Centrality Analysis Research Articles

Spontaneous changes in brain network centrality in patients with pathological myopia: A voxel-wise degree centrality analysis.

Myopia has become a worldwide problem that endangers public health and adds a serious socioeconomic burden. Current research has focused on the pathogenesis and manifestations of pathological myopia (PM). However, few studies have been conducted on the spontaneous activity of the patient's brain. To study the potential brain network activity in patients with PM by the degree centrality (DC) method. This experiment included 15 PM patients and 15 healthy controls (HCs). Every participant experienced a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Receiver operating characteristic (ROC) curve analysis was used to distinguish between PM patients and HCs. Correlation analysis was used to explore the relationships between mean DC values and clinical performance in different brain regions. It showed that patients with PM had lower DC values in the right fusiform gyrus (FR) and right cingulate (CAR). The ROC curve was used to indicate the accuracy of the correlation. It showed that in PM group, left best corrected visual acuity (BCVA-L) and right best corrected visual acuity (BCVA-R) were negatively correlated with the DC value of FR. The occurrence of PM is mainly related to the abnormal activity of the fusiform and cingulum. DC value might be used as a biological marker of abnormal brain activity in PM patients.

Evaluation of Japanese port policies through network analysis

Although the Japanese government has implemented port reforms since the 1990s in response to declining international competitiveness of Japan's container ports, Japanese ports have continued to decline, at least in terms of containerized cargo handling volume. However, since the relative share of port handling volumes would naturally decline with the growth in other emerging Asian economies, it may be unfair to evaluate Japan's port policies or international presence solely in terms of cargo handling volume. This study evaluates Japan's port policies based on centrality analysis of Keihin and Hanshin Ports, which are the targets of the policies, in the context of global maritime container network. The results of the network analysis reveal that the rankings of Keihin and Hanshin in each centrality index have declined while major ports in Asia have consistently ranked high in the global marine container transport network. However, the increased importance of the Asian major ports in the network, combined with the strengthening of links of both ports to other Asian major ports, does not result in a decline in the presence of Keihin and Hanshin in the network as much as a decline in the ranking of cargo handling volume. In addition, the three scenario analyses confirm that Keihin and Hanshin would be further enhanced if the policy goal of strengthening domestic hub functions is fully achieved, and that a policy shift to dependence on hub ports in Asia for transshipment functions would ensure their presence on the network. In addition, as Japanese ports are highly dependent on Pusan, they are extremely vulnerable to the malfunction of Pusan in terms of network structure; thus, making Keihin and Hanshin function as domestic hubs is crucial to address this problem. Moreover, considering the vulnerability in the network structure in addition to the centrality index helps evaluate the reasonableness of Japan's port policies.

The alterations of brain network degree centrality in patients with neovascular glaucoma: a resting-state fMRI study.

To explore the alterations of whole brain functional network using the degree centrality (DC) analysis in neovascular glaucoma (NVG) and the correlation between DC values and NVG clinical indices. Twenty NVG patients and twenty normal controls (NC), closely matched in age, sex, and education, were recruited for this study. All subjects underwent comprehensive ophthalmologic examinations and a resting-state functional magnetic resonance imaging (rs-fMRI) scan. The differences in DC values of brain network between NVG and NC groups were analyzed, and correlation analysis was performed to explore the relationships between DC values and clinical ophthalmological indices in NVG group. Compared with NC group, significantly decreased DC values were found in the left superior occipital gyrus and left postcentral gyrus, while significantly increased DC values in the right anterior cingulate gyrus and left medial frontal gyrus in NVG group. (All P < 0.05, FDR corrected). In the NVG group, the DC value in left superior occipital gyrus showed significantly positive correlations with retinal nerve fiber layer (RNFL) thickness (R = 0.484, P = 0.031) and mean deviation of visual field (MDVF) (R = 0.678, P = 0.001). Meanwhile, the DC value in the left medial frontal gyrus demonstrated significantly negative correlations with RNFL (R = - 0.544, P = 0.013) and MDVF (R = - 0.481, P = 0.032). NVG exhibited decreased network degree centrality in visual and sensorimotor brain regions and increased degree centrality in cognitive-emotional processing brain region. Additionally, the DC alterations might be complementary imaging biomarkers to assess disease severity.

The Institutional Social Role for Maritim Village’s Food Stability

This study aims to explore social networks and interactions between networks in strengthening food stability in maritime villages in coastal areas. The geographical condition of the region has become one of the triggers for food instability and food security problems in coastal areas. This study uses the method of Social Network Analysis (SNA). Data processing uses R to visualize the role of stakeholder centrality. The analysis used is the analysis of baseline network, degree, and betweenness centrality. The value of modularity is an indicator of increasing or decreasing community networks. The number of nodes and links identifies the actors in the network and the interactions between actors. The results showed that social networks have an essential role in food stability. The private sector is a key actor in food stability in maritime villages. Is indicated by the modularity value of 0.43 (>0), which connects to 30 social network nodes and 62 links to food stability in maritime villages in the coastal area of Bintan Regency.

Exploring the Latent Mechanism of Huanglian Jiedu Decoction Formula for Anti-atopic Dermatitis by Systems Pharmacology.

Atopic dermatitis (AD) is a chronic inflammatory skin disease, which does not have a specific drug presently. Huanglian jiedu decoction (HJD) is one of the effective traditional Chinese medicine prescriptions. The real material and mechanisms of HJD for AD are not clear. Network pharmacology and in vivo experiments were used to explore the real material and mechanisms of HJD for AD. A systems' pharmacology approach that provides a comprehensive analysis of bioactive compounds, targets, and pathway interactions was employed to elucidate the molecular pathogenesis of HJD for AD. First, the compound databases were constructed for HJD, and compound targets were predicted. Then, the hub targets of HJD were selected by degree centrality analysis and validated using the molecular docking method. Finally, Compound-Target and Target-Pathway networks were constructed to explore the latent mechanism of HJD for AD. Then, animal models of AD were established, the pathology of the skin lesions was observed, and RT-PCR and ELISA methods were used to verify the key targets in the serum of AD mice. The results showed that 60 bioactive compounds (palmatine, wogonin, cavidine, etc.) of HJD interacting with 169 related hub targets (PTGS2, HSP90AA1, etc.) were authenticated. HJD potentially participates in response to stimuli, biological regulation, and reproduction through the PI3K-Akt signaling pathway, MAPK signaling pathway, Ras signaling pathway, and Fc epsilon RI signaling pathway, which are interrelated to the pathogenesis of AD. Compared with the control group, the thickening of the epidermis in the model group was obvious with inflammatory cells infiltrating, the levels of PI3K, AKT, JNK, ERK, IL-4 and TNF-α were up-regulated; and 6.4g/kg and 12.8g/kg HJD could significantly reduce the thickening of the epidermis and infiltration of inflammatory cells, down-regulate the levels of PI3K, AKT, JNK, ERK, IL-4 and TNF-α in the AD mice. HJD might exert its anti-AD effects by downregulating key indicators (PI3K, AKT, JNK, ERK, IL-4, and TNF-α) in the PI3K/AKT and MAPK pathways. Our study could help us understand the compound and mechanism of HJD for AD. Moreover, it had a guidance function to change the traditional arrangement of formula for HJD.

Transcriptome profiling for developmental stages Protaetia brevitarsis seulensis with focus on wing development and metamorphosis.

A white-spotted flower chafer Protaetia brevitarsis seulensis widely distributed in Asian countries is traditionally used in oriental medicine. This study explored gene expression abundance with respect to wing development and metamorphosis in P. b. seulensis based on the large-scale RNA-seq data. The transcriptome assembly consists of 23,551 high-quality transcripts which are approximately 96.7% covered. We found 265 wing development genes, 19 metamorphosis genes, and 1,314 candidates. Of the 1,598 genes, 1,594 are included exclusively in cluster 4 with similar gene co-expression patterns. The network centrality analyses showed that wing development- and metamorphosis-related genes have a high degree of betweenness centrality and are expressed most highly in eggs, moderately in pupa and adults, and lowest in larva. This study provides some meaningful clues for elucidating the genetic modulation mechanism of wing development and metamorphosis in P. b. seulensis.

소셜 네트워크 분석을 활용한 액티브 시니어 관광객의 관광 공간 행동 조사

Purpose: This study was to investigate tourism spatial behavior based on the characteristics of the connection relationship among the spaces they visited by active senior tourists visiting Daegu Metropolitan City. It could provide a basic data to enhance the tourism revitalization strategy for active senior tourists by understanding their tourism space and movement behavior.&#x0D; Methods: This study used social network analysis to analyze the tourism space networking system, which is included the order of the tourist spaces visited by active senior tourists. Using UCINET 6.733 software program, it utilized density analysis, normalized in-degree centrality, normalized out-degree centrality, and normalized betweenness centrality analysis.&#x0D; Results: The results of density analysis disclose that Mt. Palgong cable car -&gt; Mt. Palgon Donghwasa is the most common type of the visiting tourism spatial behavior at once for active senior tourists. Daegu arboretum was the 1st ranking for the result of normalized in-degree centrality analysis, Seomun Night Market was the 1st ranking for the result of normalized out-degree centrality analysis. And, Seomun Night Market was the 1st ranking for the result of normalized betweenness centrality analysis.&#x0D; Conclusion: This study could identify the characteristics between the tourism spaces where active senior tourists visited, in addition tourism spatial behavior for active senior tourists who visited Daegu Metropolitan City. It also confirmed that active senior tourists have their own tourism spatial behavior. These results are expected to enable an in-depth understanding of the tourism spatial behavior for active senior tourists.

전라북도 젊은 층 인구의 정주의향 영향요인 분석

The purpose of this study is to explore and analyze factors that may affect the improvement of the settlement will of the young population in local small and medium-sized cities. Specifically, this study limited the scope of the study to “the young population aged 25 to 49 living in Jeollabuk-do” based on the fact that the population outflow of local small and medium-sized cities occurs mainly, and collected a total of 570 short-answer data using Macromill Embrain's online panel. Subsequently, word frequency analysis, centrality analysis, and social network analysis were conducted through a data preprocessing process for the collected subjective response data. As a result of the analysis, it was found that physical environmental factors in the region were important for the settlement intention of the young population in local small and medium-sized cities. In particular, among the physical environmental factors, the centrality index for convenience facilities, jobs, and natural environment was relatively high. Therefore, local small and medium-sized cities facing the crisis of population decline due to the departure of the resident population today will need to prioritize the aforementioned factors and make efforts to improve them. On the other hand, the fact that this study collected data using open questions and conducted language network analysis centered on them can be seen as an important contribution academically compared to the fact that most existing studies rely only on structured questionnaire items.

1인 가구의 성별에 따른 배달 음식 소비 행태에 관한 연구

A study was conducted using the raw data of the ‘2021 Food Consumption Behavior Survey’ to investigate the characteristics of each female and male consumer by comparing the selection factors when using delivery food for single-personal households. This study compared and analyzed the differences between female and male consumers using social network analysis. First, through density analysis and centrality analysis, a selection criterion network that is considered when choosing delivery food for a single-person housholds was established with female and male consumers, respectively. And the difference was compared using Bootstrap Paired Sample T-test. As a result of the analysis, it was confirmed that the delivery food selection factors of single-person households were different for male and female consumers. In addition, male consumers found that ‘delivery/food preparation speed’ was the most important selection factor, but female consumers used delivery food by considering various selection factors in a complex way.Based on the research results, we would like to present implications so that they can be used as practical data in establishing management strategies and policies for entrepreneurs and companies that want to enter the food delivery market in the future.

On the centrality analysis of covert networks using games with externalities

The identification of the most potentially hazardous agents in a terrorist organisation helps to prevent further attacks by effectively allocating surveillance resources and destabilising the covert network to which they belong. In this paper, several mechanisms for the overall ranking of covert networks members in a general framework are addressed based on their contribution to the overall relative effectiveness in the event of a merger. In addition, the possible organisation of agents outside of each possible merger naturally influences their relative effectiveness score, which motivates the innovative use of games in partition function form and specific ranking indices for individuals. Finally, we apply these methods to analyse the effectiveness of the hijackers of the covert network supporting the 9/11 attacks.

Integrated twitter analysis to distinguish systems thinkers at various levels: a case study of COVID-19

Systems Thinking (ST) has become essential for practitioners and experts when dealing with turbulent and complex environments. Twitter medium harbors social capital including systems thinkers, however there are limited studies available in the extant literature that investigate how experts' systems thinking skills, if possible at all, can be revealed within Twitter analysis. This study aims to reveal systems thinking levels of experts from their Twitter accounts represented as a network. Unraveling of latent Twitter network clusters ensues the centrality analysis of their follower networks inferred in terms of systems thinking dimensions. COVID-19 emerges as a relevant case study to investigate the relationship between COVID-19 experts’ Twitter network and their systems thinking capabilities. A sample of 55 trusted expert Twitter accounts related to COVID-19 has been selected for the current study based on the lists from Forbes, Fortune, and Bustle. The Twitter network has been constructed based on the features extracted from their Twitter accounts. Community detection reveals three distinct groups of experts. In order to relate system thinking qualities to each group, systems thinking dimensions are matched with the follower network characteristics such as node-level metrics and centrality measures including degree, betweenness, closeness and Eigen centrality. Comparison of the 55 expert follower network characteristics elucidates three clusters with significant differences in centrality scores and node-level metrics. The clusters with a higher, medium, lower scores can be classified as Twitter accounts of Holistic thinkers, Middle thinkers, and Reductionist thinkers, respectfully. In conclusion, systems thinking capabilities are traced through unique network patterns in relation to the follower network characteristics associated with systems thinking dimensions.

국내 산학협력 양상 연구

Objectives This study aims to review the aspects of domestic industry-academic cooperation by keyword network analysis of web data from 2018 to 2021, focusing on impacts of each performer-government, university, industry-on the industry-academia cooperation(IAC) system before and after the COVID-19 phenomenon.&#x0D; Methods Keyword network, keyword centrality, ego network, and topic modelling analysis were conducted to examine 1) each influence from the government, university, industry on IAC system, 2) changes its influence after the COVID-19 phenomenon, and 3) the degree to IAC policies were reflected in the aspects of IAC, analysing 5,000 documents collected from web news and blogs.&#x0D; Results As a result of keyword network analysis, university, industry, and support project were found to be the top frequent keywords and also at the center of keywords, showing the characteristics of IAC that industry and universities focus on jointly designing and operating government-supported IAC programs. Analysis of ego networks shows that since COVID-19, industry and government networks have grown in size, while recruitment and field practice networks have decreased in size. Keywords such as job creation, SME support, and new industrial talent training, which are major government industry-academic cooperation policies during the text collection period, were partially derived as major areas in topic modeling analysis, but showed different patterns before and after COVID-19.&#x0D; Conclusions Government, university, industry were found to be the key players in determining the aspects of the IAC system, and the most influential subject was university over the past four years, actively cooperating with companies in the educational field through government-led development projects. The government's IAC policy for job creation was also reflected in the IAC aspect, but it seems to be limited by the COVID-19 factor. Implications for the role of government, university, and industry in sustaining IAC are discussed.

A general higher-order supracentrality framework based on motifs of temporal networks and multiplex networks

As the dynamics and complexity of networks evolve, identifying the important nodes in temporal networks (i.e., the relationships change over time) and multiplex networks (i.e., the layers can encode different types of relationships) becomes a core topic in network analysis and data science. However, the existing researches merely focus on the first-order relationships (i.e., the interactions only lie in two nodes) regardless of the higher-order relationships (i.e., the interactions exist among multiple nodes) especially for intralayer node interactions, which has been proved to be significant to affect centralities of nodes in the temporal networks and multiplex networks. Motivated by this, by observing and analyzing the interaction patterns of nodes in the temporal networks and multiplex networks, and considering the importance of triadic closure, first we adopt triangular motifs, and reconstruct the real-world system as temporal networks or multiplex networks fused with higher-order motifs information to encode the interactions among multiple nodes. Then we design a higher-order supracentrality framework to mine critical nodes with higher-order features in temporal networks and multiplex networks. The framework we proposed can characterize how the importance of nodes changes over time or different relationships, and also illustrate a complicated dependency of the higher-order supracentrality on the network topology, tunable weights of interlayer coupling and layer teleportation probability. To demonstrate the effectiveness and superiority of higher-order supracentrality framework based on motifs, we provide theoretical analysis and sufficient experiments on two empirical datasets including temporal networks and multiplex networks, the experimental results show that the higher-order supracentrality framework can identify much more abundant important nodes accurately. More significantly, our framework can provide a unifying foundation for centrality analysis in the temporal networks and multiplex networks, and also serve as a guidance for practical applications.

NETWORK ANALYSIS OF VERBAL COMMUNICATIONS IN THE NOVEL THE MASTER AND MARGARITA BY M. A. BULGAKOV

A network analysis of the structure of verbal communications in one of the most popular Russian novels of the Soviet era The Master and Margarita by M. A. Bulgakov has been carried out. The structure of the novel is complex, i.e. there is “a story within a story”. Moreover, the real-world-characters and the other-world-characters are interacting in the novel. This complex and unusual composition makes the novel especially attractive for a network analysis. In our study, only paired verbal communications (conversations) between explicitly present and acting characters have been taken into account; frontal communications, monologues, off-stage characters as well as expected connections between characters have not been taken into account. Based on a character pair verbal communication matrix, a graph has been constructed, the vertices of which are the characters of the novel, while the edges correspond to the connections between them. Taking only paired verbal communications into account leads to the result that the character network can be described by an ordinary, rather than a directed graph. Since the activity of the conversations was out of our intended scope, the edges have been given no weights. The largest connected component of the graph consists of 76 characters. Centralities, such as degree, betweenness, closeness, eigenvector, and assortativity coefficient were computed to characterize the network. The assortativity coefficient of the network under consideration is negative [Formula: see text], i.e. the network does not demonstrate the properties of a social network. The structure of the communities in the network was also analyzed. In addition to the obvious large communities — the characters from the Yershalaim part of the novel and the characters of the Moscow part — the analysis also revealed a fine structure in the Moscow component. Using the analysis of centralities, a group of main characters has been detected. The central characters of the novel are Koroviev, Margarita, Bezdomny, Woland, Behemoth, Azazello, Bosoi, Warenukha, Master, and Levi Matthew.

Noncanonical electromechanical coupling paths in the cardiac hERG channel.

The human ether-a-go-go-related gene (hERG) codes for a Kv channel involved in the delayed rectifier current that determines the plateau period of the cardiac action potential. During the cardiac action potential, this channel rapidly recovers from inactivation, to cease the action potential. hERG displays marked inward rectification, a mechanism due to an extremely fast pore inactivation and shown not to be related to the activation process. Moreover, hERG operates seamlessly without a covalent interaction between the voltage sensor (VSD) and the pore (PD), suggesting the presence of an alternative communication between VSD and PD, without the S4-S5 linker. Such mechanism has been shown in Shaker K+ channel, a domain-swapped channel, but it is yet to be determined in non-domain-swapped channels, like hERG. Here, we used a combination of mutagenesis and molecular dynamics simulations/network analysis to identify a chain of residues coupling the VSD to PD that involves S4∴S1 and S1∴S5 subunit interfaces. The role of each residue (T425, P426, A527, H562, W563, A565, W568, A614, Y616, F617, and T618) in the identified noncanonical paths was quantified by performing betweenness centrality analysis and tested using electrophysiological assays combined with leucine-scanning mutagenesis. To provide unequivocal evidence of the noncanonical path and the agreement between the computational prediction and experimental data, we plotted the betweenness centrality and the minimal distance of each mutant along the noncanonical paths against the free-energy perturbation of activation and of inactivation. These plots showed a good agreement between computational and experimental results, demonstrating that the residues identified are part of the noncanonical electromechanical coupling in hERG. Altogether, our work identifies the presence of allosteric, noncanonical gating paths in the hERG channel and shed new light on the elusive activation and inactivation mechanisms of non-domain-swapped channels. Support: NIH-GM030376, ERC:803213.

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.

A Study on Research Trends in 『Audiology and Speech Research』 Using Keyword Network Analysis

Purpose: The purpose of this study was to investigate the research trends of papers published in ‘Audiology and Speech Research’ and to obtain implications for the direction of research.Methods: Data were collected from 2005 to 2022 articles research information sharing service (RISS) which is provided by Korea education and Korea Citation Index (KCI). After extracting and cleansing keywords, the frequency, centrality, and visualization data analysis for keywords were performed using KrKwic &amp; NodeXL programs.Results: According to the results of keyword frequency analysis, ‘hearing aid’ showed the highest frequency, followed by ‘hearing loss’, ‘noise’, and ‘cochlear implant’, etc. As a result of the keyword centrality analysis, it was found that the centrality was particularly high in ‘hearing aid’, ‘hearing loss’, ‘word recognition’, ‘cochlear implant’, ‘sensorineural hearing loss’, ‘auditory training’, ‘aural rehabilitaion’.Conclusion: The results of this study will provide useful data for understanding current research trends in ASR and further suggesting new research directions in the field of audiology and speech pathology.

Research Trends in English Writing: A Semantic Network Analysis

This study explores the shifting landscape of English writing research from 2001 to 2020, utilizing a large-scale, data-driven methodology. Data from 1495 articles published in Korea over these two decades was collected and preprocessed with the Biblio data collector, then analyzed with Netminer. The approach involved four stages of semantic analysis: frequency, centrality, network, and modularity analyses. The findings highlight dynamic shifts in research focus. While keywords like ‘test’, ‘college students’, ‘vocabulary’, and ‘level-based’ remained consistent, the 2010s saw emerging themes like ‘task’, ‘textbook analysis’, ‘corpus analysis,’ 'peer feedback’, and ‘genre-based approach’. Centrality analysis showed that in the 2000s, alongside ‘sentence’, ‘questionnaire’ held a central position with multiple nodes linked to it. In the 2010s, ‘questionnaire’ persisted as a central theme but was joined by ‘relationship.’ Network maps generated with Spring 2D and PFnet depicted these evolving interconnections. In the second period, ‘feedback’ emerged as a central theme, yet directly connected to only two nodes: ‘error’ and ‘peer feedback.’ Modularity analysis identified six research groups in each period, with the ‘questionnaire group’ being most significant in the 2000s and the ‘peer feedback group’ gaining prominence in the 2010s. This research illuminates the evolving trends in English writing research, underscoring the potential of big data-driven approaches to uncover key insights and patterns.

Centrality and connectivity analysis of the European airports: a weighted complex network approach

ABSTRACT This study aims to reveal the structure of the European Airport Network (EAN) using concepts from complex network theory by utilising 2019 passenger data collected from Eurostat. Initially, the EAN was explored by computing connectivity and centrality measures and their correlations. The community structure of the EAN was also examined by modularity maximisation using the relatively new Leiden algorithm. When the network was compared with simulated models, it was observed that the EAN had small-world and scale-free properties. To measure the hub performance of the airports, their binary betweenness centrality was compared with a weighted betweenness measure employing the ratio of geographical distance to passenger traffic between the nodes. A significant difference was observed between the two centrality measures.

Alterations of degree centrality and functional connectivity in classic trigeminal neuralgia.

Recent neuroimaging studies have indicated a wide range of structural and regional functional alterations in patients with classic trigeminal neuralgia (CTN). However, few studies have focused on the intrinsic functional characteristics of network organization in the whole brain. Therefore, the present study aimed to characterize the potential intrinsic dysconnectivity pattern of the whole brain functional networks at the voxel level using the degree centrality (DC) analysis in CTN patients. Thirty-four patients with CTN and twenty-nine well-matched healthy controls (HCs) participated in this study. All subjects underwent resting-state functional magnetic resonance imaging (rs-MRI) examination and clinical and neuropsychologic assessments. DC is a graph theory-based measurement that represents the overall functional connectivity (FC) numbers between one voxel and other brain voxels. We first investigated brain regions exhibiting abnormal DC, and further identified their perturbation on FC with other brain regions using a seed-based FC analysis in patients with CTN. In addition, correlation analyses were performed to evaluate the relationship between the abnormal DC value and clinical variables in CTN patients. Compared with the HCs, the patients with CTN exhibited significantly greater DC values in the right pallidum and right putamen, and lower DC values in the right lingual gyrus, right calcarine sulcus, left paracentral lobule, and left midcingulate cortex. A further seed-based FC analysis revealed that the right lingual gyrus showed decreased FC within the visual network and with other core brain networks, including the sensorimotor network, default mode network, and salience network, relative to HCs. Additionally, the left midcingulate cortex exhibited decreased FC within the middle cingulate cortex and the visual network in CTN patients. Moreover, the DC value in the left midcingulate cortex was negatively correlated with the illness duration. The present study shows that CTN patients exhibited specific functional connectivity network alterations in the basal ganglia, visual network, and salience network, which may reflect the aberrant neural network communication in pain processing and modulation. These findings may provide novel insight for understanding the mechanisms of pain chronicity in CTN patients.