Node Strength Research Articles

Age-Related Alterations in EEG Network Connectivity in Healthy Aging.

Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

EEG based functional brain networks analysis in dyslexic children during arithmetic task.

Developmental Dyslexia is a neuro-developmental disorder that often refers to a phonological processing deficit regardless of average IQ. The present study investigated the distinct functional changes in brain networks of dyslexic children during arithmetic task performance using an electroencephalogram. Fifteen dyslexic children and fifteen normally developing children (NDC) were recruited and performed an arithmetic task. Brain functional network measures such as node strength, clustering coefficient, characteristic pathlength and small-world were calculated using graph theory methods for both groups. Task performance showed significantly less performance accuracy in dyslexics against NDC. The neural findings showed increased connectivity in the delta band and reduced connectivity in theta, alpha, and beta band at temporoparietal, and prefrontal regions in dyslexic group while performing the task. The node strengths were found to be significantly high in delta band (T3, O1, F8 regions) and low in theta (T5, P3, Pz regions), beta (Pz) and gamma band (T4 and prefrontal regions) during the task in dyslexics compared to the NDC. The clustering coefficient was found to be significantly low in the dyslexic group (theta and alpha band) and characteristic pathlength was found to be significantly high in the dyslexic group (theta and alpha band) compared to the NDC group while performing task. In conclusion, the present study shows evidence for poor fact-retrieval mechanism and altered network topology in dyslexic brain networks during arithmetic task performance.

The architecture of psychological well-being: A network analysis study of the Ryff Psychological Well-Being Scale.

The proliferation of mental health research is orienting its efforts towards the exploration of psychological well-being. One of the main burdens is the measurement challenges reported by the widely used Psychological Well-Being Scale (PWBS), which has often been criticized for inconsistencies between the theoretical and the empirical model. A potential alternative to understand the structure of psychological well-being is network models, which conceptualizes psychological phenomena as emerging systems of mutually connected variables. Employing exploratory graph analysis, we examined the network structure of the Spanish 29-item PWBS in a random sample of 1,404 adults. We estimated a regularized partial correlation network using the graphical LASSO algorithm in the item and dimension level. We tested the stability of both networks and identified the most important variables of the network. The PWBS network model revealed four dimensions, with self-acceptance, life purpose and environmental mastery clustering together. Node strength centrality suggested that self-acceptance is the most central dimension in the psychological well-being structure as measured by the PWBS. Despite the network model of psychological well-being did not replicate the theoretical structure of Ryff's model, it provides a novel conceptualization of psychological well-being and proposes target indicators for mental health interventions.

Difference in topological organization of white matter structural connectome between methamphetamine and heroin use disorder

The psychological symptoms caused by heroin and methamphetamine are significantly different in people with substance use disorders. The topological organization of structural connections that may underlie these differences remains unknown. The study sample consisted of 23 males with methamphetamine use disorder (MAUD), 20 males with heroin use disorder (HUD), and 21 male healthy controls (HCs) who were demographically matched. Diffusion tensor imaging and probabilistic tractography were used for white matter network construction. Psychological symptoms were evaluated by the Symptom Checklist-90. Using graph theoretical analysis, we examined the difference in graph-level and nodal-level properties among the groups. The network Hubs distribution and the relationship between the network alterations and psychological symptoms were identified. The MAUD group demonstrated significantly higher scores on anxiety, hostility, and symptoms of schizophrenia than the HUD and HCs groups. The HUD group showed significantly higher global efficiency and network strength than the HCs group, and higher network strength than the MAUD group. Compared with the HUD group, the MAUD group showed significantly lower Nodal Strength and efficiency, distributed mainly in the temporal, parietal, and occipital regions. We also found the network Hubs were decreased in the MAUD group, but increased in the HUD group. The Nodal Strength in the right superior temporal gyrus was significantly correlated with psychological symptoms in the MAUD group. These findings reflect the significant differences in topological structural connection between HUD and MAUD. This evidence helps shed some light on the neurobiological mechanisms of the psychological differences between HUD and MAUD, and extend our understanding of the structural disruption underlying MAUD-related psychological symptoms.

Functional connectivity in Parkinson\u2019s disease candidates for deep brain stimulation

This study aimed to identify functional neuroimaging patterns anticipating the clinical indication for deep brain stimulation (DBS) in patients with Parkinson’s disease (PD). A cohort of prospectively recruited patients with PD underwent neurological evaluations and resting-state functional MRI (RS-fMRI) at baseline and annually for 4 years. Patients were divided into two groups: 19 patients eligible for DBS over the follow-up and 41 patients who did not meet the criteria to undergo DBS. Patients selected as candidates for DBS did not undergo surgery at this stage. Sixty age- and sex-matched healthy controls performed baseline evaluations. Graph analysis and connectomics assessed global and local topological network properties and regional functional connectivity at baseline and at each time point. At baseline, network analysis showed a higher mean nodal strength, local efficiency, and clustering coefficient of the occipital areas in candidates for DBS over time relative to controls and patients not eligible for DBS. The occipital hyperconnectivity pattern was confirmed by regional analysis. At baseline, a decreased functional connectivity between basal ganglia and sensorimotor/frontal networks was found in candidates for DBS compared to patients not eligible for surgery. In the longitudinal analysis, patient candidate for DBS showed a progressively decreased topological brain organization and functional connectivity, mainly in the posterior brain networks, and a progressively increased connectivity of basal ganglia network compared to non-candidates for DBS. RS-fMRI may support the clinical indication to DBS and could be useful in predicting which patients would be eligible for DBS in the earlier stages of PD.

Electrophysiological connectivity markers of preserved language functions in post-stroke aphasia

Post-stroke aphasia is a consequence of localized stroke-related damage as well as global disturbances in a highly interactive and bilaterally-distributed language network. Aphasia is increasingly accepted as a network disorder and it should be treated as such when examining the reorganization and recovery mechanisms after stroke. In the current study, we sought to investigate reorganized patterns of electrophysiological connectivity, derived from resting-state magnetoencephalography (rsMEG), in post-stroke chronic (>6 months after onset) aphasia. We implemented amplitude envelope correlations (AEC), a metric of connectivity commonly used to describe slower aspects of interregional communication in resting-state electrophysiological data. The main focus was on identifying the oscillatory frequency bands and frequency-specific spatial topology of connections associated with preserved language abilities after stroke.RsMEG was recorded for 5 min in 21 chronic stroke survivors with aphasia and in 20 matched healthy controls. Source-level MEG activity was reconstructed and summarized within 72 atlas-defined brain regions (or nodes). A 72 × 72 leakage-corrected connectivity (of AEC) matrix was obtained for frequencies from theta to low-gamma (4–50 Hz). Connectivity was compared between groups, and, the correlations between connectivity and subscale scores from the Western Aphasia Battery (WAB) were evaluated in the stroke group, using partial least squares analyses. Posthoc multiple regression analyses were also conducted on a graph theory measure of node strengths, derived from significant connectivity results, to control for node-wise properties (local spectral power and lesion sizes) and demographic and stroke-related variables.Connectivity among the left hemisphere regions, i.e. those ipsilateral to the stroke lesion, was greatly reduced in stroke survivors with aphasia compared to matched healthy controls in the alpha (8–13 Hz; p = 0.011) and beta (15–30 Hz; p = 0.001) bands. The spatial topology of hypoconnectivity in the alpha vs. beta bands was distinct, revealing a greater involvement of ventral frontal, temporal and parietal areas in alpha, and dorsal frontal and parietal areas in beta. The node strengths from alpha and beta group differences remained significant after controlling for nodal spectral power. AEC correlations with WAB subscales of object naming and fluency were significant. Greater alpha connectivity was associated with better naming performance (p = 0.045), and greater connectivity in both the alpha (p = 0.033) and beta (p = 0.007) bands was associated with better speech fluency performance. The spatial topology was distinct between these frequency bands. The node strengths remained significant after controlling for age, time post stroke onset, nodal spectral power and nodal lesion sizes.Our findings provide important insights into the electrophysiological connectivity profiles (frequency and spatial topology) potentially underpinning preserved language abilities in stroke survivors with aphasia.

Construction of Optimal Membership Functions for a Fuzzy Routing Scheme in Opportunistic Mobile Networks

This article proposes FRIMF, a fuzzy routing scheme for opportunistic mobile networks (OMNs). In FRIMF, we exploit the pairwise intercontact times to evaluate the connection strength between nodes. Instead of assuming a random movement model, in the present case we consider node contact processes in OMNs as bursty events. Consequently, we introduce a burstiness parameter to characterize the variability in the dynamics of pairwise interactions. This variance metric, along with the statistical mean of pairwise intercontact times, is used to define a single FRIMF routing metric called closeness through a fuzzy inference system. This reflects the tie strength of the pair nodes. To improve the transmission environment, we further propose a method to develop optimal membership functions for the FRIMF’s fuzzy parameters based on the contact information. Particularly, we leverage the membership function elicitation techniques commonly used in collective opinion aggregations based on a direct rating process to establish the relevancy between vagueness estimates of the routing parameters and statistical distributions of the pairwise intercontact times in a way that eventually presents asymmetric triangular fuzzy numbers. In turn, these TFNs are used to properly define the fuzzy sets of the FRIMF’s routing parameters. Through simulations in the real human mobility environments, we show that FRIMF utilizing the enhanced asymmetric TFNs can outperform that using the typical symmetric TFNs developed based on our subjective preferences. Lastly, comparing with several algorithm benchmarks, we confirm the efficiency of FRIMF in transmission cost and delay.

Review of carbon fiber-reinforced sandwich structures

This paper first reviews the research progress of carbon fiber-reinforced sandwich plates with grid cores, truss cores, and foam cores, and the results can be summarized in the following four points. 1) The load-bearing capacity and energy absorption capacity of grid-core and foam-core structures under bending loads have been improved by toughening with short fibers and Z-pinned fibers. 2) The progressive buckling mode of the core layer under compressive loading can significantly improve the energy absorption capacity of a sandwich structure. 3) The compressive failure of a truss-core sandwich structure is closely related to the strength at the nodes. 4) Following a literature review, this paper discusses the existing problems in sandwich structures and the corresponding countermeasures, and the results can be summarized in the following two points. 1) After implementing the current toughening measures, the debonding is improved to a certain extent; however, this approach does not prevent debonding. 2) No method has been presented that can increase the strength of the end nodes in truss-core members. Therefore, it is encouraging that a trabeculae/honeycomb-core sandwich structure composed of fiber-reinforced proteins—mimicking the biological structure found in a beetle forewing—can effectively solve the abovementioned problems.

Cascading failures on complex networks with weak interdependency groups

In real complex systems, the overall function is maintained through the connections among nodes. Failures of some nodes may destroy the connectivity of the system and thus damage the function of the system. In some complex systems, some nodes can form “interdependency groups” through hidden interdependency. The failure of one node may damage the rest of the nodes in the interdependency group. In this paper, we investigate the effects of the interdependency strength of the nodes, the size distribution, and the size of the interdependency groups on the cascading dynamics and the robustness of complex networks. Through numerical simulation and theoretical analysis, it is found that the cascading failures of the networks can be divided into two processes at a scale level: “intra-group cascading” and “inter-group cascading”. In the intra-group cascading process, the failure of one node will result in damage to the other nodes in the group through the interdependence among nodes, thus inducing more nodes to be unworkable and resulting in greater destructive force. In the inter-group cascading process, the failed nodes will cause the networks to be fragmented, which leads some nodes outside the interdependency group to isolate from the giant component and go to failure. Under the synergistic effects of these two processes, it is found that there are continuous and discontinuous phase transition phenomena in the cascade dynamics of the network. The occurrence of these two kinds of phase transition phenomena is related to the interdependency strength of nodes, the network degree distribution and the size distribution of the interdependency group. This means that by controlling the characteristics of interdependency groups, such as the interdependence strength of the nodes in the interdependency group or the size distribution of interdependency groups, the system can avoid collapsing suddenly and thus the robustness of the network can be improved.

Cascading failures on complex networks with weak interdependency groups

In real complex systems, the overall function is maintained through the connections among nodes. Failures of some nodes will destroy the connectivity of the system and thus damage the function of the system. In some complex systems, some nodes can form ``interdependency groups" through hidden interdependencies, and the failure of one node will damage the rest of the nodes in the interdependency group. In this paper, we investigate the effects of the interdependency strength of the nodes, the size distribution and the size of the interdependency groups on the cascading dynamics and the robustness of complex networks. Through numerical simulation and theoretical analysis, it is found that the cascading failure of the networks can be divided into two processes at the scale level: ``intra-group cascading" and ``inter-group cascading". In the process of intra-group cascading, the failure of one node will cause damage to the failure of other nodes in the group through the dependence between nodes, thus inducing the failure of more nodes and resulting in greater destructive force. In the process of intra-group cascading, the failure of one node will cause damage to other nodes in the group through the interdependence between nodes, thus inducing the failure of more nodes and resulting in greater destructive power. In the process of inter-group cascading, the failed nodes will cause the fragmentation of the networks, which leads to some nodes outside the interdependency group isolate from the giant component and go to fail. Under the synergistic effects of these two processes, it is found that there are continuous and discontinuous phase transition phenomena in the cascade dynamics of the network. The occurrence of these two kinds of phase transition phenomena is related to the interdependency strength of nodes, the network degree distribution and the size distribution of the interdependence group. This means that by controlling the characteristics of interdependency groups, such as the interdependence strength of the nodes in the interdependency group or the size distribution of interdependency groups, sudden system collapse can be avoided and thus the robustness of the network can be improved.

种间互作对雷州湾海域鱼类群落结构的影响

PDF HTML阅读 XML下载 导出引用 引用提醒 种间互作对雷州湾海域鱼类群落结构的影响 DOI: 10.5846/stxb202108212330 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划"蓝色粮仓科技创新"重点专项资助（2020YFD0901103）；南方海洋科学与工程广东省实验室（湛江）资助项目（ZJW-2019-06） Importance of species interactions in structuring fish community of Leizhou Bay waters, northern South China Sea Author: Affiliation: Fund Project: The National Basic Research Program of China (973 Program),其他 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:从鱼类种间互作及其生态网络特征视角研究鱼类群落结构特征，对解析多鱼种共存机制和基于群落水平的多鱼种资源管理均具有重要意义。为分析鱼类资源利用的竞争互作对群落结构稳定性的影响，根据2016-2018年8个航次的雷州湾海域底拖网调查的鱼类数据，将空间生态位重叠理论和生态网络建模相结合，从种间互作的角度应用加权聚类系数C、加权平均路径长度A、关联度Co等指标分析鱼类的群落结构。结果表明：①鱼类生态位重叠所形成的种间互作网络季节变动较大，而年度变动则相对较小，共包含鱼类58-122种，种间互作的关系数量1318-7103。②雷州湾海域鱼类群落内种间互作密集，聚合程度高（C介于0.84-0.98）且能量传递效率高（A介于0.02-0.09），呈现互作弱化之趋势（Co介于0.32-0.48），群落结构总体较稳定。③小尺度范围（20°40'-21°00'N，110°30'-110°45'E）内，鱼类群落内种间互作特征是维持多种鱼类共存的重要内因。该特征表现为季节和年度的变化总体上相似，种间直接关联程度与种间互作强度呈正相关（P<0.01），而局部聚合程度与种间直接关联、互作强度均呈负相关（P<0.01）。本研究可为鱼类群落结构稳定性的量化评价和资源养护的管理规划提供参考依据。 Abstract:The interactions between fish species and their roles in the ecological network on structuring the fish community is a matter of great concern not only for understanding the species coexistence, but also for the multispecies fishery management and conservation at the community level. The data from the bottom trawl surveys of eight cruises in Leizhou Bay waters northern South China Sea during 2016 and 2018 were applied to analyze how the species interactions (interspecific competition in limited resources and space) affected the stability of fish community structure in seasons and years. Combining theories on spatial niche overlap and the ecological network modeling, six indicators such as the weighted clustering coefficient (C), weighted average path length (A), connectance (Co), degree (K), weighted node strength (Si), and locally weighted clustering coefficient (Ci) were respectively applied to measure the degree of community aggregation, the overall energy transfer efficiencies between species, interspecific connectivity, the degree of directly interspecific associations, the intensity of species interactions, and the degree of local aggregation between species. And then we analyzed the importance of species interactions in structuring fish community of Leizhou Bay waters. The results showed that:①the network of species interaction which formed by spatial niche overlap of fishes varied more widely in seasons, but comparatively minor between years, including 58-122 kinds of fishes, and 1318-7103 lines (links between species interactions). ②The fish community was characterized with the intensive interactions, high degrees of aggregation (C ranged between 0.84 and 0.98), high energy transfer efficiencies (A ranged between 0.02 and 0.09), with a weakening tendency of species interactions (Co ranged between 0.32 and 0.48) from 2016 to 2018. The fish community of Leizhou Bay waters was kept in a state of stability in all. ③At the local small scale (20°40'-21°00'N, 110°30'-110°45'E), traits of species interactions of the fish community were similar on the seasonal and annual scale. Two traits were the important internal factors driving the coexistence of multiple fish species in the Leizhou Bay waters. The first was the degree of directly interspecific associations which was positively correlated with the intensity of species interactions (P<0.01). And the second was the degree of local aggregation which was negatively correlated with both the degree of directly interspecific associations and the intensity of species interactions (P<0.01). This study provides clues for the quantitative analysis on the stability of fish community structure and planning of effective coastal fishery resources management and conservation. 参考文献 相似文献 引证文献

Variations of Resting-State EEG-Based Functional Networks in Brain Maturation From Early Childhood to Adolescence.

Background and PurposeAlterations in human brain functional networks with maturation have been explored extensively in numerous electroencephalography (EEG) and functional magnetic resonance imaging studies. It is known that the age-related changes in the functional networks occurring prior to adulthood deviate from ordinary trajectories of network-based brain maturation across the adult lifespan.MethodsThis study investigated the longitudinal evolution of resting-state EEG-based functional networks from early childhood to adolescence among 212 pediatric patients (age 12.2±3.5 years, range 4.4–17.9) in 6 frequency bands using 8 types of functional connectivity measures in the amplitude, frequency, and phase domains.ResultsElectrophysiological aspects of network-based pediatric brain maturation were characterized by increases in both functional segregation and integration up to middle adolescence. EEG oscillations in the upper alpha band reflected the age-related increases in mean node strengths and mean clustering coefficients and a decrease in the characteristic path lengths better than did those in the other frequency bands, especially for the phase-domain functional connectivity. The frequency-band-specific age-related changes in the global network metrics were influenced more by volume-conduction effects than by the domain specificity of the functional connectivity measures.ConclusionsWe believe that this is the first study to reveal EEG-based functional network properties during preadult brain maturation based on various functional connectivity measures. The findings potentially have clinical applications in the diagnosis and treatment of age-related brain disorders.

Dynamic Correlation Analysis on the Financial Institutions in Shanghai, Shenzhen, and Hong Kong Stock Markets Based on Complex Network

With the launch of the Shanghai-Hong Kong and the Shenzhen-Hong Kong Stock Connect programs, the correlations among the three stock markets have been more complex. Therefore, this paper applies the Minimum Spanning Tree and Sliding Window methods to analyze the complex correlations of the 38 financial institutions in the three markets. Based on closing prices ranging from January 1, 2011 to December 31, 2021, three conclusions are obtained. The top 20 institutions in the network were mainly banking institutions from 2011 to 2013, while the node strength of non-banking financial institutions tended to increase from 2014 to 2021. The network shows different structures over time, even the changes in the three correlation coefficients generally show the same trend. The degree correlation between any two layers is positive, but the similarity shows different characteristics. These results would help to enrich multi-layer network theory and reveal the evolutionary characteristics of the correlation between financial institutions.

The effects of digital financial inclusion on innovation and entrepreneurship: A network perspective

&lt;abstract&gt; &lt;p&gt;The development of urban digital inclusive finance has a complex network connection, and the characteristics of this network affect urban innovation and entrepreneurship. By using 287 cities from 2015 to 2019 in China, this paper constructs the network of Chinese digital inclusive finance, extracting two typical network characteristics. And it further studies the impact of the two network characteristics on innovation and entrepreneurship in various cities. The empirical results show that: first, the network centrality characteristics of digital inclusive finance have a significant promoting effect on urban innovation and entrepreneurship; second, the impact of the network centrality characteristics of digital inclusive finance is related to the speed of industrial structure transformation, but the impact mechanism is related to the node number instead of the node strength; third, the impact of the network centrality characteristics on innovation and entrepreneurship exists heterogeneity among cities and the moderately developed cities have the largest impact.&lt;/p&gt; &lt;/abstract&gt;

A network perspective on marital satisfaction among older couples

It will be helpful for older people to maintain good mental health by improving their marital satisfaction. The present study investigates how the elements of marital satisfaction among older couples are related to each other and reveal the key elements. Four hundred ninety-four older people participated in the study. Marital satisfaction was assessed by the 10-item marital satisfaction subscale of the ENRICH scale. Network analysis was adopted to estimate the network structure of these 10 items and the strength centrality of each item was calculated. The results showed that all edges in the final network were positive. Four edges with the strongest regularized partial correlations appeared between “ leisure activities and spending time together” and “ emotional expression”; “ personality and habits” and “ communication and understanding”; “ economic status and the manner of determining economic affairs” and “ relationship with relatives and friends”; and “ make decisions and resolve conflict” and “ emotional expression.” In addition, “ emotional expression” had the highest node strength value in the network. “ Communication and understanding” and “ views are consistent” had the second and third highest node strength values, respectively. “ Views are consistent” was more central in males than females and was the second central node in male networks. In conclusion, the present study offers a new perspective to deepen the understanding about the internal structure of marital satisfaction among older couples via network analysis. The results might provide potential targets of intervention for social workers or family therapists to greatly improve marital satisfaction among older couples.

Economic Indicators and Climate Change for BRICS Economies in the Post–COVID-19 World: Neural Network Approach

Climate change has emerged as one of the challenges of the global economy. Climate change economics has focused on the economic aspects of climate tradeoff. Studies have been conducted on the causal association of economic indicators and climate change indicators. However, for the sample of BRICS countries, that are important participants of global climate change, no study has attempted to identify whether causal connections apply to them. The study is an endeavor to identify the underlying causal connections between economic indicators and carbon emissions for BRICS economies. Six economic indicators, current account balance, inflation, foreign direct investment inflows, gross domestic product, real effective exchange rate, and trade openness, are selected for the sample period 2005–2019. Neural network analysis as a method of computational economics is applied for the superior methodology over standard statistical techniques. The outcome suggests that for BRICS economies, economic indicators have a significant relationship with carbon emissions, differing in intensity as per node strength of the neural network.

Knowledge acquisition for 3D coordinates of target in wireless sensor networks for smart city application

AbstractIn wireless sensor networks, non‐linear distance information characterized by uncertainty and randomness creates the deviation and significantly target nodes position is affected. Non‐linearity effect among distance and received signal strength (RSS) of target nodes is minimized by modelling edge weights using knowledge base in fuzzy logic system. Further, optimum membership functions bases' of RSS and edge weights using application of butterfly optimization algorithm are evolved to minimize the node position error. Here, a single anchor node is used to estimate target nodes 3D coordinates in anisotropic environment using range free localization methods. The anchor node has been deployed at top layer and over beneath layers target nodes are distributed equally. In This paper, simulation results of the proposed method attain substantial performance improvement in target node 3D position accuracy than the earlier proposed range‐free methods. Proposed technique is useful for mapping of several instances like, fire hazards in forests, tracking of workers at different installation sites, solar plant tracking in smart cities and so forth.

Anxiety and depressive symptoms in college students during the late stage of the COVID-19 outbreak: a network approach

Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms “Nervousness” and “Uncontrollable worry”. “Fatigue” has the highest node strength in the anxiety and depression network, followed by “Excessive worry”, “Trouble relaxing”, and “Uncontrollable worry”. “Motor” showed the highest bridge strength, followed by “Feeling afraid” and “Restlessness”. The whole network was robust in both stability and accuracy tests. Central symptoms “Fatigue”, “Excessive worry”, “Trouble relaxing” and “Uncontrollable worry”, and critical bridge symptoms “Motor”, “Feeling afraid” and “Restlessness” were highlighted in this study. Targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.

Network analysis of psychological factors related to academic pressure faced by medical students in the central and highland regions of Vietnam

ABSTRACT Medical students experience extensive pressure during their undergraduate courses. Given the complex associations between psychological factors in association with academic pressure. We investigated the study with objectives: To examine psychological factors related to academic pressure by analysing interactions between ‘study motivation’, ‘study environment’, ‘study conditions’, ‘teacher quality’, ‘training programme’, ‘management system’, ‘evaluation’, and ‘extracurricular activities’ using a network analysis approach. A total of 878 medical students majoring in general medicine from the first, third, and fifth years of a six-year course at the largest medical university in central and highland regions of Vietnam were involved in this cross-sectional study. The approach used was convenient cluster sampling with a self-administered questionnaire by the participants. Network analysis for pairwise correlations between psychological factors was estimated . Important factors in the network analysis were calculated using centrality indices including node strength (S), closeness (C), and betweenness (B). The higher score of S, C, and B indicate the more importance of the node. The results obtained from the network analysis of eight psychological factors showed that ‘teaching quality’ was mostly connected with other factors overall, while the ‘training programme’ was seen in both genders and freshman students. ‘Study conditions’ and ‘training programme’ were mostly connected with other factors in junior and senior students, respectively. The strong pairwise correlation was confirmed: management system and evaluation activity, followed by study environment and study conditions, and teaching quality and training programme. Additionally, nodes with high centrality were shown to be ‘management system’ (S = 0.97, C = 0.019, B = 1), and ‘training programme’ (S = 0.96, C = 0.021, B = 4). Our study findings indicate that satisfaction with the training programme amongst eight psychological factors is the most important factor affecting academic pressure among medical students. The training programme is linked with teaching quality, whereas the management system is correlated with evaluation activity.

Neuropeptide Y Variation Is Associated With Altered Static and Dynamic Functional Connectivity of the Salience Network.

Neuropeptide Y (NPY) is a neurotransmitter that has been implicated in the development of anxiety and mood disorders. Low levels of NPY have been associated with risk for these disorders, and high levels with resilience. Anxiety and depression are associated with altered intrinsic functional connectivity of brain networks, but the effect of NPY on functional connectivity is not known. Here, we test the hypothesis that individual differences in NPY expression affect resting functional connectivity of the default mode and salience networks. We evaluated static connectivity using graph theoretical techniques and dynamic connectivity with Leading Eigenvector Dynamics Analysis (LEiDA). To increase our power of detecting NPY effects, we genotyped 221 individuals and identified 29 healthy subjects at the extremes of genetically predicted NPY expression (12 high, 17 low). Static connectivity analysis revealed that lower levels of NPY were associated with shorter path lengths, higher global efficiency, higher clustering, higher small-worldness, and average higher node strength within the salience network, whereas subjects with high NPY expression displayed higher modularity and node eccentricity within the salience network. Dynamic connectivity analysis showed that the salience network of low-NPY subjects spent more time in a highly coordinated state relative to high-NPY subjects, and the salience network of high-NPY subjects switched between states more frequently. No group differences were found for static or dynamic connectivity of the default mode network. These findings suggest that genetically driven individual differences in NPY expression influence risk of mood and anxiety disorders by altering the intrinsic functional connectivity of the salience network.