Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Generalized chaos synchronization of a weighted complex network with different nodes

This paper proposes a method of realizing generalized chaos synchronization of a weighted complex network with different nodes. Chaotic systems with diverse structures are taken as the nodes of the complex dynamical network, the nonlinear terms of the systems are taken as coupling functions, and the relations among the nodes are built through weighted connections. The structure of the coupling functions between the connected nodes is obtained based on Lyapunov stability theory. A complex network with nodes of Lorenz system, Coullet system, RSssler system and the New system is taken as an example for simulation study and the results show that generalized chaos synchronization exists in the whole weighted complex network with different nodes when the coupling strength among the nodes is given with any weight value. The method can be used in realizing generalized chaos synchronization of a weighted complex network with different nodes. Furthermore, both the weight value of the coupling strength among the nodes and the number of the nodes have no effect on the stability of synchronization in the whole complex network.

Multiscale Theories and Applications:From Microstructure Design to Macroscopic Assessment for Carbon Nanotubes Networks

Carbon nanotube(CNT)networks enable CNTs to be used as building blocks for synthesizing novel advanced materials,thus taking full advantage of the superior properties of individual CNTs.Multiscale analyses have to be adopted to study the load transfer mechanisms of CNT networks from the atomic scale to the macroscopic scale due to the huge computational cost.Among them,fully resolved structural features include the graphitic honeycomb lattice(atomic),inter-tube stacking(nano)and assembly(meso)of CNTs.On an atomic scale,the elastic properties,ultimate stresses,and failure strains of individual CNTs with distinct chiralities and radii are obtained under various loading conditions by molecular mechanics.The dependence of the cohesive energies on spacing distances,crossing angles,size and edge effects between two CNTs is analyzed through continuum modeling in nanoscale.The mesoscale models,which neglect the atomic structures of individual CNTs but retain geometrical information about the shape of CNTs and their assembly into a network,have been developed to study the multi-level mechanism of material deformation and microstructural evolution in CNT networks under stretching,from elastic elongation,strengthening to damage and failure.This paper summarizes the multiscale theories mentioned above,which should provide insight into the optimal assembling of CNT network materials for elevated mechanical performance.

Brain networks modeling for studying the mechanism underlying the development of Alzheimer’s disease

Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).

Eco-spatial Structure of Urban Agglomeration

In terms of ecological theory, this paper makes a comprehensive analysis of the mutualism and co- evolutionary mechanism between the eco-spatial structure and socio-economic development of the urban agglomeration, and maps out optimized modes of the eco-spatial structure of the urban agglomeration. The analysis is a case study of the urban agglomeration on different levels of global, national, provincial and local scales, on the basis of those conclusions are drawn: 1) Within the scope of the urban agglomeration, the cities should be reasonably sized and appropriately densified; the spatial combination of the urban agglomeration ought to be orderly, and its eco-spatial structure ought to be optimized and efficient; the relationship between the economic society and eco-spatial environment ought to be that of mutual benefit and co-evolution. 2) “The mode of corridor group network” is a certain trend evoked from the spatial structure of urban agglomeration. 3) The eco-spatial structure of urban agglomeration under “the mode of corridor group network” can further increase the environmental capacity of urban agglomeration, and is in favor of the harmonious relationship between man and nature.

Applications of Wavelets in 3-D Audio Simulation

Wavelet has been used as a powerful tool in the signal processing and function approximation recently. This paper presents the application of wavelets for solving two key problems in 3-D audio simulation. First, we employ discrete wavelet transform (DWT) combined with vector quantization (VQ) to compress audio data in order to reduce tremendous redundant data storage and transmission times. Secondly, we use wavelets as the activation functions in neural networks called feed-forward wavelet networks to approach auditory localization information cues (head-related transfer functions (HRTFs) are used here). The experimental results demonstrate that the application of wavelets is more efficient and useful in 3-D audio simulation.

Two Network Structure Indicators for Conventional Public Transit

The significance of network structure indicators for the planning and management of conventional public transit is widely acknowledged.In order to improve and enrich the conventional public transit assessment system,two network structure indicators are proposed.Firstly,according to the obvious defects lying in the traditional no-linear coefficient,the realistic no-linear coefficient γRNL,a modified no-linear coefficient indicator,is put forward,which takes into account the effects of barriers in a city.Secondly,to cover the gap of an indicator which can reflect the coverage homogeneity of a transit network,the length dimension LDis proposed on the basis of Fractal Theory.Finally,a case study is applied to verify the validity and practicability of the two indicators in problem diagnosis using regression analysis.The results validate that γRNLcan evaluate the detour of bus lines more reasonably than the previous no-linear coefficient because it reflects the layout of bus lines,and LDcan represent the rate of change of the network density,adding a new member to the scheme of network structure indicators for public transit.

Heterogeneous responsibilities of three grassland resource governance modes in Inner Mongolia, China

Grassland resource governance is an important part of ecological civilization construction,and it directly af‐fects grassland governance performance.This study deploys principal-agent theory and uses social network analysis to compare grassland resource governance modes in China in terms of institutional settings and insti‐tutional network characteristics.This study found three types of grassland resource governance modes:self-designed,docked,and integrated.The self-designed mode forms a network structure with dual centers and multiple members,and has the second-best structural mode of the three types.The docked mode forms a net‐work structure with a single center in which the institution is the absolute core and is relatively divergent.It has the weakest structural advantage of the three types.The integrated mode forms a network structure with a single institution at the core and other institutions distributed evenly throughout the structure.This mode has the strongest structural advantage among the three types.This study offers the practical application of improv‐ing the practice of grassland governance in China and is theoretically significant because it can contribute to improving grassland governance modes and enriching the public goods resources governance.

阿尔茨海默病患者大脑形态学及结构协变网络的改变

目的探讨阿尔茨海默病(Alzheimer's disease,AD)患者大脑灰质体积、灰质皮层厚度及基于皮层厚度的结构协变网络(structural covariance network,SCN)的拓扑属性改变。材料与方法本研究共筛选了250例来自ADNI数据库的被试,包括AD组100人,健康对照(healthy controls,HCs)组150人。首先,利用基于体素的形态学分析方法(voxel-based morphometry,VBM)和基于表面的形态学分析方法(surface-based morphometry,SBM)分别计算每组被试的灰质体积和皮层厚度并比较其组间差异。其次,将有组间差异的脑区定义为感兴趣区(region of interest,ROI),提取每一个ROI的灰质体积和皮层厚度值,与认知量表进行偏相关分析。最后,构建基于皮层厚度的SCN并利用图论分析方法分析该网络的全局属性及局部属性的变化特征。结果第一,相较于HCs组,AD组的灰质体积和皮层厚度显著下降[体素和顶点水平总体误差(family-wise error,FWE)校正后P<0.001]。AD组灰质体积下降的脑区主要包括双侧海马、双侧眶额皮层、左侧岛叶、右侧枕下回、左侧楔前叶、左侧中央前回、左侧中央扣带回。AD组皮层厚度变薄的脑区主要包括双侧颞叶、双侧额叶、双侧顶叶、双侧扣带回、双侧梭状回、双侧岛回、双侧楔前叶等。第二,偏相关分析表明,AD组简易精神状态检查量表(Mini-Mental State Examination,MMSE)得分分别与右侧海马体积[rs=0.35,错误发现率(false discovery rate,FDR)校正后P<0.001]、左侧海马体积(r_(s)=0.38,FDR校正后P<0.001)、右侧梭状回皮层厚度(r_(s)=0.38,FDR校正后P<0.001)呈正相关;临床痴呆评定量表(Clinical Dementia Rating Sum of Boxes,CDR-SB)评分与左侧梭状回皮层厚度(r_(s)=-0.39,FDR校正后P<0.001)呈负相关。第三,脑网络分析表明,AD组SCN的全局效率(P<0.001)、局部效率(P=0.03)及小世界属性(P<0.001)高于HCs组,最短路径低于HCs组(P<0.001)。结论联合VBM、SBM的形态学分析及SCN的图论分析有助于全面理解AD患者脑网络的重组及其意义,进而为AD患者神经影像学改变提供新的见解和证据。

分形视角下城市非规则道路网近百年演化过程研究

城市道路网在不同时期具有独特的网络结构,然而在近百年历程中如何演变,其过程并不清晰。研究以城市非规则道路网为例,通过历史资料整理提取了南京市鼓楼片区1900年来4个时期道路网,运用分形理论量化表达道路网覆盖均匀度和发育复杂度。结果表明:鼓楼片区非规则路网是由贯通性主干路牵引形成的;其路网相对稀疏,依然有更新和改进的空间;路网覆盖度和连通度近年来趋于平缓,城市应重视干道的功能拓展和“大院制”支路的打通。通过文献史料分析,解释了不同阶段规划思想、社会变革等因素对道路网演变起到的作用,认为历史古城道路网的更新保护应重视起牵引作用的骨架干道,同时重视非机动车和行人的需求,提升其可达性。

重大突发事件下网络舆情传播中回声室网络结构研究

[目的/意义]在社交网络中回声室对重大突发事件下的社交网络舆情演进具有重要作用,研究重大突发事件网络舆情中回声室网络结构可以为相关部门的舆情引导和管控提供指导和帮助。[方法/过程]基于网络结构理论,利用社会网络分析法和情感分析方法,进行用户回声室网络结构识别以及用户回声室网络结构表征及网络同质性检验。在此基础上,构建重大突发事件中回声室网络结构分析模型,并结合重大突发事件“3·21”东航客机事故中典型舆情话题进行实证研究。[结果/结论]重大突发事件网络舆情中存在回声室效应,由于微博评论与转发机制的不同,回声室网络结构存在明显的网络特征差异,且评论机制更有利于社交网络在重大突发事件下的情感分享,并可以凝聚群体观点和用户想法,从而促进意见领袖在群内和群外成员之间的互动。

基于KAP理论的大学生网络安全教育研究

为改善大学生网络安全教育现状,提高大学生网络安全教育水平,结合知识-态度-行为(KAP)理论,构建大学生安全教育KAP模型;通过问卷发放和筛选,获取659份有效问卷;采用结构方程模型(SEM)检验网络安全教育对网络安全认知、网络安全态度、网络安全行为的影响,以及可能存在的中介作用影响路径。结果表明:网络安全教育显著影响网络安全认知、网络安全态度、网络安全行为,其中,网络安全认知、网络安全态度起到独立中介及链式中介作用;法律道德教育、知识教育、实践活动、榜样引领均能直接影响网络安全行为,其中,法律道德教育对网络安全行为的影响最强,知识教育次之,实践活动和榜样引领最弱。在具体作用路径方面,法律道德教育主要通过直接效应来影响网络安全行为,知识教育、实践活动、榜样引领主要通过安全认知间接影响网络安全行为。

理论驱动的弹性结构体系图神经网络计算模型

该文提出了一个理论驱动的弹性结构体系图神经网络计算模型StructGNN-E,能够高保真数字化结构体系的拓扑连接关系与构件组成信息,无需外部标签数据即可实现对任意杆系结构体系的弹性内力分析,且计算结果具有理论正确性。总结了结构体系层次的特点,理论分析了常规神经网络的不可行性,进而采用了基于非欧图数据的图神经网络架构,能够有效刻画结构体系的非序列性与非平移不变性。考虑到体系层次数据严重匮乏以及常规智能计算方法忽视力学意义的问题,通过将三大力学方程与深度学习推理过程相结合,提出了适用于体系内力分析的理论驱动模式,实现了不依赖于外部标签数据的智能求解方案。数值试验表明:StructGNN-E模型能够高精度完成杆系结构体系的弹性内力分析,且在大规模框架结构计算中计算效率提升可达36%。通过具体的对比试验,证明了常规深度学习模型与数据驱动模式在体系层次的不适用性,进一步阐释了StructGNN-E模型的有效性与合理性。

急性轻度创伤性脑损伤患者的个体形态学脑网络研究

目的探究急性轻度创伤性脑损伤(mild traumatic brain injury,mTBI)患者个体形态学脑网络的拓扑属性改变。材料与方法共纳入43例mTBI患者和37例健康对照(health control,HC),采集所有受试者高分辨率T1WI的图像,使用Freesurfer软件对数据进行预处理得到5个形态学指标(皮层厚度、灰质体积、脑表面积、脑沟深度、平均曲率)脑图并构建个体形态学脑网络,使用图论分析的方法计算网络拓扑属性,通过双样本t检验比较组间差异,并采用错误发生率(false discovery rate,FDR)对结果进行多重比较校正。结果与HC组相比,mTBI组左侧额极横回(t=-2.186,P=0.032)、中央沟(t=-2.617,P=0.011)、外侧沟水平支(t=-2.456,P=0.016)和右侧枕极(t=-2.013,P=0.048)的节点度中心性(degree centrality,DC)增高,左侧额极横回(t=-2.182,P=0.032)、中央沟(t=-2.226,P=0.029)、外侧沟水平支(t=-2.440,P=0.017)和右侧楔前叶(t=-2.207,P=0.030)的节点效率(nodal efficiency,N_(e))增高,与认知和执行功能相关;左侧楔叶(t=2.173,P=0.033)、角回(t=2.498,P=0.015)、海马旁回(t=4.009,P<0.001)的节点DC下降,左侧颞上回极平面(t=2.394,P=0.019)、角回(t=2.668,P=0.009)、海马旁回(t=4.671,P<0.001)、胼胝体沟(t=2.189,P=0.032)的N_(e)下降,与记忆和情绪调节相关。对于全局拓扑属性,二者差异无统计学意义(P>0.05)。结论急性期mTBI的个体形态学脑网络仍保持了小世界属性。mTBI异常增高的节点DC和N_(e),主要集中在与认知和执行功能相关的脑区,反映了大脑对认知功能的应激性代偿;异常减低的节点DC和N_(e),主要集中在与记忆、情绪调节相关的脑区,揭示了急性期的认知和情绪变化。这为急性期mTBI的研究提供了新的角度,对其大脑网络改变机制的探索提供了进一步线索。

都市圈新城区乡村医疗服务中心地网络结构及其影响因素研究——以武汉市新城区为例

医疗服务是维护居民健康的重要保障。基于修正引力模型和社会网络分析法,分析武汉市6个新城区乡村医疗服务中心地空间网络结构及其影响因素。研究表明:(1)新城区乡村医疗服务中心地呈现网络结构,多中心层级化共存。医疗服务网络密度整体上处于初级阶段,总体关联度偏低,且大部分联系限于各区内部,整体节点聚集度较低。医疗服务中心地的网络非均衡性明显,形成了不同尺度的空间嵌套格局。(2)医疗服务中心地可分为6个子群,并呈现由内到外的圈层结构。6个子群的空间关联网络由北向南形成层次分明的板块,而且呈现出小团体现象。特别需要指出的是,子群的划分表现出鲜明的邻近指向特征,地理邻近关系对医疗服务中心地关联强度的影响比较明显。(3)空间邻近性、农业产值、第二产业企业注册资本、人口数量、城镇化水平、道路密度和交通可达性对新城区乡村医疗服务中心地空间关联网络强度的提高具有显著影响,而第三产业企业的影响并不显著。

基于SEM-MIP状态估计的配电网拓扑辨识方法

配电网拓扑辨识是配电系统操作的关键问题和重要前提。由于缺乏足够的量测设备和配电系统拓扑的频繁变化,导致实际的配电网拓扑辨识问题十分具有挑战性。为此提出了基于同步量测数据状态估计的实时拓扑辨识方法。该方法可以利用少量相量测量单元的测量数据准确估计配电网拓扑结构。首先,在配电网中引入结构方程模型(structural equation model,SEM),用于捕捉拓扑与支路电流的关系。然后,针对拓扑辨识问题提出了混合整数规划(mixed integer programming,MIP)方法,通过复数方程的虚实部解耦,利用泰勒展开与等效松弛等手段对复数方程和约束条件进行线性化。此外,将配电网运行方式约束以节点为单位进行了重新规划。最后,所提基于结构方程模型的混合整数规划方法(SEM-MIP)可以使用现有的商业求解器求解,并通过一个实际12节点配电系统和IEEE 33节点测试系统验证了所提方法的有效性。

社会网络理论视域中的职业教育产教关系:问题表征与关系重构

考察职业教育产教内部关系对推进职业教育产教深度融合具有重要价值。社会网络理论提供了分析产教内部关系的全新框架,即结构洞驱动了以技能传递为核心的职业教育产教关系网络生成,产教关系深入是从技能需求出发的弱关系向强关系转化并保持适度平衡的过程,产教互动的本质是以技能转换为诉求的产教资源双向转化。而当前我国职业教育产教关系呈现出僵化、失衡、退化的产教强弱关系样态,结构洞中介作用弱化导致产教资源流动受限,职业教育存量社会资源的可观与高质量技术技能社会资本匮乏并存。未来产教关系构建的着力点应以强化弱关系、拓展结构洞、增强社会资本为目标,创新构建以业缘关系和地缘关系相互交织、团体格局和差序格局优势互补、信息流动与资源共享有效协同的产教融合载体。

基于图论算法的光纤传感网络异常通信节点识别方法

光纤传感网络在现代监测系统中发挥着重要作用,然而,异常通信节点则会影响网络性能和数据准确性。为了解决这一问题,本文提出了一种基于图论算法的异常通信节点识别方法。通过对光纤传感网络进行拓扑结构分析,并结合图论算法,实现了对异常通信节点的准确识别和定位。实验结果表明,所提方法能够有效识别光纤传感网络中的异常节点,为网络管理和维护提供了有力支持。

基于问题行为理论的低年级大学生社交网络成瘾的影响因素研究

目的·基于问题行为理论,构建结构方程模型,开展对于大学生社交网络成瘾的相关研究。方法·在上海市某高校一、二年级本科生中开展关于大学生社交网络成瘾的横断面问卷调查,采用Logistic回归分析性别、年级、学习压力、自尊、孤独感、抑郁、困顿感、挫败感、人际需求、社会支持、吸烟、饮酒、运动和学习成绩对社交网络成瘾的影响。以问题行为理论为理论框架,构建大学生社交网络成瘾的理论框架模型。结果·60.31%(591/980)的低年级大学生有社交网络成瘾情况。单因素Logistic回归结果显示:抑郁、自尊、孤独感、挫败感、困顿感、社会支持、人际需求、运动和学习成绩对社交网络成瘾有显著影响。研究构建的大学生社交网络成瘾的结构方程模型拟合结果良好[S-Bχ^(2)/df=8.03,拟合指数(goodness-of-fit index,GFI)=0.924,比较拟合指数(comparative fit index,CFI)=0.909,非规范拟合指数(Tucker-Lewis index,TLI)=0.872,近似误差均方根(root mean square error of approximation,RMSEA)=0.096,标准化残差均方根(standardized root mean square residual,SRMR)=0.070],提示人格系统与社会环境系统之间、人格系统与行为系统之间、社会环境系统与行为系统之间均相互影响(β=1.018,P=0.000;β=0.218,P=0.003;β=0.268,P=0.000)。人格系统和行为系统对社交网络成瘾的影响在统计学上不存在显著性,社会环境系统对社交网络成瘾有显著的正向影响(β=0.481,P=0.001)。结论·人格系统和行为系统通过影响社会环境系统间接影响社交网络成瘾,社会环境系统直接影响社交网络成瘾。对于低年级大学生社交网络成瘾问题,应当充分尊重大学生的特点,从系统3个层面共同入手,降低大学生社交网络成瘾风险。

乡村农旅产业行动者社交网络结构特征研究--以珠三角A市H镇乡村为例

乡村振兴重点是产业振兴,在资金、政策等投入下,投资有效性无法保证。乡村产业规模小,个体行动者在产业中影响大,引入行动者网络和社交网络分析,解读村域尺度下各类行动者如何影响乡村产业成败。首先,用行动者网络绘制分析图谱;其次,用社交网络分析主导能力、稳定性、关联性,分析网络模型的结构特征;最后,运用个体质性分析。结果:一,行动者的投入空间聚集明显,网络呈现线性与复杂性;二,成功产业网络具有结构紧密、整体关联性强且较为稳定的特征;三,行动者支持程度与投入个人(团体)的协调能力对成败影响最大。