Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks

Graph theoretical analysis of structural and functional connectivity MRI data (ie. diffusion tractography or cortical volume correlation and resting-state or task-related (effective) fMRI, respectively) has provided new measures of human brain organization in vivo. The most striking discovery is that the whole-brain network exhibits “small-world” properties shared with many other complex systems (social, technological, information, biological). This topology allows a high efficiency at different spatial and temporal scale with a very low wiring and energy cost. Its modular organization also allows for a high level of adaptation. In addition, degree distribution of brain networks demonstrates highly connected hubs that are crucial for the whole-network functioning. Many of these hubs have been identified in regions previously defined as belonging to the default-mode network (potentially explaining the high basal metabolism of this network) and the attentional networks. This could explain the crucial role of these hub regions in physiology (task-related fMRI data) as well as in pathophysiology. Indeed, such topological definition provides a reliable framework for predicting behavioral consequences of focal or multifocal lesions such as stroke, tumors or multiple sclerosis. It also brings new insights into a better understanding of pathophysiology of many neurological or psychiatric diseases affecting specific local or global brain networks such as epilepsy, Alzheimer’s disease or schizophrenia. Graph theoretical analysis of connectivity MRI data provides an outstanding framework to merge anatomical and functional data in order to better understand brain pathologies.     

两种新的电网连通性分析快速算法

不同于以往基于网络节点的算法,在简单数据结构的基础上,依次提出了两种算法效率与网络节点半相关(节点标记算法)及与网络节点完全无关(往返替换算法)的快速算法.理论分析和实例表明了这两种算法具有编程简单,不舍乘法运算,需求存储空间小,对网络结构改变适应性良好的藉点,大大缩短了电网连通性判别所需时间.     

Functional connectivity dynamics among cortical neurons: a dependence analysis

This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100-1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quantification of functional connectivity under identical conditions than cross correlation (CC) and phase synchronization (PhS) when the sample size is 1 s. The results of the simulated analysis are extended to real neuronal recordings to assess the functional connectivity in monkey's cortex corresponding to three movement states in a food reaching task and construct the assembly graph given a movement state and the activation degree of a state-related assembly over time using the statistical test exclusively from neural data dependencies. The activation degree of a given state-related assembly reaches the peak repeatedly when the specific movement states occur, which also reveals the network of interactions among the neurons are key for the operation of a specific behavior.     

混流式水轮机尾水管压力脉动换算初探

针对混流式水轮机尾水管压力脉动换算问题,本文提出了一种基于零环量单位流量的相对流量和基于叶片出口速度头压力脉动相对幅值来预测原型机尾水管进口压力脉动的方法。首先通过速度三角形进行了理论分析,并以某电站尾水管锥管压力脉动为例,分别在模型水轮机和相似原型机上比较了本文推荐方法与标准推荐方法的优缺点。之后采用该方法对12个电站水轮机模型和相似原型机尾水管压力脉动试验结果进行比较,模型和原型表现很好的相似性。最后在原型机上比较了基于本文方法的预测结果和现场测量结果,两者吻合性较好,证明了该方法的可行性。     

RF MEMS for ubiquitous wireless connectivity. Part I. Fabrication

The aim of this article to expose the impact and status of the application of RF-MEMS switchable capacitors, varactors and switches in the three elements of this paradigm, namely, handsets, base stations, and satellites. In particular, issues such as system-level motivation/justification for RF MEMS, device requirements, high-volume manufacturing, packaging, and state-of-the-art performance and reliability, are presented.     

RF MEMS for ubiquitous wireless connectivity. Part II. Application

This paper reports on RF-MEMS switchable capacitors, varactors, and ohmic switch technology attribute for both base stations and handsets in realizing frequency-agile RF/wireless systems capable of serving multiple frequency bands. For the handset, this leads to a smaller footprint combined with low power consumption of the RF radio. For the base station the benefit lies in ability for reconfiguration of the air interface, which leads to high logistical savings for infrastructure vendors through a reduction in the number of product variants. Reconfigurable frequency-agile radios are a perfect addition to reconfigurable baseband processing. Both together form the basis of a realistic and reasonable approach to realize software radios.     

Group ICA of resting-state data: a comparison

Objective

Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. [7], and (2) PICA, proposed by Beckmann et al. [3]. Both methods are based on the assumption of statistical independence of the extracted component maps (“spatial ICA”). Group maps are estimated via ICA on pre-calculated group data sets.

Material and Methods

In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load.

Results

At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses

Conclusion

Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times.     

Distributed cooperative formation of multiple mobile agents with preserved connectivity

This paper investigates distributed cooperative formation control of a group of multiple mobile agents with a virtual leader, where information exchange among agents is modeled by the group topology, and the states of the virtual leader are known only by parts of the agents.We develop a class of distributed formation control laws with similar form. The steered group is proved to achieve the desired formation objectives as long as the intersection of the initial communication topology and the formation goal topology is connected. This requirement of connectivity can be easily achieved by many practical applications; consequently, our developed distributed control laws are effective and feasible. Furthermore, for the developed control laws, we show the influence of different information flow graph of agents on the convergence rate and robustness to node and connection failures.     

一种基于ICA的图像信息隐藏算法

独立分量分析（ICA）是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,已广泛应用于信号处理领域。信息隐藏是一种新兴的技术,其目的在于将秘密信息隐藏于另一非机密信息中。本文提出一种新的信息隐藏技术,即将Arnold置乱后的秘密图像嵌入到载体图像中,再利用ICA算法从中提取出秘密图像。仿真结果表明该算法有效可行。     

独立分量分析在输电塔防盗监测系统中的应用

在电磁干扰下检测出因不良企图引起的铁塔振动信号,防止塔材被盗事件的发生,对保障电网安全运行有着重要意义。文中介绍了一种基于独立分量分析（ICA）的输电塔防盗监测系统。ICA方法能够在没有源信号和传输通道参数先验知识的情况下,按照统计独立的原则,通过选择判据和优化算法将信号分解成若干独立的源成分,适合于该系统中非平稳振动信号的提取。系统在硬件方面采取了相应的抗干扰措施,有效降低了电磁等干扰。采用负熵的FastICA方法和探查性投影追踪信号处理算法快速地对铁塔盗窃中产生的振动信号进行了较好的提取和分离,结合自适应阈值脉冲提取算法,有效地提取振动脉冲成分,降低了噪声干扰。实验结果表明该系统能够较好地检测出铁塔盗窃中产生的振动信号。     

远程备用智能保护一体化中心架构通信系统连通可靠性研究

针对双重化智能变电站站域保护在检修、故障退出、灾变恢复等多种场景时可靠性不充分的问题,提出远程备用智能保护一体化中心(Remote Duplicate Configuration Smart Protection Center, RDCSPC)的继电保护新架构。因其建立在异地,必须考虑其通信系统的可靠性。采用Markov状态空间法对RDCSPC架构通信系统各子模块进行建模,确定模型各状态之间失效率、修复率逻辑关系,得出各子模块及RDCSPC架构通信系统两种组网下的可靠性指标表达式。通过算例分析计算得到可靠性指标具体数值。算例结果验证了RDCSPC架构通信系统在两种组网下具有较高的可靠性。在此基础上进一步讨论了一个RDCSPC所辖几座变电站更合理。     

Estimation of the cortical connectivity patterns during the intention of limb movements.

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during recent years as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. In this article, we propose the use of the directed transfer function (DTF) method on cortical signals estimated from high-resolution electroencephalography (EEG) recordings. An application of the proposed technique to the estimation of the cortical connectivity pattern in normal subjects and in one spinal-cord-injured patient is also provided.     

基于Bootstrap方法的独立分量分析可靠性研究

语音信号盲分离受到随机采样的数据所影响,产生统计可靠性问题,从而影响分离的效果.Bootstrap方法是以原始数据为基础的抽样统计推断法,可方便的应用于实际的数据处理之中,分析ICA的统计可靠性,但传统Bootstrap方法本身的计算特性限制了自助样本的生成范围,从而使得自助概率分布产生了一定的偏离,使之无法渐近于真实情形.本文通过对Bootstrap样本生成范围的拓展,改进了独立分量分析在语音信号应用的可靠性算法,获得了更加精确的可靠性参数.     

基于独立分量分析的交流采样信号分离技术的研究

实现电力系统自动化的过程中,数据采集是一个重要因素,交流采样实时性好,效率高,相位失真小,逐步成为电力系统主要的数据采集方式.但交流采样数据采集过程中存在多次谐波干扰,影响数据采集的精度与稳定,提出了一种基于FASTICA算法的交流采样信号分离方法,经数据分析和处理表明,该算法有效地还原了各次谐波,减小了工频失真度,提高了数据的采集精度,为数据采集设备提供了合理的测试方案.     

基于独立分量分析盲源分离快速算法

独立分量分析(ICA)是一种统计方法,在信号处理方面有着广泛的应用.文中介绍ICA的基本原理,探讨快速的定点算法,并给出了仿真结果.     

A historical perspective on the evolution of resting-state functional connectivity with MRI

In this review article, I discuss initial and recent studies of the assessment of functional connectivity in the human brain using low-frequency BOLD fluctuations in the resting state. By putting the studies in their historical context, the goal is to give the reader an appreciation of the evolution of the field and the pivotal events and studies that have led to the widespread acceptance of the method as a neuroscience tool for investigating functional connectivity in the human brain.     

Finite difference modeling of piezoelectric composite transducers with 0-3 connectivity

The mechanical and electrical properties of transducers are modelled by finite-difference representations of the relevant physical equations and the numerical problems are discussed. The theory is applied to a transducer consisting of a calcium-modified lead titanate (PTCa) ceramic mixed with the polymer P(VDF-TrFE), and comparison of /spl isin//sub 33/ with experimental values is found to be good. The introduction of ceramic into the mix has an immediate effect on the voltage, and s/sub 11/ and s/sub 12/. d/sub 31/ is not much affected by the presence of the ceramic until it reaches 70% of the mixture whereas d/sub 33/ changes sign rather quickly and then increases more slowly. A second transducer having the ceramic PZT5 mixed with P(VDF-TrFE) is also modelled and the characteristics of the 0-3 structure are compared with those of the 1-3 structure of the same components modelled in an earlier paper. The 1-3 construction brings the effect of the ceramic in much faster than the 0-3 construction. For example, at 50% ceramic, e/sub 33/, d/sub 3l/ and d/sub 33/ are roughly trebled compared to the 0-3 values.     

一种基于ICA和DWT的数字图像水印算法

数字水印是图像处理领域研究的一个热点。独立分量分析（ICA）是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,已广泛应用于信号处理领域。提出了一种结合独立分量分析（ICA）和离散小波变换（DWT）的数字图像水印方法。该算法将原始图像进行小波分解,然后在小波逼近子图嵌入置乱后的水印图像。数字水印的检测使用了快速独立分量分析（FastICA）的方法,最后对分离出的水印进行增强处理。仿真结果表明该算法有效可行,具有较强的鲁棒性和隐蔽性。     

Surfing the brain.

In this paper, an overview with unification of techniques that deploy the wavelet transform in the spatial domain for the analysis of functional magnetic resonance imaging (fMRI) is presented.