基于模糊近似熵的抑郁症患者静息态功能磁共振成像信号复杂度分析

提出采用模糊近似熵的方法对功能磁共振成像(functional magnetic resonance imaging,fMRI)复杂度量化分析,并与样本熵进行比较.采用的22个成年抑郁症患者中,11位男性,年龄在18—65岁之间.我们期望测量的静息态fMRI信号复杂度与Goldberger/Lipsitz模型一致,越健康、越稳健其生理表现的复杂度越大,且复杂度随年龄的增大而降低.全脑平均模糊近似熵与年龄之间差异性显著(r=-0.512,p0.001).相比之下,样本熵与年龄之间差异性不显著(r=-0.102,p=0.482).模糊近似熵同样与年龄相关脑区(额叶、顶叶、边缘系统、颞叶、小脑顶叶)之间差异性显著(p0.05),样本熵与年龄相关脑区之间差异性不显著性.这些结果与Goldberger/Lipsitz模型一致,说明采用模糊近似熵分析fMRI数据复杂度是一个有效的新方法.     

磁共振结构成像在自闭症研究中的应用

自闭症,又称作孤独症,是一种常发生在儿童中的广泛性发育障碍,表现为交流障碍、语言障碍以及重复刻板的行为和狭窄的兴趣爱好. 磁共振成像作为一种无损伤的和多参数的影像方法,其各种检测手段均被应用于自闭症实验研究,其中结构磁共振成像(sMRI)和功能磁共振成像(fMRI)研究居多. 研究表明自闭症患者脑部结构发生了显著性改变. 该文综述了当前世界上利用磁共振结构成像研究自闭症的主要成果,包括结构磁共振成像研究脑体积变化,扩散张量成像研究自闭症的脑白质损伤. 该文也说明磁共振成像是一种十分有用的研究工具,有望在将来更多被用于探索自闭症的未知领域.     

两种熵测度在量化射击运动员短时心率变异性信号复杂度上的一致性

利用两种基于熵的非线性复杂度测度：近似熵和样本熵,研究了专业射击运动员两种不同状态下(休息和练习赛)心率变异性信号的复杂度.计算结果表明：射击运动员休息时其心率变异性信号的熵值大于射击比赛时信号的熵值,这意味着运动员一旦进行射击比赛时,其心率变异性信号复杂度降低了,心跳变得更为规则了.为了更好地应用这两种基于熵的方法,进一步分析了算法中的两个重要影响因素：矢量匹配容差r和序列长度N对算法性能的影响.分析结果表明：只要参数选择在合适的范围内,近似熵和样本熵都能够正确地区分出两种不 关键词： 近似熵 样本熵 复杂度 射击     

基于振幅-周期二维特征的脑电样本熵分析

样本熵可以有效反映一维时间序列中新模式的生成概率,但缺乏对二维序列复杂度的表征能力.基于对传统样本熵方法的改进,提出一种在振幅-周期二维空间描述波形复杂度的方法,二维样本熵反映了波形振动在振幅-周期空间中新模式的生成概率.通过仿真实验证明了这种方法描述波形复杂度的有效性,当波形的复杂度特征表现为振幅-周期的交互作用时,二维样本熵对复杂度的描述比一维条件下的样本熵更加有效.基于二维样本熵对抑郁症组和对照组的脑电复杂度进行分析,结果表明,抑郁症组在Alpha频段左侧顶区和左侧枕区的二维样本熵显著低于对照组,表明在上述频段和位置,抑郁症患者脑电中新模式的生成概率显著低于正常人,这一特征可能成为抑郁症的潜在生物标记.     

等概率符号化样本熵应用于脑电分析

样本熵(或近似熵)以信息增长率刻画时间序列的复杂性,能应用于短时序列,因而在生理信号分析中被广泛采用.然而,一方面由于传统样本熵采用与标准差线性相关的容限,使得熵值易受非平稳突变干扰的影响,另一方面传统样本熵还受序列概率分布的影响,从而导致其并非单纯反映序列的信息增长率.针对上述两个问题,将符号动力学与样本熵结合,提出等概率符号化样本熵方法,并对其物理意义、数学推导及参数选取都做了详细阐述.通过对噪声数据的仿真计算,验证了该方法的正确性及其区分不同强度时间相关的有效性.此方法应用于脑电信号分析的结果表明,在不对信号做人工伪迹去除的前提下,只需要1.25 s的脑电信号即可有效地区分出注意力集中和注意力发散两种状态.这进一步证明了该方法可很好地抵御非平稳突变干扰,能快速获得短时序列的潜在动力学特性,对脑电生物反馈技术具有很大的应用价值.     

脑电信号的样本熵分析

运用非线性动力学方法对癫痫患者与正常人的脑电数据进行分析。研究结果表明,癫痫患者发作期的样本熵值总体上低于正常人,并且癫痫患者在发作时的脑电样本熵值较发作前有明显降低,发作后又回到发前水平。这预示着样本熵可能为癫痫病的临床诊断提供一定的参考。同时通过实验验证,样本熵具有较好的一致性,且只需要较短的数据就能达到分析目的,是分析脑电信号的有利手段。     

静息态功能磁共振成像评估健康老年人认知行为的多尺度熵模型研究

当前,静息态功能磁共振成像(rfMRI)为脑功能检测提供了高效、快捷的先进技术.熵可以捕捉神经信号动态特征,可作为量化评估参数,但尚存在固定尺度计算缺陷且对认知行为的生物学标记少有研究,影响检测精准性.为此,本文将多尺度熵模型与机器学习方法联合,寻求BOLD信号复杂度表征健康老年人认知分数的功能影像学标记.由扫描前认知量表测试分数将98名健康老年人分为优、差两组,78名纳入训练,20名纳入测试.首先,构建多尺度熵模型,计算两组扫描数据熵,统计和对比以优化模型参数;然后,在优化参数下由统计显著性高的脑区熵值构建特征向量;最后,用极限学习机对两组分类并统计检验.发现:rfMRI多尺度熵在评估老年人认知分数时,在额、颞叶脑区存在较大显著性差异,以此为标记区分认知分数可达80%准确率.结论:额、颞叶等脑区优化的多尺度熵可有效区分健康老年人认知行为优劣.该研究将为rfMRI替代主观繁琐的传统认知量表测试提供新的检测参数和新方法.     

静息态脑电信号动态功能连接分析

静息态脑功能连接分析是近年来脑研究的一个热点问题, 对于某些脑疾病的诊断及成因理解具有重要意义. 已有的脑功能连接研究基本上都假设功能连接网络在一段时间内是稳定不变的, 但越来越多的证据表明它应该是随时间动态变化的. 对25名被试睁眼和闭眼状态的64电极脑电生理信号, 采用独立成分分析、滑动时间窗、低分辨率脑电断层溯源、图论等方法和技术进行动态功能连接分析, 展现了睁眼和闭眼两种基线状态下视觉网络、默认网络等功能连接网络随时间的动态变化, 并对动态连接矩阵进行主成分分析得到了在整个时间段内具有代表意义的功能连接模式. 该结论支持和补充了传统稳态脑功能连接的研究, 也将为相关实验设计以及脑电信号临床研究提供基线选择依据.     

基于改进功率谱熵的抑郁症脑电信号活跃性研究

采用非线性动力学方法研究脑精神疾病是近年来国内外学者研究的热点和趋势.针对脑精神疾病的研究和诊断中缺少客观有效的量化参数和量化指标的状况,提出了一种根据对时间序列功率谱划分而定义的谱熵,然后用其计算和分析脑电信号谱熵的方法.通过数据仿真试验证明该谱熵和信号活跃性之间存在正相关关系.基于这种相关性,应用该方法对抑郁症患者和正常对照组的脑电信号功率谱熵进行了数值计算,然后进行了分析对比和统计检验.实验结果表明:抑郁症患者脑电信号的功率谱熵在部分脑区显著弱于正常健康人.证明该谱熵能够表征大脑电生理活动状况,提供反映其活动性强弱的信息,可以作为度量大脑电生理活动性的一个参数.这对于能否将该功率谱熵作为诊断脑精神疾病的物理参数具有积极意义.     

基于近似熵的突变检测新方法

近似熵是一个有效的非线性动力学指数,能够用于表征时间序列的复杂性,通过滑动窗口技术,近似熵对于一维时间序列的动力学结构突变具有一定的识别能力,但其突变检测结果依赖于子序列长度的选择,且不能准确定位突变点.鉴于此,本文提出了一种新的突变检测方法——滑动移除近似熵.测试结果表明,滑动移除近似熵具有检测结果稳定性好、准确性高等特点,明显优于滑动近似熵和Mann-Kendall方法,其在实际观测资料中的应用进一步证实了新方法的可靠性. 关键词： 近似熵 滑动移除近似熵 突变检测     

Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.     

EEG-Based Emotion Recognition by Exploiting Fused Network Entropy Measures of Complex Networks across Subjects

It is well known that there may be significant individual differences in physiological signal patterns for emotional responses. Emotion recognition based on electroencephalogram (EEG) signals is still a challenging task in the context of developing an individual-independent recognition method. In our paper, from the perspective of spatial topology and temporal information of brain emotional patterns in an EEG, we exploit complex networks to characterize EEG signals to effectively extract EEG information for emotion recognition. First, we exploit visibility graphs to construct complex networks from EEG signals. Then, two kinds of network entropy measures (nodal degree entropy and clustering coefficient entropy) are calculated. By applying the AUC method, the effective features are input into the SVM classifier to perform emotion recognition across subjects. The experiment results showed that, for the EEG signals of 62 channels, the features of 18 channels selected by AUC were significant (p < 0.005). For the classification of positive and negative emotions, the average recognition rate was 87.26%; for the classification of positive, negative, and neutral emotions, the average recognition rate was 68.44%. Our method improves mean accuracy by an average of 2.28% compared with other existing methods. Our results fully demonstrate that a more accurate recognition of emotional EEG signals can be achieved relative to the available relevant studies, indicating that our method can provide more generalizability in practical use.     

Neural correlates of a clinical continuous performance test

Functional magnetic resonance imaging (fMRI) was performed in 30 healthy adults to identify the location, magnitude, and extent of activation in brain regions that are engaged during the performance of Conners' Continuous Performance Test (CPT). Performance on the task during fMRI was highly correlated with performance on the standard Conners' CPT in the behavioral testing laboratory. An extensive neural network was activated during the task that included the frontal, cingulate, parietal, temporal, and occipital cortices; the cerebellum and the basal ganglia. There was also a network of brain regions which were more active during fixation than task. The magnitude of activation in several regions was correlated with reaction time. Among regions that were more active during task, the overall volume of supratentorial activation and cerebellar activation was greater in the left hemisphere. Frontal activation was greater in dorsal than in ventral regions, and dorsal frontal activation was bilateral. Ventral frontal region and parietal lobe activation were greater in the right hemisphere. The volume of clusters of activation in the extrastriate ventral visual pathway was greater in the left hemisphere. This network is consistent with existing models of motor control, visual object processing and attentional control and may serve as a basis for hypothesis-driven fMRI studies in clinical populations with deficits in Conners' CPT performance.     

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.     

Quantitative in vivo1H-spectroscopy of healthy and tumorous human brain tissue at 4 tesla

In 35 healthy volunteers 79 hydrogen spectra were measured from the parietal lobe, parieto-occipital lobe, frontal lobe, temporal lobe, thalamus and insular region. Voxels were selected with a double spin-echo sequence at TE 71, 135 and 270 ms. The spectra were quantitatively evaluated by fitting a Lorentzian model to the resonances of the creatine pool at 3.02 ppm and the choline pool at 3.22 ppm. No differences were found in the intensities of either metabolite in the 6 investigated regions. Creatine and choline were equally distributed in these regions. The interindividual reproducibility of the spectra decreases with longer echo delays. The coefficients of variation of the areas of creatine and choline corrected for the number of acquisitions and the voxel size are ±13% at TE 71 ms, ±23% at TE 135 ms, ±43% at TE 270 ms. This is caused by an interindividual variation in T₂ by ±15%, which affects all resonances of a spectrum. Signal variations from the fit, the Q-factor of the RF-coil loaded with different subjects and variations in the flip angle are less than 10% at each echo delay. The intraindividual variation without repositioning of the subject was better than 10%. Using creatine as an internal reference the ratios of the amplitudes of N-acetyl-aspartate (NAA) at 2.01 ppm and γ-methylene protons of glutamic acid at 2.34 ppm were not specific for special regions of the brain. Only in the temporal lobe the ratio of NAA and creatine was reduced. A mean concentration ratio of 1.7 for NAA and Cre was measured as an average over all subjects and the investigated brain regions with the exception of the temporal lobe. Initial applications of the method to 7 patients with brain tumors are described.     

Complexity of Body Movements during Sleep in Children with Autism Spectrum Disorder

Recently, measuring the complexity of body movements during sleep has been proven as an objective biomarker of various psychiatric disorders. Although sleep problems are common in children with autism spectrum disorder (ASD) and might exacerbate ASD symptoms, their objectivity as a biomarker remains to be established. Therefore, details of body movement complexity during sleep as estimated by actigraphy were investigated in typically developing (TD) children and in children with ASD. Several complexity analyses were applied to raw and thresholded data of actigraphy from 17 TD children and 17 children with ASD. Determinism, irregularity and unpredictability, and long-range temporal correlation were examined respectively using the false nearest neighbor (FNN) algorithm, information-theoretic analyses, and detrended fluctuation analysis (DFA). Although the FNN algorithm did not reveal determinism in body movements, surrogate analyses identified the influence of nonlinear processes on the irregularity and long-range temporal correlation of body movements. Additionally, the irregularity and unpredictability of body movements measured by expanded sample entropy were significantly lower in ASD than in TD children up to two hours after sleep onset and at approximately six hours after sleep onset. This difference was found especially for the high-irregularity period. Through this study, we characterized details of the complexity of body movements during sleep and demonstrated the group difference of body movement complexity across TD children and children with ASD. Complexity analyses of body movements during sleep have provided valuable insights into sleep profiles. Body movement complexity might be useful as a biomarker for ASD.     

Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders

Recent findings of neurological functioning in autism spectrum disorder (ASD) point to altered brain connectivity as a key feature of its pathophysiology. The cortical underconnectivity theory of ASD (Just et al., 2004) provides an integrated framework for addressing these new findings. This theory suggests that weaker functional connections among brain areas in those with ASD hamper their ability to accomplish complex cognitive and social tasks successfully. We will discuss this theory, but will modify the term underconnectivity to ‘disrupted cortical connectivity’ to capture patterns of both under- and over-connectivity in the brain. In this paper, we will review the existing literature on ASD to marshal supporting evidence for hypotheses formulated on the disrupted cortical connectivity theory. These hypotheses are: 1) underconnectivity in ASD is manifested mainly in long-distance cortical as well as subcortical connections rather than in short-distance cortical connections; 2) underconnectivity in ASD is manifested only in complex cognitive and social functions and not in low-level sensory and perceptual tasks; 3) functional underconnectivity in ASD may be the result of underlying anatomical abnormalities, such as problems in the integrity of white matter; 4) the ASD brain adapts to underconnectivity through compensatory strategies such as overconnectivity mainly in frontal and in posterior brain areas. This may be manifested as deficits in tasks that require frontal–parietal integration. While overconnectivity can be tested by examining the cortical minicolumn organization, long-distance underconnectivity can be tested by cognitively demanding tasks; and 5) functional underconnectivity in brain areas in ASD will be seen not only during complex tasks but also during task-free resting states. We will also discuss some empirical predictions that can be tested in future studies, such as: 1) how disrupted connectivity relates to cognitive impairments in skills such as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD.     

基于变分模态分解-传递熵的脑肌电信号耦合分析

皮层肌肉功能耦合是大脑皮层和肌肉组织间的相互作用, 脑肌电信号的多尺度耦合特征可以体现皮层-肌肉间多时空的功能联系. 本文引入变分模态分解并与传递熵结合, 构建变分模态分解-传递熵模型应用于脑肌间耦合研究. 首先基于变分模态分解将同步采集的脑电(EEG) 和肌电(EMG) 信号分别进行时频尺度化, 然后计算不同时频尺度间的传递熵值, 获取不同耦合方向(EEG→EMG 及EMG→EEG) 上不同尺度间的非线性耦合特征. 结果表明, 在静态握力输出条件下, 皮层与肌肉beta (15—35 Hz) 频段间的耦合强度最为显著; EEG→EMG 方向上脑电与肌电高gamma (50—72 Hz) 频段的耦合强度总体上高于EMG→EEG 方向.研究结果揭示皮层-肌肉功能耦合具有双向性, 且脑肌间不同耦合方向上、不同频段间的耦合强度有所差异.因此可利用变分模态分解-传递熵方法定量刻画大脑皮层与肌肉各时频段之间的非线性同步特征及功能联系.     

Evidence from human scalp electroencephalograms of global chaotic itinerancy

My objective of this study was to find evidence of chaotic itinerancy in human brains by means of noninvasive recording of the electroencephalogram (EEG) from the scalp of normal subjects. My premise was that chaotic itinerancy occurs in sequences of cortical states marked by state transitions that appear as temporal discontinuities in neural activity patterns. I based my study on unprecedented advances in spatial and temporal resolution of the phase of oscillations in scalp EEG. The spatial resolution was enhanced by use of a high-density curvilinear array of 64 electrodes, 189 mm in length, with 3 mm spacing. The temporal resolution was advanced to the limit provided by the digitizing step, here 5 ms, by use of the Hilbert transform. The numerical derivative of the analytic phase revealed plateaus in phase that lasted on the order of 0.1 s and repeated at rates in the theta (3-7 Hz) or alpha (7-12 Hz) ranges. The plateaus were bracketed by sudden jumps in phase that usually took place within 1 to 2 digitizing steps. The jumps were commonly synchronized in each cerebral hemisphere over distances of up to 189 mm, irrespective of the orientation of the array. The jumps were usually not synchronized across the midline separating the hemisphere or across the sulcus between the frontal and parietal lobes. I believe that the widespread synchrony of the jumps in analytic phase manifest a metastable cortical state in accord with the theory of self-organized criticality. The jumps appear to be subcritical bifurcations. They reflect the aperiodic evolution of brain states through sequences of attractors that on access support the experience of remembering.