复杂度脑电地形图研究

脑电地形图是近年脑电分析的热点之一。通过对各种复杂度算法的分析得出,近似熵由于所需要的时间序列长度较短,大大减少了脑电非平稳性所带来的困难,且无需粗粒化,在对生物医学信号的复杂度分析中有其一定的优点,采用近似熵对多道脑电信号的复杂度运算结果,通过空间插值,构建复杂性动态脑地形图,以便于观察大脑各部ＥＥＧ信号复杂度在同一时刻的相对强弱关系和这种关系随时间的变化。并通过对一些脑疾病患者脑电数据的分析,     

大脑皮层信息传输和精神分裂症

本工作用脑电图为测试手段,比较了正常人与精神分裂症病人的大脑皮层的信息传输。我们发现精神分裂病人的大脑皮层信息传输有非常特殊的现象。正常人在睁眼时大脑皮层信息传输比较活跃,当闭眼时信息传输相对减少。而精神分裂症病人则恰好相反。闭眼时信息传输很活跃而睁眼对它们产生抑制,严重的情况可与正常人深度睡眠时类似。经过近三百多例的统计分析这种差别是非常显著的。我们认为这种方法可能作为诊断精神分裂症的客观指标。     

不同生理状态时脑电时间序列的三神复杂度计算比较

为了研究不同生理状态下的脑电复杂度变化特点,本文依照１９９４年徐京等人应用的算法,对４种状态（安静睁眼,清醒闭目,浅度睡眠,深度睡眠）下的三种脑电复杂度（Ｋｃ,Ｃ１,Ｃ２）的变化规律进行了比较分析,Ｋｃ与Ｃ１的变化相一致,从安静睁眼剂的清醒闭目到浅睡到深度睡眠,Ｋｃ与Ｃ１值均依次下降,Ｃ２值的变化则与它们相反,尤其在深睡期显著升高,实验结果提示,复杂度可做为脑电时间序列的研究指标。     

不同生理状态时脑电时间序列的三神复杂度计算比较

为了研究不同生理状态下的脑电复杂度变化特点,本文依照１９９４年徐京华等人应用的算法,对４种状态（安静睁眼、清醒闭目、浅度睡眠、深度睡眠）下的三种脑电复杂度（ＫＣ、Ｃ１、２）的变化规律进行了比较分析,ＫＣ与Ｃ１的变化相一致。从安静睁眼到清醒闭目到浅睡到深度睡眠,ＫＣ与Ｃ１值均依次下降。Ｃ２值的变化则与它们相反,尤其在深睡期显著升高。实验结果提示,复杂度可做为脑电时间序列的研究指标。     

局限性癫痫脑电时间序列的三种复杂度计算比较

为探索非线性动力学复杂性测度诊断癫痫病的新方法,对局限性癫痫病患者脑电时间序列进行了三种复杂度（Ｋｃ、Ｃ１、Ｃ２）的计算比较。观察到,痫性导联脑电的三种复杂度多低于对侧导联的值;复杂度Ｋｃ的相对变化量较Ｃ１和Ｃ２大;复杂度Ｋｃ和Ｃ１的变化趋势相似,而复杂度Ｃ２的变化趋势与复杂度Ｋｃ和Ｃ１的规律不尽相同;但正常人ＥＥＧ信号的复杂度没有这种显著变化。结果提示,脑电复杂性测度有可能成为诊断癫痫的特征参数,值得进一步深入研究。     

脑电与认知活动的相关性

本文简要介绍了认知活动与脑电（包括自发脑电、诱发脑电、脑电事件相关电位、４０ＨＺ脑电事件相关电位）的相关性及其应用前景。     

失眠障碍与过度觉醒:来自静息态脑电和睡眠脑电的证据

失眠障碍已成为仅次于抑郁症的全球第二大流行性精神疾病.过度觉醒模型是解释失眠障碍维持机制的重要理论之一,而静息态脑电和睡眠脑电则为这一模型提供了最重要的证据支持.本文首先归纳了进行静息态和睡眠脑电分析的方法,并提出从清醒静息态、入睡到不同睡眠期的系统分析框架.通过分析前人的研究发现,失眠障碍过度觉醒的静息态和睡眠脑电证...     

一种新型的脑电位分布处理仪

我们研制的BKC-1型脑电处理仪,也称为脑电地形图仪。它是一种新颖的脑电信息处理仪器,采用高档微机(有高精度A/D转换接口)、精密的AR参数模型和快速傅立叶变换(FFT)相结合的脑电波处理软件。可直接与现有的国内外各种类型的脑电仪连接,能对多种脑电信号进行数据采集和处理。脑电位分布处理仪原理框图如图1所示。它用电子计算机对脑电图(EEG)信号进行处理,将脑电图的曲线波形转化成二维的彩色图象。同普通脑电图信号相比,它     

微弱电刺激对失眠者睡眠状况及睡眠脑电影响的初步研究

根据睡眠是由脑内亿万神经元同步振荡所刻划的观点［１］,及各种电刺激对动物睡眠影响的实验［２,３］,设计了用特定θ频率的正弦波微弱电流,刺激失眠病人颈部安眠２穴,以观察其对受试者脑电频率的客观影响。其结果是刺激后失眠病人由醒到２期的脑电记录中,θ波逐渐增加,增加了病人的总睡眠时间。这启示我们这种脑部的特殊频率微弱电流刺激,可能有引起脑部神经元群的共振现象,改变了受试者脑电中频率成分的分布特征,从而有助于失眠的治疗。这一现象是值得进一步研究的。     

基于奇异值第一主成分的睡眠脑电分期方法研究

目的:脑电信号含多种噪声和伪迹,信噪比较低,特征提取前必须进行复杂的预处理,严重影响睡眠分期的速度。鉴于此,本文提出一种基于奇异值第一主成分的睡眠脑电分期方法,该方法抗噪性能较强,可省去预处理过程,减少计算量,提高睡眠分期的效率。方法:对未经过预处理的睡眠脑电进行奇异系统分析,研究奇异谱曲线,提取奇异值第一主成分,探索其随睡眠状态变化的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果:奇异值第一主成分不仅能表征脑电信号主体,而且可以抑制噪声、降低维数。随着睡眠的深入,奇异值第一主成分的值逐渐增大,但在REM期处于S1期和S2期之间。经MIT-BIH睡眠数据库中5例同导联位置的脑电数据测试(仅1导脑电数据),睡眠脑电分期的准确率达到86.4%。结论:在未对脑电信号进行预处理的情况下,提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态,是一种有效的睡眠分期依据。本文运用提出的方法仅采用1导脑电数据,就能得到较为满意的睡眠分期结果。该方法有较强的分类性能,且抗噪能力强,不需要对脑电作复杂的预处理,计算量小,方法简单,很大程度上提高了睡眠分期的效率。     

Finding simplicity in complexity: general principles of biological and nonbiological organization

What differentiates the living from the nonliving? What is life? These are perennial questions that have occupied minds since the beginning of cultures. The search for a clear demarcation between animate and inanimate is a reflection of the human tendency to create borders, not only physical but also conceptual. It is obvious that what we call a living creature, either bacteria or organism, has distinct properties from those of the normally called nonliving. However, searching beyond dichotomies and from a global, more abstract, perspective on natural laws, a clear partition of matter into animate and inanimate becomes fuzzy. Based on concepts from a variety of fields of research, the emerging notion is that common principles of biological and nonbiological organization indicate that natural phenomena arise and evolve from a central theme captured by the process of information exchange. Thus, a relatively simple universal logic that rules the evolution of natural phenomena can be unveiled from the apparent complexity of the natural world.     

External and internal complexity of complex adaptive systems

We introduce concepts of external and internal complexity to analyze the relation between an adaptive system and its environment. We apply this theoretical framework to the construction of models in a cognitive system and the selection between hypotheses through selective observations performed on a data set in a recurrent process and propose a corresponding neural network architecture.     

Information,complexity and generative replication

The established definition of replication in terms of the conditions of causality, similarity and information transfer is very broad. We draw inspiration from the literature on self-reproducing automata to strengthen the notion of information transfer in replication processes. To the triple conditions of causality, similarity and information transfer, we add a fourth condition that defines a “generative replicator” as a conditional generative mechanism, which can turn input signals from an environment into developmental instructions. Generative replication must have the potential to enhance complexity, which in turn requires that developmental instructions are part of the information that is transmitted in replication. Demonstrating the usefulness of the generative replicator concept in the social domain, we identify social generative replicators that satisfy all of the four proposed conditions.

Informational Landscapes in Art, Science, and Evolution

An informational landscape refers to an array of information related to a particular theme or function. The Internet is an example of an informational landscape designed by humans for purposes of communication. Once it exists, however, any informational landscape may be exploited to serve a new purpose. Listening Post is the name of a dynamic multimedia work of art that exploits the informational landscape of the Internet to produce a visual and auditory environment. Here, I use Listening Post as a prototypic example for considering the creative role of informational landscapes in the processes that beget evolution and science.     

Regional distribution of neuropeptide-degrading enzyme activity in the rat brain: Effects of subacute exposure to carbon disulfide

Carbon disulfide, a volatile solvent, is widely used in industry. It has been demonstrated that it causes several neuropsychological symptoms. However, the neurochemical basis of its neurotoxic effect is relatively unknown. In this paper we have measured the effect of subacute i.p. administration on neutral and basic aminopepti-dase activities in discrete zones of the rat brain using lysine- and leucine-2-naphtylamides as substrates. Neutral aminopeptidase activity showed a significant decrease in the thalamus and cerebellum with marked (not significant) changes in the hypothalamus, hippocampus, medulla, and occipital cortex. There were no changes in basic ami-nopeptidase activity. It is suggested that amino-peptidase activity could play a role in carbon disulfide neurotoxic action in the aforementioned regions by generating changes in several neuropeptide levels.     

Geometric robustness theory and biological networks

We provide a geometric framework for investigating the robustness of information flows over biological networks. We use information measures to quantify the impact of knockout perturbations on simple networks. Robustness has two components, a measure of the causal contribution of a node or nodes, and a measure of the change or exclusion dependence, of the network following node removal. Causality is measured as statistical contribution of a node to network function, wheras exclusion dependence measures a distance between unperturbed network and reconfigured network function. We explore the role that redundancy plays in increasing robustness, and how redundacy can be exploited through error-correcting codes implemented by networks. We provide examples of the robustness measure when applied to familiar boolean functions such as the AND, OR and XOR functions. We discuss the relationship between robustness measures and related measures of complexity and how robustness always implies a minimal level of complexity.     

Complexity measures of brain wave dynamics

To understand the nature of brain dynamics as well as to develop novel methods for the diagnosis of brain pathologies, recently, a number of complexity measures from information theory, chaos theory, and random fractal theory have been applied to analyze the EEG data. These measures are crucial in quantifying the key notions of neurodynamics, including determinism, stochasticity, causation, and correlations. Finding and understanding the relations among these complexity measures is thus an important issue. However, this is a difficult task, since the foundations of information theory, chaos theory, and random fractal theory are very different. To gain significant insights into this issue, we carry out a comprehensive comparison study of major complexity measures for EEG signals. We find that the variations of commonly used complexity measures with time are either similar or reciprocal. While many of these relations are difficult to explain intuitively, all of them can be readily understood by relating these measures to the values of a multiscale complexity measure, the scale-dependent Lyapunov exponent, at specific scales. We further discuss how better indicators for epileptic seizures can be constructed.     

高压静电场对过氧化氢酶的稳定作用及机理研究

用高压静电场直接处理过氧化氢酶溶液 ,结果表明 :不同强度的高压静电场能提高和降低过氧化氢酶的活力。电场强度越大 ,酶活力达到平衡所需时间越短。处理后的过氧化氢酶溶液 ,玻棒 30秒的搅拌作用能使酶活力基本上回到原来的状态。溶液极性降低 ,酶活力达到平衡所需时间减少。处于高压静电场中的过氧化氢酶溶液 ,在 2 0℃时 ,电场强度为 3× 10 3V/cm ,活力能保持 5～ 6天 ,而对照只能保持 2～ 3天。在 30℃时 ,同样电场强度下 ,活力能保持 48小时左右 ,对照能保持 2 4小时左右。经高压静电场处理的酶 ,活力保持时间也比对照长。研究表明 :高压静电场对酶活力的稳定作用很可能是高压静电场使酶及溶液发生极化作用产生的。     

Small-world connectivity, motif composition, and complexity of fractal neuronal connections

Connection patterns of the cerebral cortex consist of pathways linking neuronal populations across multiple levels of scale, from whole brain regions to local minicolumns. This nested interconnectivity suggests the hypothesis that cortical connections are arranged in fractal or self-similar patterns. We describe a simple procedure to generate fractal connection patterns that aim at capturing the potential self-similarity and hierarchical ordering of neuronal connections. We examine these connection patterns by calculating a broad range of structural measures, including small-world attributes and motif composition, as well as some global measures of functional connectivity, including complexity. As we vary fractal patterns by changing a critical control parameter, we find strongly correlated changes in several structural and functional measures, suggesting that they emerge together and are mutually linked. Measures obtained from some modeled fractal patterns closely resemble those of real neuroanatomical data sets, supporting the original hypothesis.