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.     

基于变分模态分解-相干分析的肌间耦合特性

肌间耦合是肢体运动过程中不同肌肉间的相互关联与相互协调作用.通过研究肌电信号(s EMG)间特征频段的耦合特性可以获得肌肉间的功能联系及中枢神经系统支配肢体运动的执行与协调方式机理.本文将变分模态分解与相干分析相结合,构建变分模态分解-相干分析模型,定量描述肢体运动中相关肌肉sEMG在特征频段的耦合特性.在20%最大自主收缩力静态负荷强度下,采集20名健康被试的sEMG,基于变分模态分解方法将sEMG时频尺度化,进而分析不同sEMG在特征频段的相干性,并计算显著相干面积指标,定量分析肌间特征频段的功能耦合特性.结果表明:低负荷静态握力维持过程中,指浅屈肌与尺侧腕曲肌、指浅屈肌与指伸肌的beta与gamma频段耦合强度随时间推进而增强;相较于指浅屈肌与指伸肌,疲劳状态下指浅屈肌与尺侧腕曲肌beta与gamma频段耦合强度变化更显著,且瞬时频率特征变化相似,揭示运动致疲劳过程中协同肌受中枢神经系统控制以更加同步的方式活动.     

基于网络连接度指标的脑梗死患者脑电信号相同步分析

基于图论的脑功能网络分析是近年来的一个研究热点,而相同步分析已被证实为揭示多导联脑电信号之间功能连接的有效工具.针对当脑电采集系统中导联数目较少而不适用于采用图论分析的情况,提出使用基于导联间相同步分析的网络连接度指标研究脑功能网络的关联特性和整体特性.采用新的频带划分方法,将0.5—30 Hz带宽内的脑电信号划分到5个子带上,计算了不同数据长度下各子带分量的网络连接度指标,并对比分析了各子带分量的相对功率.结果表明:在对脑梗死患者的脑电图和正常人的脑电图进行分析时,需要合理的数据长度量化不同动力学系统之间的差异;在合理的数据长度下,在网络连接度指标的区分效果方面,19—24 Hz分量信号优于其他分量,而且仅在19—24 Hz频带上,脑梗死患者组的所有导联出现了与对照组的所有导联相同趋势的变化.研究表明19—24 Hz频带是脑梗死最佳的脑电图诊断频段,可将该频段下的网络连接度指标作为脑梗死辅助诊断的新指标.     

基于弹性变分模态分解的癫痫脑电信号分类方法

癫痫脑电信号分类对于癫痫诊治具有重要意义.为了实现病灶性与非病灶性癫痫脑电信号的分类,本文利用弹性网回归重构变分模态分解算法,提出弹性变分模态分解算法并将其应用到所提癫痫脑电信号分类方法中.该方法先将原信号分割成多个子信号,并对各子信号进行弹性变分模态分解,然后从分解后的不同变分模态函数中提取精细复合多尺度散布熵作为特征,最后利用支持向量机进行分类.针对癫痫脑电的公共数据集,最终的实验结果表明,准确率、灵敏度和特异度三个性能指标分别达到92.54%,93.22%和91.86%.     

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.     

感性神经元模型及其动力学特性研究

神经元的大小属于介观尺度范围,本文考虑神经元的电感特性,建立了由细胞膜电感、膜电容、钾离子忆阻器和氯离子电阻构成的神经元经典电路模型和介观电路模型.利用经典电路理论和介观电路的量子理论,推导了在外部冲击激励下神经元细胞膜电压响应的表达式.将枪乌贼神经元的电生理参数代入膜电压表达式并计算可知,两种模型下的膜电压均先增大后减小,最后达到零值的静息状态,且其能量主要集中在0—30 Hz的脑电频率范围内.进一步比较发现,介观电路模型下膜电压的峰值及达到峰值所需的时间(达峰时间)均低于经典电路模型下的值,并与枪乌贼轴突受到刺激后的实验结果更接近,说明介观电路模型更能反应神经元受到刺激后的生理特征.基于介观电路模型,随着外部激励强度的增加,膜电压的峰值增加且达峰时间变短.膜电压峰值及达峰时间等参数更易受神经元膜电容的影响.神经元的介观电路模型对于理解神经元受到刺激后的兴奋性,推动受大脑功能启发的量子神经网络的发展等具有重要意义.     

感性神经元模型及其动力学特性研究

神经元的大小属于介观尺度范围,本文考虑神经元的电感特性,建立了由细胞膜电感、膜电容、钾离子忆阻器和氯离子电阻构成的神经元经典电路模型和介观电路模型.利用经典电路理论和介观电路的量子理论,推导了在外部冲击激励下神经元细胞膜电压响应的表达式.将枪乌贼神经元的电生理参数代入膜电压表达式并计算可知,两种模型下的膜电压均先增大后减小,最后达到零值的静息状态,且其能量主要集中在0—30 Hz的脑电频率范围内.进一步比较发现,介观电路模型下膜电压的峰值及达到峰值所需的时间(达峰时间)均低于经典电路模型下的值,并与枪乌贼轴突受到刺激后的实验结果更接近,说明介观电路模型更能反应神经元受到刺激后的生理特征.基于介观电路模型,随着外部激励强度的增加,膜电压的峰值增加且达峰时间变短.膜电压峰值及达峰时间等参数更易受神经元膜电容的影响.神经元的介观电路模型对于理解神经元受到刺激后的兴奋性,推动受大脑功能启发的量子神经网络的发展等具有重要意义.     

Corticomuscular synchronization with small and large dynamic force output

Background  

Over the last few years much research has been devoted to investigating the synchronization between cortical motor and muscular activity as measured by EEG/MEG-EMG coherence. The main focus so far has been on corticomuscular coherence (CMC) during static force condition, for which coherence in beta-range has been described. In contrast, we showed in a recent study [1] that dynamic force condition is accompanied by gamma-range CMC. The modulation of the CMC by various dynamic force amplitudes, however, remained uninvestigated. The present study addresses this question. We examined eight healthy human subjects. EEG and surface EMG were recorded simultaneously. The visuomotor task consisted in isometric compensation for 3 forces (static, small and large dynamic) generated by a manipulandum. The CMC, the cortical EEG spectral power (SP), the EMG SP and the errors in motor performance (as the difference between target and exerted force) were analyzed.     

Effect of pulsed transcranial ultrasound stimulation at different number of tone-burst on cortico-muscular coupling

Background

Pulsed transcranial ultrasound stimulation (pTUS) can modulate the neuronal activity of motor cortex and elicit muscle contractions. Cortico-muscular coupling (CMC) can serve as a tool to identify interaction between the oscillatory activity of the motor cortex and effector muscle. This research aims to explore the neuromodulatory effect of low-intensity, pTUS with different number of tone burst to neural circuit of motor-control system by analyzing the coupling relationship between motor cortex and tail muscle in mouse. The motor cortex of mice was stimulated by pulsed transcranial ultrasound with different number of tone bursts (NTB?=?100 150 200 250 300). The local field potentials (LFPs) in tail motor cortex and electromyography (EMG) in tail muscles were recorded simultaneously during pTUS. The change of integral coupling strength between cortex and muscle was evaluated by mutual information (MI). The directional information interaction between them were analyzed by transfer entropy (TE).

Results

Almost all of the MI and TE values were significantly increased by pTUS. The results of MI showed that the CMC was significantly enhanced with the increase of NTB. The TE results showed the coupling strength of CMC in descending direction (from LFPs to EMG) was significantly higher than that in ascending direction (from EMG to LFPs) after stimulation. Furthermore, compared to NTB?=?100, the CMC in ascending direction were significantly enhanced when NTB?=?250, 300, and CMC in descending direction were significantly enhanced when NTB?=?200, 250, 300.

Conclusion

These results confirm that the CMC between motor cortex and the tail muscles in mouse could be altered by pTUS. And by increasing the NTB (i.e. sonication duration), the coupling strength within the cortico-muscular circuit could be increased, which might further influence the motor function of mice. It demonstrates that, using MI and TE method, the CMC could be used for quantitatively evaluating the effect of pTUS with different NTBs, which might provide a new insight into the effect of pTUS neuromodulation in motor cortex.

     

Estimation of time delay by coherence analysis

Using coherence analysis (which is an extensively used method to study the correlations in frequency domain, between two simultaneously measured signals) we estimate the time delay between two signals. This method is suitable for time delay estimation of narrow band coherence signals for which the conventional methods cannot be reliably applied. We show, by analysing coupled Rössler attractors with a known delay, that the method yields satisfactory results. Then, we apply this method to human pathologic tremor. The delay between simultaneously measured traces of electroencephalogram (EEG) and electromyogram (EMG) data of subjects with essential hand tremor is calculated. We find that there is a delay of 11–27 milli-seconds (ms) between the tremor correlated parts (cortex) of the brain (EEG) and the trembling hand (EMG) which is in agreement with the experimentally observed delay value of 15 ms for the cortico-muscular conduction time. By surrogate analysis we calculate error bars of the estimated delay.     

Nonlinear statistical properties of fMRI signals in human visual cortex during resting-state

The purpose of the current work is to study nonlinear dynamics in neuronal activity within human brain visual cortex based on blood-oxygen-level dependent (BOLD) contrast imaging. In particular, based on functional magnetic resonance imaging (fMRI) signals, measures of fractality, complexity, and state disorder are estimated from central and peripheral eccentricity bands across three visual areas. Statistical results from analysis of 48750 resting-state fMRI signals show evidence that nonlinear dynamics of neuronal activity in resting-state in central and peripheral eccentricity bands of human visual cortex are persistent. However, they exhibit heterogeneous variability across eccentricity bands and visual areas. Also, information content in first visual area is more ordered than in the second one, whilst information content in the third visual area is the least ordered. These interesting nonlinear statistical properties are a further step toward understanding neuronal activity and nonlinear dynamics in human brain visual cortex.     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

Solvation beyond the linear response regime

Transient two-dimensional infrared spectroscopy (2D-IR) on a charge transfer model system is used as a nonlinear probe of solvation dynamics. Unlike what is expected in the linear response case, nonequilibrium relaxation and equilibrium spectral diffusion occur on different time scales. Transient 2D-IR spectroscopy is shown to be sensitive to higher order frequency fluctuation correlation functions, and provides evidence for a coupling between commonly observed fast and slow solvation processes.     

Mapping of spikes, slow waves, and motor tasks in a patient with malformation of cortical development using simultaneous EEG and fMRI

We report on the simultaneous and continuous acquisition of EEG and functional MRI data in a patient with a left hemiparesis and focal epilepsy secondary to malformation of cortical development in the right hemisphere. EEG-triggered fMRI localization was previously demonstrated in this patient. In the experiments reported here, 322 spikes maximum at electrode C4 and 126 focal slow waves were identified offline. A hierarchy of models was explored in order to assess the relative contributions of each type of EEG event. Modeling the BOLD response to C4 spikes alone showed an area of activation within the large malformation, adjacent to the area of infolding cortex. However, also modeling slow-waves gave rise to a broader and stronger activation, suggesting that the generators overlap. Motor mapping of the right hand showed activation in the left sensorimotor cortex; left-hand tapping led to a more diffuse area of activation, displaced superiorly into the superior frontal gyrus, and a small area of activation within the lesion. In conclusion, continuous EEG-fMRI is useful to compare the functional mapping of epileptiform activity and eloquent cortices in individual patients.     

Sleep disturbances in highly stress reactive mice: Modeling endophenotypes of major depression

Background  

Neuronal mechanisms underlying affective disorders such as major depression (MD) are still poorly understood. By selectively breeding mice for high (HR), intermediate (IR), or low (LR) reactivity of the hypothalamic-pituitary-adrenocortical (HPA) axis, we recently established a new genetic animal model of extremes in stress reactivity (SR). Studies characterizing this SR mouse model on the behavioral, endocrine, and neurobiological levels revealed several similarities with key endophenotypes observed in MD patients. HR mice were shown to have changes in rhythmicity and sleep measures such as rapid eye movement sleep (REMS) and non-REM sleep (NREMS) as well as in slow wave activity, indicative of reduced sleep efficacy and increased REMS. In the present study we were interested in how far a detailed spectral analysis of several electroencephalogram (EEG) parameters, including relevant frequency bands, could reveal further alterations of sleep architecture in this animal model. Eight adult males of each of the three breeding lines were equipped with epidural EEG and intramuscular electromyogram (EMG) electrodes. After recovery, EEG and EMG recordings were performed for two days.