval, 0.229 to 0.831) compared with others. Additionally, network analysis revealed different bacterial interactions with 61 and 57 exclusive edges in the NP and HDP groups, respectively. Compared with the HDP group, the NP group showed a higher weighted degree and closeness centrality. Several taxa, including G. vaginalis, L. iners, and bacterial vaginosis-associated bacteria (Prevotella, Megasphaera, Finegoldia, and Porphyromonas), were identified as "drivers" for network rewiring. Notable alterations of predicted pathways involved in amino acid, cofactor, and vitamin metabolism; membrane transport; and bacterial toxins were observed in the HDP group. IMPORTANCE The etiology of HDP remains unclear to date. Effective methods for the individualized prediction and prevention are lacking. Pregestational vaginal dysbiosis precedes the diagnosis of HDP, providing a novel perspective on the etiology of HDP. Early pregnancy is the critical period of placental development, and abnormal placentation initiates HDP development. Thus, disease prevention should be considered before pregnancy. Vaginal microbiome characterization and probiotic interventions before pregnancy are preferred because of their safety and potential for early prevention. This study is the first to prospectively assess associations between pregestational vaginal microbiome and HDP. L. crispatus-dominated vaginal community state type is linked to a reduced risk for HDP. These findings suggest that vaginal microbiome characterization may help identify individuals at high risk for HDP and offer potential targets for the development of novel pregestational intervention methods.

A Port Importance Evaluation Method Based on the Projection Pursuit Model in Shipping Networks

Shipping networks face natural or man-made port emergencies, and the failure of a port affects the network’s connectivity and efficiency. It is very necessary to give priority to the selection of ports that should be maintained or ensure service capacity in case of port failure. The importance evaluation of ports is of great significance to improve the efficiency of maritime transport. In view of this, this paper proposed a port importance evaluation method in shipping networks integrating the centrality index and vulnerability index. The indexes are, respectively, degree centrality, weighted degree centrality, betweenness centrality, closeness centrality, change rate of network efficiency, and connectivity. The weight of each index is calculated by the projection pursuit model. The results show that the proposed method integrates the different performances of each index. The importance of Singapore port, Colombo port, and Port Klang rank as the top three. They are the hub ports of the main lines of Asia, Europe, and Africa and occupy extremely important core positions in the network. Finally, the ports are classified based on importance value, and the shipping network after the failure of some ports is compared. This research can provide a scientific basis for ensuring the efficiency, connectivity, and stability of shipping networks.

How did academic collaboration occur in research on STEM attitudes and careers?

This study aims to examine the co-authorship relationships established between researchers working on STEM career interests, STEM professions, and STEM attitudes by creating a social network. For this purpose, the articles scanned in the web of science index between 1983 and 2023 were filtered. After the filtering process, a total of 6371 articles constituted the sample of this research. In our study, the relationships established between 10989 authors, 237 universities, and 121 countries in the co-authorship network were visualized and existing connections were modeled. As a result of the collaborations between the authors in the network, prominent communities were found with the Louvain algorithm. Other network metrics such as weighted degree centrality, closeness degree centrality, and betweenness degree centrality were calculated and visualized using Gephi software. The findings obtained as a result of the analysis revealed the characteristic patterns of scientific collaborations in the social network, prominent authors in the field, universities, and countries.
 Keywords: Social Network Analyses; STEM Career; STEM Occupation; Stem Vocation.

Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions.

Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-level and graph-level network features are extracted from this network, e.g., weighted degree centrality, weighted PageRanks, etc. After concatenating the network features to the original drug features, they were fed into seven machine learning models, e.g., logistic regression, random forest, support vector machine, etc., and were compared to the baseline, where there were no network-based features considered. These experiments indicate that all the tested machine-learning methods would benefit from adding these network features. Among all these models, logistic regression (LR) had the highest mean AUROC score (82.1%) across all ADRs tested. Weighted degree centrality and weighted PageRanks were identified to be the most critical network features in the LR classifier. These pieces of evidence strongly indicate that the network approach can be vital in future ADR prediction, and this network-based approach could also be applied to other health informatics datasets.

REDE DE PROPRIEDADE CORPORATIVA: CAPITALISMO HIERÁRQUICO NO CONTEXTO BRASILEIRO

ABSTRACT Several studies have indicated a type of hierarchical capitalism in Latin America, characterized by the presence of multinational and state corporations, diversified multi-enterprise groups, low-skilled labor, and segmented labor markets. From the perspective of ownership networks, we explore who the owners of large Brazilian corporations are. These networks reveal actors and groups with prominent positions in the structure and ownership of companies. Thus, this article proposes to 1) describe the ownership network of the largest Brazilian corporations; 2) analyze the density, average degree, modularity, and the number of components connected in this network; 3) analyze measures of weighted degree centrality, intermediation, and eigenvector; 4) identify and analyze subgroups in the network, and 5) relate the configuration of the ownership network with hierarchical capitalism in the Brazilian context. The results point to prominent positions of financial shareholders, the Brazilian state (state-owned firms), and groups composed mostly of large multinationals, financial firms, families, and state-owned firms, confirming that the ownership structure is strongly linked to these players.

The Construction and Application of E-Learning Curricula Evaluation Metrics for Competency-Based Teacher Professional Development

Today, students at universities in advanced countries typically enroll in colleges, such as the College of Education, which offer interdisciplinary programs for undergraduates in their first and second years, allowing them to explore personal interests, experience educational research fields, complete their integrated curricula, and then choose a major in their third year. To cooperate with the government’s epidemic prevention policies and measures in the post-COVID-19 era, the trend of e-learning and distance teaching has accelerated the establishment of integrated online curricula with interdisciplinary programs for undergraduates in the College of Education to facilitate effective future teacher professional development (TPD). Therefore, it is very important to construct e-learning curricula evaluation metrics for competency-based teacher professional development (CB-TPD) and to implement them in teaching practice. This research used social network analysis (SNA) methods, approaches, and theoretical concepts, such as affiliation networks and bipartite graphs comprised of educational occupational titles and common professional competencies (i.e., Element Name and ID), as well as knowledge, skills, abilities, and other characteristics (KSAOs), from the U.S. occupational information network (O*NET) 26.1 OnLine database, to collect data on the occupations of educational professionals. This study also used Gephi network analysis and visualization software to carry out descriptive statistics of keyword co-occurrences to measure their centrality metrics, including weighted degree centrality, degree centrality, betweenness centrality, and closeness centrality, and to verify their importance and ranking in professional competency in eight categories of educational professionals (i.e., three categories of special education teachers and five categories of teachers, except special education). The analysis of the centrality metrics identified the educational common professional competency (ECPC) keyword co-occurrences, which were then used to design, develop, and apply e-learning curricula evaluation metrics for CB-TPD. The results of this study can be used as a reference for conducting related academic research and cultivating educational professionals’ online curricula, including ECPC keywords, integrated curricula design and the development of transdisciplinary programs, and teacher education, as well as to facilitate the construction and application of future e-learning curricula evaluation metrics for CB-TPD.

A novel heterogeneous preference fusion method with dual interaction influence and dynamic risk spread

Preference fusion is crucial for multi-attribute group decision-making (MAGDM). Heterogeneous preferences and interactions are commonplace situations in real-world MAGDM. Unlike conventional MAGDM fusion methods ignoring interactions or only considering attribute interactions, a novel heterogeneous preference fusion method proposed in this study considers the dual interaction influence of attributes and alternatives, as well as the influence of the dynamic risk spread (IDRS). Specifically, a novel similarity-based three-dimensional determination–uncertainty space method is established to homogenize heterogeneous preferences, avoiding information distortion caused by preference uncertainty loss. The attribute interaction influence is evaluated by constructing an optimization model with ranking maximization. Subsequently, a multi-attribute alternative interaction network and a total interaction measurement model are developed to describe complex interaction relationships and the interaction intensity between alternatives. The susceptible-infected-recovered-susceptible Markov chain model is presented to measure the IDRS. An improved directed weighted degree centrality model based on the IDRS is then established to measure the alternative interaction influence (AII) from a global perspective. Finally, a novel interaction non-additive fusion method is designed to fuse preference more reasonably. A case study of hot dry rock siting and comparative analyses demonstrate the practicality, robustness, and superiority of the proposed method. The results show that the AII and IDRS significantly influence the fusion accuracy and should be considered in MAGDM fusion methods.

A network pharmacology-based approach to explore mechanism of action of medicinal herbs for alopecia treatment

Hair loss is one of the most common skin problems experienced by more than half of the world's population. In East Asia, medicinal herbs have been used widely in clinical practice to treat hair loss. Recent studies, including systematic literature reviews, indicate that medicinal herbs may demonstrate potential effects for hair loss treatment. In a previous study, we identified medical herbs used frequently for alopecia treatment. Herein, we explored the potential novel therapeutic mechanisms of 20 vital medicinal herbs for alopecia treatment that could distinguish them from known mechanisms of conventional drugs using network pharmacology analysis methods. We determined the herb-ingredient–target protein networks and ingredient-associated protein (gene)-associated pathway networks and calculated the weighted degree centrality to define the strength of the connections. Data showed that 20 vital medicinal herbs could exert therapeutic effects on alopecia mainly mediated via regulation of various target genes and proteins, including acetylcholinesterase (AChE), phospholipase A2 (PLA2) subtypes, ecto-5-nucleotidase (NTE5), folate receptor (FR), nicotinamide N-methyltransferase (NNMT), and quinolinate phosphoribosyltransferase (QPRT). Findings regarding target genes/proteins and pathways of medicinal herbs associated with alopecia treatment offer insights for further research to better understand the pathogenesis and therapeutic mechanism of medicinal herbs for alopecia treatment with traditional herbal medicine.

Does the high‐speed rail network improve economic growth?

AbstractThis paper discusses whether high‐speed rail (HSR) construction in China promotes urban economic growth, and it uses social network analysis (SNA) and a panel threshold model. We provide robust evidence that HSR networks have non‐linear effects on the urban economy. Urban location and transportation hub status change as the density of HSR networks increases, and from 2008 to 2017, the national average annual growth rate of the weighted degree centrality (WDC) was 44.93%. We find the WDC and betweenness centrality (BC) have positive influences on urban economic growth. However, HSR network expansion reduces the economic benefits of HSR because the marginal contribution of new HSR lines to economic productivity decreases and there is a crowding out effect caused by excessive agglomeration. HSR networks can promote the economy of Western China to a greater extent and can promote the economy more in small cities than in large and medium‐sized cities. These findings can help the government rationalize its strategy for designing HSR lines in various regions.

Mining Proteome Research Reports: A Bird's Eye View.

The complexity of data has burgeoned to such an extent that scientists of every realm are encountering the incessant challenge of data management. Modern-day analytical approaches with the help of free source tools and programming languages have facilitated access to the context of the various domains as well as specific works reported. Here, with this article, an attempt has been made to provide a systematic analysis of all the available reports at PubMed on Proteome using text mining. The work is comprised of scientometrics as well as information extraction to provide the publication trends as well as frequent keywords, bioconcepts and most importantly gene–gene co-occurrence network. Out of 33,028 PMIDs collected initially, the segregation of 24,350 articles under 28 Medical Subject Headings (MeSH) was analyzed and plotted. Keyword link network and density visualizations were provided for the top 1000 frequent Mesh keywords. PubTator was used, and 322,026 bioconcepts were able to extracted under 10 classes (such as Gene, Disease, CellLine, etc.). Co-occurrence networks were constructed for PMID-bioconcept as well as bioconcept–bioconcept associations. Further, for creation of subnetwork with respect to gene–gene co-occurrence, a total of 11,100 unique genes participated with mTOR and AKT showing the highest (64) number of connections. The gene p53 was the most popular one in the network in accordance with both the degree and weighted degree centrality, which were 425 and 1414, respectively. The present piece of study is an amalgam of bibliometrics and scientific data mining methods looking deeper into the whole scale analysis of available literature on proteome.

Combining centrality indices: Maximizing the predictability of keystone species in food webs

Network analysis offers a rich toolkit to study various graph models in biology. In ecology, centrality indices have been suggested to indicate keystone species in interaction networks and to quantify their importance in an ecosystem. There is a large number of centrality indices, however, and it is often unclear what is their precise biological meaning, how are they related to each other and which one is the “best” predicting the functioning of the modelled biological system. It is a major challenge to use simple structural indicators in order to predict the outcome of much more complicated dynamical simulations. The question is which one is the most preidictive one and what is the meaning of particular structural indices. Here we use machine learning techniques to combine k centrality indices out of n in such a way that the gained combined index (a “cocktail” of single indices) correlates better with simulated dynamics. In particular, we are interested in rank correlations between single-node and multi-node centrality and simulated node importance. We identify index families based on similarity. The best single-index correlations (weighted degree centrality) can predict simulated food web dynamics with an accuracy up to 70.06%. This accuracy can be raised reasonably, using the best cocktail, up to 78.42%. This is a combination of node degree (D) and 5-step-long weighted importance index (WI5). Since they have completely different properties (the former is local and binary, the latter is meso-scale and weighted), we can demonstrate that a good cocktail has to combine indices from different families in order to best improve predictions. If one needs to predict dynamics from structure, there is a way to use wise proxies of simple topological indices – instead of performing complicated simulations.

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

Transport networks are essential for societies. Their proper operation has to be preserved to face any perturbation or disruption. It is therefore of paramount importance that the modeling and quantification of the resilience of such networks are addressed to ensure an acceptable level of service even in the presence of disruptions. The paper aims at characterizing network resilience through weighted degree centrality. To do so, a real dataset issued from probe vehicle data is used to weight the graph by the traffic load. In particular, a set of disrupted situations retrieved from the study dataset is analyzed to quantify the impact on network operations. Results demonstrate the ability of the proposed metrics to capture traffic dynamics as well as their utility for quantifying the resilience of the network. The proposed methodology combines different metrics from the complex networks theory (i.e., heterogeneity, density, and symmetry) computed on temporal and weighted graphs. Time-varying traffic conditions and disruptions are analyzed by providing relevant insights on the network states via three-dimensional maps.

International Cooperation Among Artificial Intelligence Research Teams Based on Regional Cooperation Models

The paper explores the regional cooperation model and the differences among artificial intelligence research teams. It is helpful to reveal the status and strategies of scientific cooperation models across regions or within regions. We identified the world of artificial intelligence research teams with co-authorship network, and then identified the leading team based on the Number of Publications, Number of Citations, H-index, Weighted Degree Centrality, Betweenness Centrality, and Closeness Centrality. Based on the identified artificial intelligence research leading teams, this paper divides different types of cooperation models by region and comprehensively analyzed the three aspects of geographical distribution, cooperation indicators, and cooperation topics in the research teams from the perspective of comparisons. In order to find the international gap between China and other countries, we still highlight the difference between China's participation and non-participation in cooperation. The research results show that Chinese and their foreign research maintain close ties with major scientific research countries; international cooperation is widespread and is conducive to crossing into the leading team; China's domestic cooperation is higher than in other countries, and their domestic cooperation research is mainly manifested in the data processing and application level, while the core technology and basic algorithm levels need to cooperate with foreign countries.

Transcutaneous Spinal Cord Direct-Current Stimulation Modulates Functional Activity and Integration in Idiopathic Restless Legs Syndrome.

Idiopathic restless legs syndrome (RLS) is a sensorimotor disorder and is suggested to be caused by central nervous system abnormalities. Non-invasive transcutaneous spinal direct-current stimulation (tsDCS) was recently used for RLS therapy. However, the neurophysiological basis of tsDCS treatment is still unknown. In this study, we explored the neural basis of tsDCS in 15 RLS patients and 20 gender- and age-matched healthy controls using resting-state functional magnetic resonance imaging. We calculated the whole-brain voxel-wise fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and weighted degree centrality (DC) to characterize the intrinsic functional activities and the local and global functional integration. We found that tsDCS can effectively improve the sleep and RLS symptoms in RLS patients. Moreover, after tsDCS therapy, the RLS patients showed decreased fALFF in the right anterior insula/temporal pole, decreased ReHo in the supplementary motor area, increased weighted DC in the left primary visual cortex, and decreased weighted DC in the right posterior cerebellum. The changed patterns were consistent with that found between RLS patients and healthy controls. The weighted DC in the left primary visual cortex after treatment and the fALFF in the right anterior insula/temporal pole before treatment were significantly and marginally correlated with sleep and RLS symptom scores, respectively. These results revealed that tsDCS can normalize the functional patterns of RLS patients and is an effective way for RLS therapy. Our findings provide the neurophysiological basis for tsDCS treatment and may facilitate understanding the neuropathology of RLS and directing other neuromodulation treatments.