Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,instit...Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,institutional cooperation network analysis,author collaboration network analysis,keyword co-occurrence,and emergent words analysis are drawn.Combined with the literature content analysis,four hot spots in the research field of landscape architecture microclimate in China are obtained,namely ENVI-MET,comfort,design strategy and urban green space.The research trend is thermal comfort,human body comfort and winter city.The research results can provide reference for the research on domestic garden microclimate.展开更多
Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters...Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.展开更多
This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be use...This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be used to assess the impact of a tree planting design’s spatial configuration on an urban park’s visual fields.Trees play an important role in determining the spatial characteristics of an outdoor space.According to space syntax theory,an urban area is a collection of connected spaces that can be represented by a matrix of quantitative properties known as syntactic measures.Computer simulations can be used to measure the quantitative properties of these matrices.This study uses space syntax techniques to assess how tree configurations and garden area which can affect the social structures of small-scale gardens in Port Said.It also looks at how these techniques can be used to predict the social structures of four garden zones in El Sallam Garden.The study includes an observational and space syntax study through comparative analysis of four garden zones in El Sallam garden.The results of the study show that the area and planting configurations of the garden had a significant effect on the syntactic social and visual measures of the urban garden.The conclusions and recommendations can be a useful tool for landscape architects,urban planners,and legislators who want to enhance public areas and encourage social interaction in urban settings.展开更多
Limit equilibrium method （LEM） and strength reduction method （SRM） are the most widely used methods for slope stability analysis. However, it can be noted that they both have some limitations in practical applicat...Limit equilibrium method （LEM） and strength reduction method （SRM） are the most widely used methods for slope stability analysis. However, it can be noted that they both have some limitations in practical application. In the LEM, the constitutive model cannot be considered and many assumptions are needed between slices of soil/rock. The SRM requires iterative calculations and does not give the slip surface directly. A method for slope stability analysis based on the graph theory is recently developed to directly calculate the minimum safety factor and potential critical slip surface according to the stress results of numerical simulation. The method is based on current stress state and can overcome the disadvantages mentioned above in the two traditional methods. The influences of edge generation and mesh geometry on the position of slip surface and the safety factor of slope are studied, in which a new method for edge generation is proposed, and reasonable mesh size is suggested. The results of benchmark examples and a rock slope show good accuracy and efficiency of the presented method.展开更多
When conducting company performance evaluations,the traditional method cannot reflect the distribution characteristics of the company’s operating conditions in the entire securities market.Gephi is an efficient tool ...When conducting company performance evaluations,the traditional method cannot reflect the distribution characteristics of the company’s operating conditions in the entire securities market.Gephi is an efficient tool for data analysis and visualization in the era of big data.It can convert the evaluation results of all listed companies into nodes and edges,and directly display them in the form of graphs,thus making up for the defects of traditional methods.This paper will take all the listed companies in the Shanghai and Shenzhen Stock Exchange as the analysis object.First uses tushare and web crawlers to collect the financial statement data of these companies.And then,uses the Economic Value Added model to calculate the EVA of each listed company and build graph data.Next,import the graph data into gephi to generate the distribution graph of all listed companies’performance,and summarize the distribution characteristics of business performance.Finally,select a listed company that you want to analyze in detail,using the traditional DuPont analysis method to conduct micro level visualization analysis of the business performance to find the main factors affecting the company’s operating performance.Incorporating gephi into traditional performance analysis methods will make the results of traditional analytical methods more effective and complete.展开更多
BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairmen...BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairment,is critical for clinical intervention,yet it remains elusive and challenging to identify.AIM To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.METHODS Using diffusion tensor imaging(DTI),we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls.Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.RESULTS T2DM patients exhibited reduced global/local efficiency and small-worldness,alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections,suggesting compensatory mechanisms.A classification model leveraging 18 connectivity features achieved 92.5%accuracy in distinguishing T2DM brains.Structural connectivity patterns further predicted disease onset with an error of±1.9 years.CONCLUSION Our findings reveal early-stage brain network reorganization in T2DM,highlighting subcortical-frontal connectivity as a compensatory biomarker.The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.展开更多
This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel （BSP） com puting model. The tool is named Mapreduce and BSP based Graphmining tool （MBGM）. The core...This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel （BSP） com puting model. The tool is named Mapreduce and BSP based Graphmining tool （MBGM）. The core of this mining system are four sets of parallel graphmining algorithms programmed in the BSP parallel model and one set of data extractiontransformationload ing （ETE） algorithms implemented in MapReduce. To invoke these algorithm sets, we designed a workflow engine which optimized for cloud computing. Finally, a welldesigned data management function enables users to view, delete and input data in the Ha doop distributed file system （HDFS）. Experiments on artificial data show that the components of graphmining algorithm in MBGM are efficient.展开更多
Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-o...Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-of-the-art link analysis tech-niques,we propose a two-state model to approximate how CCs tangle with core modules.According to this model,we obtain scatter and centralization scores for each program element.Espe-cially,the scatter scores are adopted to select CC seeds.Further-more,to identify composite CCs,we adopt a novel similarity measurement and develop an undirected graph clustering to group these seeds.Finally,we compare it with the previous work and illustrate its effectiveness in identifying composite CCs.展开更多
Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a cli...Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.展开更多
Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture t...Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.展开更多
AIM To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug(AED) treatment.METHODS The relation between functional magnetic resonance-acquired brain network measures,...AIM To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug(AED) treatment.METHODS The relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A "low risk" category(lamotrigine or levetiracetam, n=16), an "intermediate risk" category(carbamazepine, oxcarbazepine, phenytoin, or valproate, n=34) and a "high risk" category(topiramate, n=5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment. RESULTS Central information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant(P < 0.05, ANCOVA), with a significantly higher global efficiency for patient from the low category compared with the high risk category(P < 0.05, post-hoc test). Risk category had no significant effect on the clustering coefficient(ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient(linear regression analysis, P > 0.15) were observed. CONCLUSION Only the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.展开更多
Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor nodes.However,this weighting mechanism is learned ba...Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor nodes.However,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant limitations.In this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise interference.Furthermore,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting mechanism.The model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message passing.Experiments show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.展开更多
We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral grap...We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral graph. The contribution may be divided into three parts. Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model. Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features. Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.展开更多
Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits...Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.展开更多
We build a double quantum-dot system with Coulomb coupling and aim at studying connections among the entropy production,free energy,and information flow.By utilizing concepts in stochastic thermodynamics and graph the...We build a double quantum-dot system with Coulomb coupling and aim at studying connections among the entropy production,free energy,and information flow.By utilizing concepts in stochastic thermodynamics and graph theory analysis,Clausius and nonequilibrium free energy inequalities are built to interpret local second law of thermodynamics for subsystems.A fundamental set of cycle fluxes and affinities is identified to decompose two inequalities by using Schnakenberg's network theory.Results show that the thermodynamic irreversibility has energy-related and information-related contributions.A global cycle associated with the feedback-induced information flow would pump electrons against the bias voltage,which implements a Maxwell demon.展开更多
The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data gener...The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel （BSP） computing model named BSP-based Parallel Graph Mining （BPGM）. This system has four sets of parallel graph mining algorithms programmed in the BSP parallel model and a well-designed workflow engine optimized for cloud computing to invoke these algorithms. Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.展开更多
Background:Amblyopia(lazy eye)is one of the most common causes of monocular visual impairment.Intensive investigation has shown that amblyopes suffer from a range of deficits not only in the primary visual cortex but ...Background:Amblyopia(lazy eye)is one of the most common causes of monocular visual impairment.Intensive investigation has shown that amblyopes suffer from a range of deficits not only in the primary visual cortex but also the extra-striate visual cortex.However,amblyopic brain processing deficits in large-scale information networks especially in the visual network remain unclear.Methods:Through resting state functional magnetic resonance imaging(rs-fMRI),we studied the functional connectivity and efficiency of the brain visual processing networks in 18 anisometropic amblyopic patients and 18 healthy controls(HCs).Results:We found a loss of functional correlation within the higher visual network(HVN)and the visuospatial network(VSN)in amblyopes.Additionally,compared with HCs,amblyopic patients exhibited disruptions in local efficiency in the V3v(third visual cortex,ventral part)and V4(fourth visual cortex)of the HVN,as well as in the PFt,hIP3(human intraparietal area 3),and BA7p(Brodmann area 7 posterior)of the VSN.No significant alterations were found in the primary visual network(PVN).Conclusion:Our results indicate that amblyopia results in an intrinsic decrease of both network functional correlations and local efficiencies in the extra-striate visual networks.展开更多
Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains un...Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains unclear.We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.Methods:We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography(PET).Based on graph theory approaches,we detected local and global functional connectivity across the whole brain by using degree centrality(DC)and eigenvector centrality(EC)respectively.Additionally,we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid(CSF)data.The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition.Then,we correlated these network metrics with pathological results.Results:The SMC individuals showed higher DC in the bilateral hippocampus(HP)and left fusiform gyrus and lower DC in the inferior parietal region than controls.Across all subjects,the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau181.However,no significant between-group difference existed in EC and cerebral amyloid deposition.Conclusion:We found impaired local,but not global,intrinsic connectivity networks in SMC individuals.Given the relationships between DC value and tau level,we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.展开更多
Background： Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder （MDD）. Moreover, the exactly topological organization o...Background： Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder （MDD）. Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods： The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results： Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions： All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.展开更多
Background:Different oscillations of brain networks could carry different dimensions of brain integration.We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease(PD)across ...Background:Different oscillations of brain networks could carry different dimensions of brain integration.We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease(PD)across early stage to middle stage by using graph theory-based analysis.Methods:Eighty-eight PD patients including 39 PD patients in the early stage(EPD)and 49 patients in the middle stage(MPD)and 36 controls were recruited in the present study.Graph theory-based network analyses from three oscillation frequencies(slow-5:0.01–0.027 Hz;slow-4:0.027–0.073 Hz;slow-3:0.073–0.198 Hz)were analyzed.Nodal metrics(e.g.nodal degree centrality,betweenness centrality and nodal efficiency)were calculated.Results:Our results showed that(1)a divergent effect of oscillation frequencies on nodal metrics,especially on nodal degree centrality and nodal efficiency,that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed,which visually showed that network was perturbed in PD;(2)PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities,which was consistently detected within all three oscillation frequencies;(3)the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients;(4)logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level;(5)occipital disruption within high frequency(slow-3)made a significant influence on motor impairment which was dominated by akinesia and rigidity.Conclusions:Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.展开更多
基金Sponsored by the National Natural Science Foundation of China(Youth Program)(51908063)。
文摘Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,institutional cooperation network analysis,author collaboration network analysis,keyword co-occurrence,and emergent words analysis are drawn.Combined with the literature content analysis,four hot spots in the research field of landscape architecture microclimate in China are obtained,namely ENVI-MET,comfort,design strategy and urban green space.The research trend is thermal comfort,human body comfort and winter city.The research results can provide reference for the research on domestic garden microclimate.
文摘Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.
文摘This paper provides a comprehensive examination of El Sallam Garden in Port Said City,concentrating on its landscape characteristics and potential for design enhancement.This study looks at how space syntax can be used to assess the impact of a tree planting design’s spatial configuration on an urban park’s visual fields.Trees play an important role in determining the spatial characteristics of an outdoor space.According to space syntax theory,an urban area is a collection of connected spaces that can be represented by a matrix of quantitative properties known as syntactic measures.Computer simulations can be used to measure the quantitative properties of these matrices.This study uses space syntax techniques to assess how tree configurations and garden area which can affect the social structures of small-scale gardens in Port Said.It also looks at how these techniques can be used to predict the social structures of four garden zones in El Sallam Garden.The study includes an observational and space syntax study through comparative analysis of four garden zones in El Sallam garden.The results of the study show that the area and planting configurations of the garden had a significant effect on the syntactic social and visual measures of the urban garden.The conclusions and recommendations can be a useful tool for landscape architects,urban planners,and legislators who want to enhance public areas and encourage social interaction in urban settings.
基金support of the National Natural Science Foundation of China (Grant No. 41130751)China Scholarship Council, Research Program for Western China Communication (Grant No. 2011ZB04)China Central University Funding
文摘Limit equilibrium method （LEM） and strength reduction method （SRM） are the most widely used methods for slope stability analysis. However, it can be noted that they both have some limitations in practical application. In the LEM, the constitutive model cannot be considered and many assumptions are needed between slices of soil/rock. The SRM requires iterative calculations and does not give the slip surface directly. A method for slope stability analysis based on the graph theory is recently developed to directly calculate the minimum safety factor and potential critical slip surface according to the stress results of numerical simulation. The method is based on current stress state and can overcome the disadvantages mentioned above in the two traditional methods. The influences of edge generation and mesh geometry on the position of slip surface and the safety factor of slope are studied, in which a new method for edge generation is proposed, and reasonable mesh size is suggested. The results of benchmark examples and a rock slope show good accuracy and efficiency of the presented method.
基金This research is funded by the National Social Science Fund Project,grant number 14BJL086.This research is funded by the Open Foundation for the University Innovation Platform in the Hunan Province,grant number 18K103Hunan Provincial Natural Science Foundation of China,grant number 2017JJ2016+2 种基金Accurate crawler design and implementation with a data cleaning function,National Students innovation and entrepreneurship of training program,grant number 201811532010This research work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province.Open project,grant number 20181901CRP03,20181901CRP04,20181901CRP05National Social Science Fund Project:Research on the Impact Mechanism of China’s Capital Space Flow on Regional Economic Development(Project No.14BJL086)。
文摘When conducting company performance evaluations,the traditional method cannot reflect the distribution characteristics of the company’s operating conditions in the entire securities market.Gephi is an efficient tool for data analysis and visualization in the era of big data.It can convert the evaluation results of all listed companies into nodes and edges,and directly display them in the form of graphs,thus making up for the defects of traditional methods.This paper will take all the listed companies in the Shanghai and Shenzhen Stock Exchange as the analysis object.First uses tushare and web crawlers to collect the financial statement data of these companies.And then,uses the Economic Value Added model to calculate the EVA of each listed company and build graph data.Next,import the graph data into gephi to generate the distribution graph of all listed companies’performance,and summarize the distribution characteristics of business performance.Finally,select a listed company that you want to analyze in detail,using the traditional DuPont analysis method to conduct micro level visualization analysis of the business performance to find the main factors affecting the company’s operating performance.Incorporating gephi into traditional performance analysis methods will make the results of traditional analytical methods more effective and complete.
基金Supported by National Natural Science Foundation of China,No.82104698,No.82330058,No.T2341014,and No.32200923.
文摘BACKGROUND Cognitive decline in type 2 diabetes mellitus(T2DM)occurs years before the onset of clinical symptoms.Early detection of this incipient cognitive decline stage,which is T2DM without mild cognitive impairment,is critical for clinical intervention,yet it remains elusive and challenging to identify.AIM To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.METHODS Using diffusion tensor imaging(DTI),we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls.Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.RESULTS T2DM patients exhibited reduced global/local efficiency and small-worldness,alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections,suggesting compensatory mechanisms.A classification model leveraging 18 connectivity features achieved 92.5%accuracy in distinguishing T2DM brains.Structural connectivity patterns further predicted disease onset with an error of±1.9 years.CONCLUSION Our findings reveal early-stage brain network reorganization in T2DM,highlighting subcortical-frontal connectivity as a compensatory biomarker.The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.
基金supported by ZTE Industry-Academia-Research Cooperaton Funds
文摘This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel （BSP） com puting model. The tool is named Mapreduce and BSP based Graphmining tool （MBGM）. The core of this mining system are four sets of parallel graphmining algorithms programmed in the BSP parallel model and one set of data extractiontransformationload ing （ETE） algorithms implemented in MapReduce. To invoke these algorithm sets, we designed a workflow engine which optimized for cloud computing. Finally, a welldesigned data management function enables users to view, delete and input data in the Ha doop distributed file system （HDFS）. Experiments on artificial data show that the components of graphmining algorithm in MBGM are efficient.
基金Supported by the National Pre-research Project (513150601)
文摘Identifying composite crosscutting concerns（CCs） is a research task and challenge of aspect mining.In this paper,we propose a scatter-based graph clustering approach to identify composite CCs.Inspired by the state-of-the-art link analysis tech-niques,we propose a two-state model to approximate how CCs tangle with core modules.According to this model,we obtain scatter and centralization scores for each program element.Espe-cially,the scatter scores are adopted to select CC seeds.Further-more,to identify composite CCs,we adopt a novel similarity measurement and develop an undirected graph clustering to group these seeds.Finally,we compare it with the previous work and illustrate its effectiveness in identifying composite CCs.
基金supported by an internal fund from Macao Polytechnic University(RP/FCSD-02/2022).
文摘Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease mechanisms.The herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely unknown.This study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating CFS.Methods:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS cases.Causal modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug targets.Furthermore,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated proteins.Results:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by 2011.Causal graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of CFS.Proteome-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in CFS.The major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual screening.Conclusions:Our integrated analytical framework combining epidemiological,genetic,and in silico data provides a novel strategy for elucidating complex disease mechanisms,such as CFS,and informing models of action of traditional Chinese medicines,such as QJYQ.Further validation in animal models is warranted to confirm the potential pharmacological effects of QJYQ on ESAM and as a treatment for CFS.
文摘Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.
文摘AIM To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug(AED) treatment.METHODS The relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A "low risk" category(lamotrigine or levetiracetam, n=16), an "intermediate risk" category(carbamazepine, oxcarbazepine, phenytoin, or valproate, n=34) and a "high risk" category(topiramate, n=5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment. RESULTS Central information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant(P < 0.05, ANCOVA), with a significantly higher global efficiency for patient from the low category compared with the high risk category(P < 0.05, post-hoc test). Risk category had no significant effect on the clustering coefficient(ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient(linear regression analysis, P > 0.15) were observed. CONCLUSION Only the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.
基金Supported by the Natural Science Foundation of Xiamen (3502Z20227067)。
文摘Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor nodes.However,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant limitations.In this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise interference.Furthermore,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting mechanism.The model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message passing.Experiments show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.
基金supported by the National Natural Science Foundation of China (No.60375003)the Aeronautics and Astronautics Basal Science Foundation of China (No.03I53059)the Science and Technology Innovation Foundation of Northwestern Polytechnical University (No.2007KJ01033)
文摘We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral graph. The contribution may be divided into three parts. Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model. Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features. Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(LGF19H090023)the National Natural Science Foundation of China(81801785 and 82172056)+5 种基金the National Key Research and Development Program of China(2019YFC1711800)the Key Research and Development Program of Shanxi(2020ZDLSF04-03)This work was partly supported by the grants from the Zhejiang Lab(2019KE0AD01 and 2021KE0AB04)the Zhejiang University Global Partnership Fund(100000-11320)the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Fundamental Research Funds for the Central Universities.
文摘Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.
基金Project supported by the National Natural Science Foundation(Grant No.11805159)the First Batch of National First-class Undergraduate Courses of China(2020)+1 种基金the Natural Science Foundation of Fujian Province,China(Grant No.2019J05003)Teaching Research Program of Thermodynamics and Statistical Physics in the Institution of Higher Education of China(2019).
文摘We build a double quantum-dot system with Coulomb coupling and aim at studying connections among the entropy production,free energy,and information flow.By utilizing concepts in stochastic thermodynamics and graph theory analysis,Clausius and nonequilibrium free energy inequalities are built to interpret local second law of thermodynamics for subsystems.A fundamental set of cycle fluxes and affinities is identified to decompose two inequalities by using Schnakenberg's network theory.Results show that the thermodynamic irreversibility has energy-related and information-related contributions.A global cycle associated with the feedback-induced information flow would pump electrons against the bias voltage,which implements a Maxwell demon.
基金supported by the National Key Basic Research and Department (973) Program of China (No. 2013CB329603)the National Natural Science Foundation of China (Nos. 61074128, 61375058, and 71231002)
文摘The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel （BSP） computing model named BSP-based Parallel Graph Mining （BPGM）. This system has four sets of parallel graph mining algorithms programmed in the BSP parallel model and a well-designed workflow engine optimized for cloud computing to invoke these algorithms. Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.
基金supported by the National Natural Science Foundation of China(grant numbers 81501942,81701665,81500754)by the Fundamental Research Funds for the Central Universities(grant number WK2100230016).
文摘Background:Amblyopia(lazy eye)is one of the most common causes of monocular visual impairment.Intensive investigation has shown that amblyopes suffer from a range of deficits not only in the primary visual cortex but also the extra-striate visual cortex.However,amblyopic brain processing deficits in large-scale information networks especially in the visual network remain unclear.Methods:Through resting state functional magnetic resonance imaging(rs-fMRI),we studied the functional connectivity and efficiency of the brain visual processing networks in 18 anisometropic amblyopic patients and 18 healthy controls(HCs).Results:We found a loss of functional correlation within the higher visual network(HVN)and the visuospatial network(VSN)in amblyopes.Additionally,compared with HCs,amblyopic patients exhibited disruptions in local efficiency in the V3v(third visual cortex,ventral part)and V4(fourth visual cortex)of the HVN,as well as in the PFt,hIP3(human intraparietal area 3),and BA7p(Brodmann area 7 posterior)of the VSN.No significant alterations were found in the primary visual network(PVN).Conclusion:Our results indicate that amblyopia results in an intrinsic decrease of both network functional correlations and local efficiencies in the extra-striate visual networks.
基金Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative(ADNI)(National Institutes of Health Grant U01 AG024904)and DOD ADNI(Department of Defense award number W81XWH-12-2-0012)This study was funded by National Key Research and Development Program of China(Grant No.2016YFC1306600)+3 种基金Zhejiang Provincial Natural Science Foundation of China(Grant Nos.LZ14H180001 and Y16H090026)Young ResearchTalents Fund,Chinese Medicine Science,and Technology Project of Zhejiang Province(Grant No.2018ZQ035)the Fundamental Research Funds for the Central Universities(No.2017XZZX001-01)Zhejiang Medicine and Health Science and Technology Program(2018KY418).
文摘Background:Individuals with subjective memory complaints(SMC)feature a higher risk of cognitive decline and clinical progression of Alzheimer’s disease(AD).However,the pathological mechanism underlying SMC remains unclear.We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.Methods:We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography(PET).Based on graph theory approaches,we detected local and global functional connectivity across the whole brain by using degree centrality(DC)and eigenvector centrality(EC)respectively.Additionally,we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid(CSF)data.The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition.Then,we correlated these network metrics with pathological results.Results:The SMC individuals showed higher DC in the bilateral hippocampus(HP)and left fusiform gyrus and lower DC in the inferior parietal region than controls.Across all subjects,the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau181.However,no significant between-group difference existed in EC and cerebral amyloid deposition.Conclusion:We found impaired local,but not global,intrinsic connectivity networks in SMC individuals.Given the relationships between DC value and tau level,we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.
基金The work was supported by the grants from:The National High-tech Research and Development Program of China，the National Natural Science Foundation of China，the Clinical Medicine Technology Foundation of Jiangsu Province，the Natural Science Foundation of Jiangsu Province，State Key Clinical Specialty，Provincial Medical Key Discipline
文摘Background： Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder （MDD）. Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods： The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results： Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions： All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.
基金This work was supported by the 13th Five-year Plan for National Key Research and Development Program of China(Grant No.2016YFC1306600)the Fundamental Research Funds for the Central Universities of China(Grant No.2017XZZX001-01)+3 种基金the 12th Five-year Plan for National Science and Technology Supporting Program of China(Grant No.2012BAI10B04)the National Natural Science Foundation of China(Grant Nos.81571654,81371519 and 81701647)the Cooperative Project by Ministry of Health and Provincial Department(Grant No.2016149022)the Projects of Medical and Health Technology Development Program in Zhejiang Province(Grant No.2015KYB174).
文摘Background:Different oscillations of brain networks could carry different dimensions of brain integration.We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson’s disease(PD)across early stage to middle stage by using graph theory-based analysis.Methods:Eighty-eight PD patients including 39 PD patients in the early stage(EPD)and 49 patients in the middle stage(MPD)and 36 controls were recruited in the present study.Graph theory-based network analyses from three oscillation frequencies(slow-5:0.01–0.027 Hz;slow-4:0.027–0.073 Hz;slow-3:0.073–0.198 Hz)were analyzed.Nodal metrics(e.g.nodal degree centrality,betweenness centrality and nodal efficiency)were calculated.Results:Our results showed that(1)a divergent effect of oscillation frequencies on nodal metrics,especially on nodal degree centrality and nodal efficiency,that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed,which visually showed that network was perturbed in PD;(2)PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities,which was consistently detected within all three oscillation frequencies;(3)the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients;(4)logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level;(5)occipital disruption within high frequency(slow-3)made a significant influence on motor impairment which was dominated by akinesia and rigidity.Conclusions:Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.
