睡眠剥夺下人脑功能脑网络分析

在静息态和睡眠剥夺下分别获取了人脑f MRI数据,通过小波变换提取功能磁共振的时间序列,计算人脑116个脑区的相关性,发现在睡眠剥夺下人脑功能连接相较于静息态下有明显的变化,连接强度、聚类系数、特征路径长度、网络效率、小世界特性都有明显的变化;睡眠剥夺下脑区点效率变化在大脑脑区的激活增强居多,大脑激活补偿增强,而在小脑脑区点效率减弱比较明显,且影响小脑脑区数量相对于大脑更多,这表明睡眠剥夺对小脑影响比大脑更加明显。     

基于静息态fMRI的人脑功能网络的小世界特性

从19个健康志愿者静息态的功能磁共振成像中提取了时间序列,计算了90个脑区的功能相关性,设定阈值建立脑功能网络的无向简单图,然后计算了一些网络测度.结果显示人脑功能网络具有小世界特性;性别分层后并没有发现各网络测度在全脑水平有显著性的性别差异.     

基于复杂网络的三种眼睛状态下脑功能连通性比较

尝试构建不同眼睛状态下的静息态脑功能网络,研究睁眼、闭眼和自由状态下的大脑网络拓扑结构差异.招募了13例被试者,用一台3T的西门子磁共振仪,获取了3种眼睛静息状态下各8min的功能磁共振数据,并同时采集了结构像.随后,构建3组被试者的功能连通矩阵,计算其复杂网络的全局特征系数、局部特征系数等测度参数.对测度进行方差分析与双样本t检验,以统计分析被试者之间的差异.我们发现,所有的状态在合适的稀疏度下,展现了小世界特性;相比于其他状态,闭眼状态下的全局效率较低.自由状态在复杂网络的各项测度上接近睁眼状态.综上所述,该项研究展现了睁眼、闭眼、自由状态对人脑功能连通网络的影响,并为眼睛状态如何影响功能磁共振实验提供了关键的证据.     

静息状态下脑网络建模及功能连接特性

实验采集静息状态下功能磁共振成像数据,经预处理后结合种子相关分析方法、t-检验法以及复杂网络理论和方法构建正常人脑功能网络.针对脑网络构建中种子相关分析法存在阈值设定随意性大的问题,引入两个原则,即设定的阈值需保证网络的整体性和小世界特性,使建立的脑网络模型充分具有实际系统的特征.在建模基础上进一步研究脑网络功能连接特性,发现网络具有明显的小世界特性;并通过计算网络中心化指标推测出后扣带回、楔前叶、楔叶以及顶上小叶等是静息状态下脑功能网络的关键脑区.     

基于rs-fMRI数据的脑功能网络构建与分析

从脑网络的角度研究大脑功能脑区之间的连接关系,对于理解大脑的工作方式乃至探究精神疾病的病理机制具有重要意义。本文基于静息态功能磁共振成像(rs-fMRI)数据,计算264个脑区间的相关性,提出了3个合理的假设来确定相关系数阈值,构建出相应的脑功能网络。通过计算网络的聚类系数和平均最短路径长度等属性,结果表明脑功能网络具有小世界特性。针对脑区节点数大于信号时间序列长度情况下的偏相关计算,提出了一种矩阵变换法,获得脑区间的偏相关系数,能够消除其他节点的间接影响。最后在标准脑图上实现了脑功能网络连接关系的可视化。实验证明本文的构建和分析算法是可行的,为脑功能网络分析提供了有益的探索。     

基于脑电的磁刺激神门穴脑功能网络研究

研究磁刺激神门穴状态下与静息状态下9名健康被试的脑电信号样本,分别构建两种状态下的脑功能网络,并对不同的网络测度进行分析.首次从复杂网络角度探索神门穴的治病机理,期望为穴位磁刺激疗法提供实验参考.分析结果表明磁刺激神门穴时脑电信号之间的关联系数值相比静息状态下降低,平均聚类系数、平均最短特征路径长度以及平均度在阈值区间均发生显著变化,小世界属性指标增强,并存在显著性差异(P<0.01).磁刺激神门穴状态下各节点通道间的紧密程度降低、连接情况较为松散、网络中各节点同步能力减弱、效率降低、网络规模减小,这时的大脑很可能处于一种比较镇静的状态而有利于睡眠的产生.     

静息态亚健康失眠脑功能网络分析

针对亚健康失眠者根据匹兹堡睡眠质量指数筛选被试参与实验,利用128导脑电（EEG）分析仪,提取静息态64导脑电信号,通过多通道脑电信号同步性分析,脑功能网络的构建和分析,研究亚健康失眠者与健康人脑电信号的特异性差异,同时进一步比较分析亚健康失眠者与健康人脑电的负相关特性.分析结果表明亚健康失眠者与健康人相比,脑电信号同步性降低,脑功能网络的连接减弱,大脑的活跃度降低,并且这种差异性在脑电负相关特性中表现更为明显.     

脑疲劳状态下脑功能网络研究与分析

旨在研究连续长时间脑力活动引发的脑疲劳对大脑连接性的影响,探索大脑疲劳评价的客观指标.通过持续认知任务实验诱发脑疲劳,选用互相关方法对采集到的脑电信号进行了不同导联间时域关联特性分析,构建并比较分析了正常态和脑疲劳态的脑功能网络.最后基于复杂网络理论对脑功能网络的特征参数进行了统计分析.结果表明,持续认知任务后,主观感觉疲劳程度显著增加,脑功能网络的平均度、平均聚类系数和网络密度与正常态相比均显著降低,而平均路径长度显著增大.脑功能网络参数可以很好地反映脑疲劳后大脑的连接性变化情况.     

任务背景下腹侧注意功能网络的fMRI研究

基于静息状态的功能磁共振成像(functional MRI,fMRI)已经成为当前人脑功能研究的重要手段之一,本研究采用任务背景来获取一种更为"纯净"的静息状态,其中脑区的"任务背景"被定义为那些不会激活感兴趣脑区的任务.由于到目前为止还不清楚在"任务背景"下人脑除默认网络外的其他"高级"功能网络内部是否被中断,本文研究了人脑听觉背景下腹侧注意网络内部的功能连接.结果表明在听觉背景下该网络内部存在显著的功能连接,这一结果说明简单的感觉任务不会中断人脑的腹侧注意网络.本研究首次证明在任务背景下,除默认网络之外,人脑还有一些高级功能网络内部存在显著功能连接.     

镇江公交网络静态特性分析

城市公交系统在城市交通中具有重要作用,城市公交网络的静态特性对公交系统的性能具有重要影响。针对镇江公交特点,采用公交站点网络（SpaceL）方法对镇江公交网络建模,通过计算节点度、路径长度和聚集系数及其分布规律,分析镇江公交网络的静态特性。结果表明,镇江公交站点网络节点度分布服从指数分布,平均路径长度为14．9019,平均聚集系数为0．1039,具有明显的小世界网络特性。     

Deactivations during the numerical processing

Deactivation has been encountered frequently in functional brain imaging researches. However, the deactivations during the numerical processing have not been reported yet. In this study, the functional magnetic resonance imaging (fMRI) was employed to investigate the pattern of the deactivation in the brain of 15 healthy subjects during the numerical addition task. Analyses revealed significant deactivations in several brain regions, including the posterior cingulate, precuneus, anterior cingulate and prefrontal cortex. Especially, we found notable deactivation in bilateral insula. Accounting for the cognitive functions of these regions participating in a combinated way, we discuss their contributions in sustaining the brain activity during conscious resting state, and indicate that the insula is an important area of gathering auditory information from the external world.     

Resting state brain activity and functional brain mapping

Functional brain imaging studies commonly use either resting or passive task states as their control conditions, and typically identify the activation brain region associated with a specific task by subtracting the resting from the active task conditions. Numerous studies now suggest, however, that the resting state may not reflect true mental “rest” conditions. The mental activity that occurs during “rest” might therefore greatly influence the functional neuroimaging observations that are collected through the usual subtracting analysis strategies. Exploring the ongoing mental processes that occur during resting conditions is thus of particular importance for deciphering functional brain mapping results and obtaining a more comprehensive understanding of human brain functions. In this review article, we will mainly focus on the discussion of the current research background of functional brain mapping at resting state and the physiological significance of the available neuroimaging data.     

Resting state brain activity and functional brain mapping

Functional brain imaging studies commonly use either resting or passive task states as their control conditions, and typically identify the activation brain region associated with a specific task by subtracting the resting from the active task conditions. Numerous studies now suggest, however, that the resting state may not reflect true mental "rest" conditions. The mental activity that occurs during "rest" might therefore greatly influence the functional neuroimaging observations that are collected through the usual subtracting analysis strategies. Exploring the ongoing mental processes that occur during resting conditions is thus of particular importance for deciphering functional brain mapping results and obtaining a more comprehensive understanding of human brain functions. In this review article, we will mainly focus on the discussion of the current research background of functional brain mapping at resting state and the physiological significance of the available neuroimaging data.     

Detection of dynamic brain networks modulated by acupuncture using a graph theory model

Neuroimaging studies involving acute acupuncture manipulation have already demonstrated significant modulatory effects on wide limbic/paralimbic nuclei, subcortical gray structures and the neocortical system of the brain. Due to the sustained effect of acupuncture, however, knowledge on the organization of such large-scale cortical networks behind the active needle stimulation phase is lacking. In this study, we originally adopted a network model analysis from graph theory to evaluate the functional connectivity among multiple brain regions during the post-stimulus phase. Evidence from our findings clearly supported the existence of a large organized functional connectivity network related to acupuncture function in the resting brain. More importantly, acupuncture can change such a network into a functional state underlying both pain perception and modulation, which is exhibited by significant changes in the functional connectivity of some brain regions. This analysis may help us to better understand the long-lasting effects of acupuncture on brain function, as well as the potential benefits of clinical treatments.     

Finer discrimination of brain activation with local multivariate distance

The organization of human brain function is diverse on different spatial scales. Various cognitive states are always represented as distinct activity patterns across the specific brain region on fine scales. Conventional univariate analysis of functional MRI data seeks to determine how a particular cognitive state is encoded in brain activity by analyzing each voxel separately without considering the fine-scale patterns information contained in the local brain regions. In this paper, a local multivariate distance mapping (LMDM) technique is proposed to detect the brain activation and to map the fine-scale brain activity patterns. LMDM directly represents the local brain activity with the patterns across multiple voxels rather than individual voxels, and it employs the multivariate distance between different patterns to discriminate the brain state on fine scales. Experiments with simulated and real fMRI data demonstrate that LMDM technique can dramatically increase the sensitivity of the detection for the fine-scale brain activity patterns which contain the subtle information of the experimental conditions.     

Finer discrimination of brain activation with local multivariate distance

The organization of human brain function is diverse on different spatial scales. Various cognitive states are always represented as distinct activity patterns across the specific brain region on fine scales. Conventional univariate analysis of functional MRI data seeks to determine how a particular cognitive state is encoded in brain activity by analyzing each voxel separately without considering the fine-scale patterns information contained in the local brain regions. In this paper, a local multivariate distance mapping (LMDM) technique is proposed to detect the brain activation and to map the fine-scale brain activity patterns. LMDM directly represents the local brain activity with the patterns across multiple voxels rather than individual voxels, and it employs the multivariate distance between different patterns to discriminate the brain state on fine scales. Experiments with simulated and real fMRI data demonstrate that LMDM technique can dramatically increase the sensitivity of the detection for the fine-scale brain activity patterns which contain the subtle information of the experimental conditions.