静息状态下脑功能连接的磁共振成像研究

静息状态下脑功能连接的磁共振成像研究近年来取得了迅猛发展. 通过对fMRI信号低频涨落成分的同步性分析,可以得到大脑静息态任意脑区的功能连接和多套网络系统,其中“默认网络”的发现可能为人脑固有网络的研究提供新的思路. 而静息态网络与解剖连接之间可能存在的对应,以及在神经精神疾病患者脑中性质和连接的异常改变,使其具有重要的研究和临床应用价值. 该文总结了静息状态功能磁共振成像的主要研究成果,对静息状态脑功能网络的发现和发展、研究方法、各网络及其特点以及在临床方面的应用进行简单的介绍和分析.     

时变动态贝叶斯网络模型及其在皮层脑电网络连接中的应用

大脑执行语言的发音需要顶叶、颞叶、额叶等多个脑区协同完成.皮层脑电具有高时间分辨率、较高空间分辨率和高信噪比等优势,为研究大脑的电生理特性提供了重要的技术手段.为了探索大脑对语言的动态处理过程,利用多尺度皮层脑电(标准电极与微电极)分析了被试在执行音节朗读任务时的皮层脑电信号的高频gamma段特征,提出采用时变动态贝叶斯网络构建单次实验任务的有向网络.结果显示该方法能够快速有效地构建语言任务过程中标准电极、微电极以及二者之间的有向网络连接,且反映了大规模网络(标准电极之间的连接)、局部网络(微电极之间的连接)以及大规模网络与局部网络之间的连接(标准电极与微电极之间的连接)随语言任务发生的动态改变.研究还发现,发音时刻之前与之后的网络连接存在显著性差异,且发音方式不同的音节网络间也存在明显差异.该研究将有助于癫痫等神经疾病的术前临床评估以及理解大脑对语言加工的实时处理过程.     

基于持久同调的复杂脑网络动态演化分析

有研究表明阿尔茨海默病（Alzheimer's disease,AD）的认知状态与动态功能连接时间特性的改变有关,持久同调指标分析方法可为AD动态脑网络的研究提供更深的见解,但是目前研究主要集中在空间演化方面,尚未有针对时变方面的脑网络演化研究．本文基于静息态功能磁共振成像（resting state-functional magnetic resonanceimaging,rs-fMRI）,对AD患者和正常被试（normal controls,NC）的静态脑网络和基于滑动窗口构建的动态脑网络进行功能连接性分析．对基于持久同调和基于图论的分析结果进行了比较,并采用k均值聚类进行了时间属性的分析．结果表明相对图论指标,持久同调的指标在AD患者和NC被试间具有更显著的差异性;而且相对于静态脑网络,基于持久同调的动态脑网络演化分析可为脑功能网络标志物的检测提供新思路．     

基于因子分析方法的相位同步脑电源的时-空动力学分析

头皮脑电时间序列的相关性是大脑皮层源的相位同步性的一种体现,因此对相位同步源进行定位,同时找到源对应的时间序列在脑成像研究领域具有重要意义.基于Rössler 模型提出仿真相位同步偶极子源的时间序列的方法,利用时间序列进行同心四层球头模型正演,获得仿真头皮脑电数据.提出了基于最大似然因子分析的相位同步脑电源的时-空动力学分析方法,对仿真和真实头皮脑电数据进行了验证,并与主成分分析法进行对比.仿真实验结果表明:最大似然因子分析法估计的时间序列与仿真源的时间序列具有更高的相关系数,同时估计源与仿真源 关键词： 脑电图 相位同步 因子分析 主成分分析     

基于时间序列的网络失效模型

随着网络科学的发展,静态网络已不能清晰刻画网络的动态过程.在现实网络中,个体之间的交互随时间而快速演化.这种网络模式将时间与交互过程紧密联系,能够清晰刻画节点的动态过程.因此,如何更好地基于时间序列刻画网络行为变化是现有级联失效研究的重要问题.为了更好地研究该问题,本文提出一种基于时间序列的失效模型.通过随机攻击某时刻的节点,分析了时间、激活比例、连边数、连接概率4个参数对失效的影响并发现网络相变现象.同时为验证该模型的有效性与科学性,采用真实网络进行研究.实验表明,该模型兼顾时序以及传播动力学,具有较好的可行性,为解释现实动态网络的级联传播提供了参考.     

基于自适应模板法的脑电信号转移熵分析

脑电信号是一种产生机理相当复杂且非常微弱的随机信号, 综合反映了大脑组织的脑电活动及大脑的功能状态. 由于脑电信号的微弱性, 传统的基本模板方法在脑电信号分析上得到了良好的应用. 为进一步提升分析脑电信号的性能, 提出了一种新的基于自适应模板的转移熵方法并分析了青少年脑电与成年人脑电信号. 结果表明: 对于青少年脑电还是成年人脑电, 与基本模板法相比, 基于自适应模板法的转移熵可以更显著地表示脑电信号的耦合作用, 并且具有更好的区分度, 这将能更好地捕捉到信号中的动态信息、系统动力学复杂性的改变. 同时, 该方法将更有利于医学临床诊断的辅助检测, 对脑电信号是否处于病理状态的诊断提供了新的更好的判断依据.     

基于Kendall改进的同步算法癫痫脑网络分析

提出了一种基于Kendall等级相关改进的同步算法IRC(inverse rank correlation).Kendall等级相关是非线性动力学分析的一般化算法,可有效地度量变量间的非线性相关性.复杂网络的研究已逐渐深入到社会科学的各个领域,脑网络的研究已经成为当今脑功能研究的热点.利用改进的IRC算法,基于脑电EEG(electroencephalogram)数据来构建大脑功能性网络.对构建的脑功能网络的度指标进行了分析,以调查癫痫脑功能网络是否异于正常人.结果显示:使用该改进的算法能够对癫痫和正常脑功能网络显著区分,且只需要记录很短的脑电数据.实验结果数据表明,该方法适用于区分癫痫和正常脑组织网络度指标,它可有助于进一步地加深对大脑的神经动力学行为的研究,并为临床诊断提供有效工具.     

人脑默认模式网络的动力学行为

大脑具有自适应、自组织、多稳态等重要特征,是典型的复杂系统.人脑在静息态下的关键功能子网络--默认模式网络(DMN)的激活处于多状态间持续跳转的非平衡过程,揭示该过程背后的动力学机制具有重要的科学意义和临床应用前景.本文基于功能磁共振获得的血氧水平依赖(BOLD)信号,建立了DMN吸引子跳转非平衡过程的能量图景、吸引子非联通图、跳转关系网络等;以高级视觉皮层和听觉等皮层活动为例,通过对应激活DMN状态空间的分布,以及XGBoost、深度神经网络等算法验证了DMN状态变化与外部脑区状态的密切依赖关系;通过偏相关、收敛交叉映射等方法分析了DMN内各个脑区之间的相互作用.本文结果有助于理解静息态下大脑内在非平衡过程的动力学机制,以及从动力学的角度探索具有临床意义的脑功能障碍生物标志物.     

基于抑制性突触可塑性的神经元放电率自稳态机制

神经元放电率自稳态是指大脑神经网络的放电率维持在相对稳定的状态.大量实验研究发现神经元放电率自稳态是神经电活动的重要特征,并且放电率自稳态是实现神经信息处理及维持正常脑功能的基础,因此放电率自稳态的研究是神经科学领域的重要科学问题.脑神经网络是一个高度复杂的动态系统,存在大量输入扰动信号及由于动态链接导致的参数摄动,因此如何建立并维持神经元放电率自稳态及其鲁棒性仍有待深入研究.反馈神经回路是皮层神经网络的典型连接模式,抑制性突触可塑性对神经元放电率自稳态具有重要的调控作用.本文通过构建包含抑制性突触可塑性的反馈神经回路模型对神经元放电率自稳态及其鲁棒性进行计算研究.结果表明:在抑制性突触可塑性的作用下,神经元放电率可自适应地跟踪目标放电率,从而取得放电率自稳态;在有外部输入干扰和参数摄动的情况下,神经元放电率具有良好的抗扰动性能,表明放电率自稳态具有很强的鲁棒性;理论分析揭示了抑制性突触可塑性学习规则是神经元放电率自稳态的神经机制;仿真分析进一步揭示了学习率及目标放电率对放电率自稳态建立过程具有重要影响.     

变权小世界生物神经网络的兴奋及优化特性

根据实际生物神经网络具有小世界连接和神经元之间的连接强度随时间变化的特点,首先构造了一个以Hodgkin-Huxley方程为节点动力学模型的动态变权小世界生物神经网络模型,然后研究了该模型神经元的兴奋特性、权值变化特点和不同的学习系数对神经元的兴奋统计特性的影响.最有意义的结果是,在同样的网络结构、网络参数及外部刺激信号的条件下,学习系数b存在一个最优值b^*,使生物神经网络的兴奋度在b=b^*时达到最大. 关键词： 动态变权生物神经网络 小世界网络 Hodgkin-Huxley方程     

A SVM-based quantitative fMRI method for resting-state functional network detection

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.     

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.     

The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks

In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.     

Detecting functional connectivity in the resting brain: a comparison between ICA and CCA

Independent component analysis (ICA) and cross-correlation analysis (CCA) are general tools for detecting resting-state functional connectivity. In this study, we jointly evaluated these two approaches based on simulated data and in vivo functional magnetic resonance imaging data acquired from 10 resting healthy subjects. The influence of the number of independent components (maps) on the results of ICA was investigated. The influence of the selection of the seeds on the results of CCA was also examined. Our results reveal that significant differences between these two approaches exist. The performance of ICA is superior as compared with that of CCA; in addition, the performance of ICA is not significantly affected by structured noise over a relatively large range. The results of ICA could be affected by the number of independent components if this number is too small, however. Converting the spatially independent maps of ICA into z maps for thresholding tends to overestimate the false-positive rate. However, the overestimation is not very severe and may be acceptable in most cases. The results of CCA are dependent on seeds location. Seeds selected based on different criteria will significantly affect connectivity maps.     

A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM. To compare the effectiveness of our approach with traditional pair-wise GCM models, we applied a well-established conditional GCM to preselected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis of an fMRI data set in the temporal domain. Data sets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM-detected brain activation regions in the emotion-related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state data set, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network that can be characterized as both afferent and efferent influences on the medial prefrontal cortex and posterior cingulate cortex. These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy in detecting network connectivity than the widely used pair-wise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.     

Construction and Application of Functional Brain Network Based on Entropy

Functional brain network (FBN) is an intuitive expression of the dynamic neural activity interaction between different neurons, neuron clusters, or cerebral cortex regions. It can characterize the brain network topology and dynamic properties. The method of building an FBN to characterize the features of the brain network accurately and effectively is a challenging subject. Entropy can effectively describe the complexity, non-linearity, and uncertainty of electroencephalogram (EEG) signals. As a relatively new research direction, the research of the FBN construction method based on EEG data of fatigue driving has broad prospects. Therefore, it is of great significance to study the entropy-based FBN construction. We focus on selecting appropriate entropy features to characterize EEG signals and construct an FBN. On the real data set of fatigue driving, FBN models based on different entropies are constructed to identify the state of fatigue driving. Through analyzing network measurement indicators, the experiment shows that the FBN model based on fuzzy entropy can achieve excellent classification recognition rate and good classification stability. In addition, when compared with the other model based on the same data set, our model could obtain a higher accuracy and more stable classification results even if the length of the intercepted EEG signal is different.     

面向下一代航电系统的数据块填装性能

结合光分组交换(OPS)网络和光纤通道(FC)技术的优势, 提出一种下一代航电系统组网方案——基于光分组交换网络的光纤通道技术(FC over OPS)。建立了数学仿真模型, 研究了一种数据块填装算法与网络实时性之间的关系, 分析不同的参数如发送带宽、最低效率门限、发送定时的选取对网络实时性的影响。进一步完成硬件原型设计和仿真, 比较了软件仿真与硬件仿真的结果, 并分析该数据块填装算法的性能。