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。     

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,     

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。     

NCBI网站及GenBank数据库概述

美国国立生物技术信息中心（ＮＣＢＩ）隶属于美国国立卫生研究院（Ｎａｔｉｏｎａｌ Ｉｎｓｔｉｕｔｅｏｆ Ｈｅａｌｔｈ,ＮＩＨ）,是国家医学图书馆（Ｎａｔｉｏｎａｌ ＬＩｂｒａｒｙ ｏｆ Ｍｅｄｉｃｉｎｅ,ＮＬＭ）的一个分支机构,负责通过ＩＮＴＥＲＮＥＴ等形式为医学、生物学方面的研究提供新的信息。本文主要介绍了ＮＣＢＩ网中ＧＥＮＢＡＮＫ序列数据库的功能及使用方法,如：数据的检索查询,数据递交等;同时还     

混沌在神经系统中的作用

随着非线性动力学的发展,发现神经的不规则电活动具有确定混沌特性。混沌广泛地存在于神经系统,神经元的混沌电活动对神经元的生理功能必不可少,服电的混沌活动特性与大脑的功能状态密切相关,在大脑正常状态下脑电混沌活动的维数、李雅普指数、复杂度等指标较高;而在服功能受损的病理状态下,上述混沌指标降低。混沌在神经系统中起着重要的作用。     

快速周期伏安法在定量研究脑内核团多巴胺释放中的应用

目的和方法：采用快速周期伏安法（ＦＣＶ）在体研究电刺激内侧前脑束（ＭＦＢ）或腹侧背盖区（ＶＴＡ）诱发的纹状体（ＣＰｕ）、伏核（Ａｃｂ）或中央杏仁核（ＣＡＮ）多巴胺（ＤＡ）释放的特点,探索电刺激诱发不同核团ＤＡ释放的适宜刺激参数。结果：ＣＰｕ、Ａｃｂ与ＣＡＮ的ＤＡ释放量及释放动力学特征均有不同。结论：在应用ＦＣＶ技术研究脑内不同部位ＤＡ释放时,应重视适宜刺激参数的选择及运用,以获取更好的实验结果。     

不同状态下脑电信号的双谱分析

根据脑电的非高斯随机特性,应用双谱技术分析脑电信号,引出脑电的参数化双谱估计,旨在克服脑电功率谱分析的缺陷。对四种不同脑功能状态（清醒闭眼、安神睁眼、快速心算、急促呼吸）的脑电进行双谱分析,并对对称脑电信号的互双谱作了初步的讨论。实验结果显示：所有脑电均出现明显的双谱结构,但不同生理状态下的脑电双谱结构存在明显的差异,结果表明双谱可能为研究脑电提供新的辅助信息。     

脑电信号的高阶奇异谱分析

奇异谱分析是脑电信号分析的一种新方法,脑电信号的奇异谱可以反映脑电的特征,它有助于研究大脑的动力学行为。奇异谱分析方法是基于二阶统计的方法,反映的是信号时间上和空间上的一种线性相关关系。而脑电信号属于非线性信号,其内在的非线性关系很难通过奇异谱得到真实的反映,从而会丢失某些有用的信息。提出一种新的基于高阶统计的脑电奇异谱分析方法,并将其运用于正常脑电和癫痫患者的脑电分析中。大量的实测信号样本仿真实验结果表明,正常脑电和癫痫脑电的奇异谱有明显的不同。此外,基于高阶统计的奇异谱和基于二阶统计的奇异谱相比更能反映出信号的细节。     

人神经生长抑制因子β结构域的高效表达及性质研究

神经生长抑制因子（ Ｇ Ｉ Ｆ）是一种特异存在于哺乳动物脑中的金属硫蛋白（ｍ ｅｔａｌｌｏｔｈｉｏｎｅｉｎ, Ｍ Ｔ）类似物,又称 Ｍ Ｔ Ⅲ．它与 Ｍ Ｔ 有相同的结合 Ｚｎ（Ⅱ）, Ｃｄ（Ⅱ）, Ｃｕ（Ⅰ）等金属的能力,但与 Ｍ Ｔ 不同的是它能够抑制神经细胞的生长,并发现在患 Ａｌｚｈｅｉｍ ｅｒ ｄｉｓｅａｓｅ（ Ａ Ｄ 症）病人的大脑中 Ｇ Ｉ Ｆ蛋白量和ｍ Ｒ Ｎ Ａ 的量均显著下降,研究证明 Ｇ Ｉ Ｆ对神经细胞的抑制活性主要存在于其 β结构域中．为进一步研究 β结构域结构和功能的关系,将 β结构域的 ｃ Ｄ Ｎ Ａ 克隆入融合表达载体ｐ Ｇ Ｅ Ｘ ４ Ｔ １ 中, Ｉ Ｐ Ｔ Ｇ 诱导并高效表达了 β结构域蛋白,通过氨基酸组成和质谱的测定,证明得到了目的蛋白．利用金属重组的方法,分别得到了结合 Ｃｄ 和 Ｚｎ 的 Ｇ Ｉ Ｆ 的 β结构域,并测定了其巯基和金属含量对蛋白量的比值,证明所得 Ｇ Ｉ Ｆ β与 Ｍ Ｔ β在结合金属能力上十分相似．用紫外光谱学的研究表明, Ｃｄ Ｍ Ｔ 的 β结构域在２５０ ｎｍ 处比 Ｃｄ Ｇ Ｉ Ｆ 的 β结构域有一明显肩峰,从而表明二者的金属—巯基结合簇的结构有明显不同,而这种结构上的差异有可能导致二者在功能上的不同．     

EEG的信息熵分析

用非线性动力学观点来分析ＥＥＧ认为它可能是一个不稳定混沌态,并提出了用信息熵的方法来分析这一混沌态的结构特征。临床观察发现在不同功能下其特征是不一样的,并发现精神病人中信息传递特征参数有“倒置“现象,这对ＥＥＧ分析和临床脑电诊断都是有很大意义的。     

基于大脑皮层互信息理论的睡眠分级研究

睡眠的分级研究是睡眠状况分析和睡眠质量评价的前提和基本内容。目前国际通用的睡眠分级方法,是利用脑电信号另加脑功能信号（如肌电图、眼动电流图）,且必须由人工来判别分析的。大脑皮层互信息理论是研究脑功能变化的有力工具。通过动态计算睡眠脑电四个导联之间的互信息时间序列的复杂度,并利用一个三层的人工神经网络进行六个级别的分类,6例720个不同时期的睡眠片段的测试表明,系统睡眠分级与人工分级的总相符率达到90.83％,且实现了睡眠动态自动分级。神经网络的学习功能,可使系统的准确率进一步提高,逐渐接近或达到人工分级的水平。     

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。     

The research of constructing dynamic cognition model based on brain network

Estimating the functional interactions and connections between brain regions to corresponding process in cognitive, behavioral and psychiatric domains is a central pursuit for understanding the human connectome. Few studies have examined the effects of dynamic evolution on cognitive processing and brain activation using brain network model in scalp electroencephalography (EEG) data. Aim of this study was to investigate the brain functional connectivity and construct dynamic programing model from EEG data and to evaluate a possible correlation between topological characteristics of the brain connectivity and cognitive evolution processing. Here, functional connectivity between brain regions is defined as the statistical dependence between EEG signals in different brain areas and is typically determined by calculating the relationship between regional time series using wavelet coherence. We present an accelerated dynamic programing algorithm to construct dynamic cognitive model that we found that spatially distributed regions coherence connection difference, the topologic characteristics with which they can transfer information, producing temporary network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time after variation audio stimulation, dynamic programing model gives the dynamic evolution processing at different time and frequency. In this paper, by applying a new construct approach to understand whole brain network dynamics, firstly, brain network is constructed by wavelet coherence, secondly, different time active brain regions are selected by network topological characteristics and minimum spanning tree. Finally, dynamic evolution model is constructed to understand cognitive process by dynamic programing algorithm, this model is applied to the auditory experiment, results showed that, quantitatively, more correlation was observed after variation audio stimulation, the EEG function connection dynamic evolution model on cognitive processing is feasible with wavelet coherence EEG recording.     

Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.     

PTFOS: Flexible and Absorbable Intracranial Electrodes for Magnetic Resonance Imaging

Intracranial electrocortical recording and stimulation can provide unique knowledge about functional brain anatomy in patients undergoing brain surgery. This approach is commonly used in the treatment of medically refractory epilepsy. However, it can be very difficult to integrate the results of cortical recordings with other brain mapping modalities, particularly functional magnetic resonance imaging (fMRI). The ability to integrate imaging and electrophysiological information with simultaneous subdural electrocortical recording/stimulation and fMRI could offer significant insight for cognitive and systems neuroscience as well as for clinical neurology, particularly for patients with epilepsy or functional disorders. However, standard subdural electrodes cause significant artifact in MRI images, and concerns about risks such as cortical heating have generally precluded obtaining MRI in patients with implanted electrodes. We propose an electrode set based on polymer thick film organic substrate (PTFOS), an organic absorbable, flexible and stretchable electrode grid for intracranial use. These new types of MRI transparent intracranial electrodes are based on nano-particle ink technology that builds on our earlier development of an EEG/fMRI electrode set for scalp recording. The development of MRI-compatible recording/stimulation electrodes with a very thin profile could allow functional mapping at the individual subject level of the underlying feedback and feed forward networks. The thin flexible substrate would allow the electrodes to optimally contact the convoluted brain surface. Performance properties of the PTFOS were assessed by MRI measurements, finite difference time domain (FDTD) simulations, micro-volt recording, and injecting currents using standard electrocortical stimulation in phantoms. In contrast to the large artifacts exhibited with standard electrode sets, the PTFOS exhibited no artifact due to the reduced amount of metal and conductivity of the electrode/trace ink and had similar electrical properties to a standard subdural electrode set. The enhanced image quality could enable routine MRI exams of patients with intracranial electrode implantation and could also lead to chronic implantation solutions.     

经颅磁刺激对癫痫病灶脑电相关维数的影响

利用脑功能指标——大鼠病灶区脑电的相关维数,研究低频经颅磁刺激对慢性颞叶癫痫大鼠脑功能改善的作用。对一组颞叶癫痫大鼠施予频率为0．5Hz、强度为0．4T、20次／日、连续一周的低频重复性经颅磁刺激(rTMS)．在rTMS前后,分别测取颞叶癫痫大鼠责任病灶区皮层和海马区的脑电,重构时间延迟吸引子,用G-P算法估算反映对应脑区功能状态的相关维数。研究结果显示：施予适量的rTMS(0.4T、20次／日、连续一周),使颞叶癫痫大鼠海马和相应皮层脑电的相关维数比刺激前明显升高。研究表明适量的rTMS有抑制癫痫的作用。     

非线性动力学在脑电信号分析中的应用

EEG是由大脑产生的非线性时间序列,体现出混沌行为。近年来迅速发展的非线性动力学理论为脑电信号分析开创了一个新的领域。本文综述了近年来非线性动力学在脑电信号研究中（睡眠阶段,麻醉深度,认知过程,精神分裂,痴呆及癫痫）的进展,以期对脑神经动力学有更好的理解。