改进的相对转移熵的癫痫脑电分析

脑电信号是由脑神经活动产生并且始终存在于中枢神经系统的自发性电位活动,是一种重要的生物电信号. 脑电信号是非常微弱的且是非线性的,脑电信号也具有时间不可逆性. 本文提出了一种新的基于正向序列转移概率与逆向序列转移概率的相对熵方法即相对转移熵方法,并应用此方法研究了正常脑电与癫痫脑电的不可逆性,实验结果显示癫痫患者的脑电信号的不可逆性明显小于正常人的脑电信号的不可逆性. 这说明改进的相对转移熵可以作为一个物理过程不可逆程度的度量参数,这使得应用脑电信号区分病人是否患有癫痫疾病具有积极指导意义. 关键词： 相对转移熵 脑电信号 符号化 时间不可逆性     

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer’s disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a “transitional change” between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.     

基于虚拟开车环境的自闭症儿童脑电样本熵

自闭症谱系障碍是一种涉及感觉、情感、记忆、语言、智力、动作等认知功能和执行功能障碍的精神疾病. 本文从神经工效学角度出发, 用虚拟开车环境作为复杂多任务激励源将大脑系统与人体动作控制等有机地结合起来, 通过对脑电信号的滑动平均样本熵分析来探索自闭症儿童在虚拟开车环境中的脑活动特征. 研究发现不论是休息状态还是开车状态, 自闭症患者的滑动平均样本熵总体上低于健康者, 尤其在前额叶、颞叶、顶叶和枕叶功能区, 表明自闭症儿童的行为适应性较低. 不过, 自闭症患者的开车状态与健康受试者的休息状态比较接近, 表明虚拟开车环境或许有助于自闭症患者的干预治疗. 此外, 自闭症患者在颞叶区呈现显著性右半球优势性. 本研究为进一步深入开展自闭症疾病的机理研究及其诊断、评估和干预等研究提供一种新的研究思路.     

EEG-fMRI mapping of asymmetrical delta activity in a patient with refractory epilepsy is concordant with the epileptogenic region determined by intracranial EEG

We studied a patient with refractory focal epilepsy using continuous EEG-correlated fMRI. Seizures were characterized by head turning to the left and clonic jerking of the left arm, suggesting a right frontal epileptogenic region. Interictal EEG showed occasional runs of independent nonlateralized slow activity in the delta band with right frontocentral dominance and had no lateralizing value. Ictal scalp EEG had no lateralizing value. Ictal scalp EEG suggested right-sided central slow activity preceding some seizures. Structural 3-T MRI showed no abnormality. There was no clear epileptiform abnormality during simultaneous EEG-fMRI. We therefore modeled asymmetrical EEG delta activity at 1-3 Hz near frontocentral electrode positions. Significant blood oxygen level-dependent (BOLD) signal changes in the right superior frontal gyrus correlated with right frontal oscillations at 1-3 Hz but not at 4-7 Hz and with neither of the two frequency bands when derived from contralateral or posterior electrode positions, which served as controls. Motor fMRI activations with a finger-tapping paradigm were asymmetrical: they were more anterior for the left hand compared with the right and were near the aforementioned EEG-correlated signal changes. A right frontocentral perirolandic seizure onset was identified with a subdural grid recording, and electric stimulation of the adjacent contact produced motor responses in the left arm and after discharges. The fMRI localization of the left hand motor and the detected BOLD activation associated with modeled slow activity suggest a role for localization of the epileptogenic region with EEG-fMRI even in the absence of clear interictal discharges.     

Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures

Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy.     

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%.     

Symbolic transfer entropy

We propose to estimate transfer entropy using a technique of symbolization. We demonstrate numerically that symbolic transfer entropy is a robust and computationally fast method to quantify the dominating direction of information flow between time series from structurally identical and nonidentical coupled systems. Analyzing multiday, multichannel electroencephalographic recordings from 15 epilepsy patients our approach allowed us to reliably identify the hemisphere containing the epileptic focus without observing actual seizure activity.     

An investigation of the relationship between BOLD and perfusion signal changes during epileptic generalised spike wave activity

In pathological conditions interpretation of functional magnetic resonance imaging (fMRI) results can be difficult. This is due to a reliance on the assumed coupling between neuronal activity and changes in cerebral blood flow (CBF) and oxygenation. We wanted to investigate the coupling between blood oxygen level dependant contrast (BOLD) and CBF time courses in epilepsy patients with generalised spike wave activity (GSW) to better understand the underlying mechanisms behind the EEG-fMRI signal changes observed, especially in regions of negative BOLD response (NBR). Four patients with frequent GSW were scanned with simultaneous electroencephalographic (EEG)-fMRI with BOLD and arterial spin labeling (ASL) sequences. We examined the relationship between simultaneous CBF and BOLD measurements by looking at the correlation of the two signals in terms of percentage signal change on a voxel-by-voxel basis. This method is not reliant on coincident activation. BOLD and CBF were positively correlated in patients with epilepsy during background EEG activity and GSW. The subject average value of the Delta CBF/Delta BOLD slope lay between +19 and +36 and also showed spatial variation which could indicate areas with altered vascular response. There was not a significant difference between Delta CBF/Delta BOLD during GSW, suggesting that neurovascular coupling to BOLD signal is generally maintained between states and, in particular, within areas of NBR.     

White matter abnormalities in children and adolescents with temporal lobe epilepsy

Background and Purpose

The widespread propagation of synchronized neuronal firing in seizure disorders may affect cortical and subcortical brain regions. Diffusion tensor imaging (DTI) can noninvasively quantify white matter integrity. The purpose of this study was to investigate the abnormal changes of white matter in children and adolescents with focal temporal lobe epilepsy (TLE) using DTI.

Materials and Methods

Eight patients with clinically diagnosed TLE and eight age- and sex-matched healthy controls were studied. DTI images were obtained with a 3-T magnetic resonance imaging scanner. The epileptic foci were localized with magnetoencephalography. Fractional anisotropy (FA), mean diffusivity (MD), parallel (λ_||) and perpendicular (λ_⊥) diffusivities in the genu of the corpus callosum, splenium of the corpus callosum (SCC), external capsule (EC), anterior limbs of the internal capsule (AIC), and the posterior limbs of the internal capsule (PIC) were calculated. The DTI parameters between patients and controls were statistically compared. Correlations of these DTI parameters of each selected structure with age of seizure onset and duration of epilepsy were analysed.

Results

In comparison to controls, both patients' seizure ipsilateral and contralateral had significantly lower FA in the AIC; PIC and SCC and higher MD, λ_|| and λ_⊥ in the EC, AIC, PIC and SCC. The MD, λ_|| and λ_⊥ were significantly correlated with age of seizure onset in the EC and PIC. λ_|| was significantly correlated with the duration of epilepsy in the EC and PIC.

Conclusion

The results of the present study indicate that children and adolescents with TLE had significant abnormalities in the white matter in the hemisphere with seizure foci. Furthermore, these abnormalities may extend to the other brain hemisphere. The age of seizure onset and duration of epilepsy may be important factors in determining the extent of influence of children and adolescents TLE on white matter.     

Clinical correlations: MRI and EEG

Main structural correlates of epileptogenesis include hippocampal sclerosis, cortical dysgenesis, foreign tissue lesions, gliosis, and dual pathology (a combination of any two). These structural abnormalities are now increasingly defined with MRI, enabling systematic EEG correlative analyses. Hippocampal atrophy (HA) and increased T₂ signal in medial temporal structures predict the presence of mesial temporal sclerosis with a high degree of sensitivity and specificity. In 50 patients with clinical evidence of temporal lobe epilepsy and isolated HA, ictal scalp EEG was concordant to the atrophic temporal lobe in 33, nonlateralizing in 12, obscured in 3, and bilateral in 2, but it was discordant in none. Earlier reports of higher levels of discordance may be ascribed to the presence of dual pathology or to differing MRI and EEG criteria for localization. In a more inclusive group of 101 patients with unilateral HA, ictal scalp EEG was obtained in 99. It was unlocalized in 53, localized elsewhere in 9, and localized to the atrophic temporal lobe in 38. Of those, 51 patients had intracranial EEG: 12 were unlocalized, 29 were localized to the atrophic hippocampus, and 9 were localized elsewhere. There is thus a rare but definite subgroup of patients with unilateral HA who have EEG localization elsewhere than the atrophy. The successful cure of seizures in half these patients after removal of the EEG focus confirms the importance of this observation and emphasizes the search for more dual pathology that has remained undetected on MRI. About 10% of the patients with HA have significant atrophy bilaterally, and several series have confirmed that surgical success is predicted by removal of the EEG identified seizure onset area, not the more or less atrophic hippocampus. In patients with other kinds of dual pathology, including HA and foreign tissue lesions or cortical dysgenesis, EEG is also paramount in predicting the site of epileptogenesis for surgical intervention. EEG correlates of cortical dysgenesis are heterogeneous, but EEG has potential to provide accurate localization of the site of epileptogenesis in foreign tissue lesions also. In a study of 59 lesional patients, a small number of patients with low grade astrocytomas and oligodendrogliomas consistently localized by EEG to an area elsewhere than the lesion, and failed seizure control when the lesion was removed. Although MRI can demonstrate the structural correlate of the epilepsy in many situations, rare patients, particularly with certain tumors, cortical dysgenesis, and dual pathology, require EEG for accurate localization.     

Neuronal mechanism of epileptogenesis in EL mouse

The convulsions of the EL mouse (EL) were described by Imaizumi et al. in 1954 and were established as epilepsy by Suzuki in 1976. The EL mouse has been kept as an inbred strain and is considered one of the best animal models originated in Japan. The mode of inheritance is autosomal dominant, and environmental risk factors for seizure occurrence are hypothesised to contribute to the polygenic background. Paroxysmal activities in the EL brain arise from the parietal cortex (PCX) and are augmented in the hippocampus, demonstrated by electrophysiology and autoradiography using 2-deoxy glucose when clinical symptoms of seizures appeared. The neurons in the EL PCX, where GABA activity is lower than that of DDY PCX demonstrate increased excitability to proprioceptive sensory input. After repetitive seizure-provoking stimuli, seizures are more easily induced, eventually occurring spontaneously. This phenomenon of “abnormal plasticity” is also observed in the EEG, decreasing GABA activity, expression of the immediately early gene, and various biochemical and molecular processes. This phenomenon is similar to the learning or progressive process of certain neurological diseases.     

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.     

Clinical correlations with hippocampal atrophy

There is now a consensus that magnetic resonance imaging (MRI) is a sensitive and specific indicator of mesial temporal sclerosis (MTS) in patients with partial epilepsy. MTS is the most common pathological finding underlying the epileptogenic zone in patients undergoing temporal lobe surgery for medically refractory partial seizures. MRI-based hippocampal volumetric studies (i.e., quantitative MRI), has been shown to provide objective evidence for hippocampal atrophy in patients with MTS. The hippocampal volume in the epileptic temporal lobe has correlated with the neuronal cell densities in selected hippocampal subfields. A history of febrile seizures in childhood and age of unprovoked seizure onset have been associated with MRI-based hippocampal volumetry. There is conflicting evidence regarding the relationship between the duration of the seizure disorder and volumetry. Quantitative MRI has compared favorably to other noninvasive techniques (e.g., scalp-recorded EEG), in indicating the diagnosis of medical temporal lobe epilepsy (MTLE). MRI-identified hippocampal atrophy has also been a favorable prognostic indicator of seizure outcome after temporal lobe surgery. The presence of hippocampal atrophy appears to serve an in vivo surrogate for the presence of MTS.     

基于频率切片小波变换和支持向量机的癫痫脑电信号自动检测

实现癫痫脑电信号的自动检测对癫痫的临床诊断和治疗具有重要意义.本文提出先使用频率切片小波变换分离出5个不同频段的节律信号,再分别计算每个节律信号的近似熵和相邻节律的波动指数,最后使用遗传算法优化的支持向量机进行分类.实验结果表明,所提出的方法能够对正常、癫痫发作间期和癫痫发作期三种脑电信号进行准确分类,分类准确率为98.33%.     

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.     

Application of spectroscopic imaging in epilepsy

Functional and anatomical neuroimaging has had a dramatic effect on the evaluation of patients for seizure surgery. The demonstration by PET that the epileptogenic focus has interictal metabolic abnormalities has allowed a greater number of patients to come to seizure surgery, with fewer of these patients requiring intracranial electrode evaluations. Metabolic changes have also been demonstrated utilizing single voxel and whole brain ¹H and ³¹P MRS imaging techniques with the interictal focus characterized by increased Pi, pH, and decreased PME and NAA. These findings can be used to accurately lateralize temporal lobe as well as frontal lobe epilepsy. Furthermore, there is evidence that these findings can be used to localize the seizure focus with the changes specific for the epileptogenic region; although, more diffuse changes both ipsilaterally and contralaterally have been seen. In patients with anterior hippocampal seizure foci the pH is significantly alkaline only in the ipsilateral hippocampus, whereas the increased Pi and decreased PME can be seen throughout the ipsilateral temporal lobe. When compared to controls the contralateral hemisphere is acidotic. Decreased NAA concentrations as well as NAA/Cr ratios have been demonstrated in the epileptogenic region in temporal and frontal lobe epilepsy. The decreased NAA has been correlated with the severity of cell loss, and may be a more sensitive measure than qualitative or quantitative measures of the hippocampal atrophy; however, the NAA decrease is more widespread than just the epileptogenic focus but may be maximal at the site of seizure initiation. In preliminary work, NAA maps of deviation from normality have suggested the maximal change to coincide with the epileptogenic region. These results suggest that in focal epilepsy there is abnormal metabolic activity throughout the brain detectable by MRS, with patterns of metabolic asymmetry that are useful for seizure localization.     

基于递归量化分析与支持向量机的癫痫脑电自动检测方法

癫痫脑电信号的自动检测对癫痫的临床诊断与治疗具有重要意义.基于递归图(recurrence plot)的递归量化分析(recurrence quantification analysis,RQA)重现了非线性时间序列的动力学行为,分析了其递归特性,本文提出了基于RQA的癫痫脑电信号特征提取方法.实验结果表明:直接基于RQA特征的癫痫脑电的检测准确率较高,其中直接基于确定率DET的分类准确率可达到90.25%.本文还把提取的RQA特征值和变化系数、波动指数相结合组成特征向量,输入到SVM分类器,实现癫痫脑电信号的自动检测;实验结果表明:该方法的分类准确率可达到99%.     

Working Memory Decline in Alzheimer’s Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Alzheimer’s disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey–Osterrieth complex figure and Raven’s progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.     

Multi-Scale Permutation Entropy: A Potential Measure for the Impact of Sleep Medication on Brain Dynamics of Patients with Insomnia

Insomnia is a common sleep disorder that is closely associated with the occurrence and deterioration of cardiovascular disease, depression and other diseases. The evaluation of pharmacological treatments for insomnia brings significant clinical implications. In this study, a total of 20 patients with mild insomnia and 75 healthy subjects as controls (HC) were included to explore alterations of electroencephalogram (EEG) complexity associated with insomnia and its pharmacological treatment by using multi-scale permutation entropy (MPE). All participants were recorded for two nights of polysomnography (PSG). The patients with mild insomnia received a placebo on the first night (Placebo) and temazepam on the second night (Temazepam), while the HCs had no sleep-related medication intake for either night. EEG recordings from each night were extracted and analyzed using MPE. The results showed that MPE decreased significantly from pre-lights-off to the period during sleep transition and then to the period after sleep onset, and also during the deepening of sleep stage in the HC group. Furthermore, results from the insomnia subjects showed that MPE values were significantly lower for the Temazepam night compared to MPE values for the Placebo night. Moreover, MPE values for the Temazepam night showed no correlation with age or gender. Our results indicated that EEG complexity, measured by MPE, may be utilized as an alternative approach to measure the impact of sleep medication on brain dynamics.     

Continuous EEG-fMRI in patients with partial epilepsy and focal interictal slow-wave discharges on EEG

Purpose

To verify whether in patients with partial epilepsy and routine electroenecephalogram (EEG) showing focal interictal slow-wave discharges without spikes combined EEG–functional magnetic resonance imaging (fMRI) would localize the corresponding epileptogenic focus, thus providing reliable information on the epileptic source.

Methods

Eight patients with partial epileptic seizures whose routine scalp EEG recordings on presentation showed focal interictal slow-wave activity underwent EEG–fMRI. EEG data were continuously recorded for 24 min (four concatenated sessions) from 18 scalp electrodes, while fMRI scans were simultaneously acquired with a 1.5-Tesla magnetic resonance imaging (MRI) scanner. After recording sessions and MRI artefact removal, EEG data were analyzed offline. We compared blood oxygen level-dependent (BOLD) signal changes on fMRI with EEG recordings obtained at rest and during activation (with and without focal interictal slow-wave discharges).

Results

In all patients, when the EEG tracing showed the onset of focal slow-wave discharges on a few lateralized electrodes, BOLD-fMRI activation in the corresponding brain area significantly increased. We detected significant concordance between focal EEG interictal slow-wave discharges and focal BOLD activation on fMRI. In patients with lesional epilepsy, the epileptogenic area corresponded to the sites of increased focal BOLD signal.

Conclusions

Even in patients with partial epilepsy whose standard EEGs show focal interictal slow-wave discharges without spikes, EEG–fMRI can visualize related focal BOLD activation thus providing useful information for pre-surgical planning.