利用相位同步聚类方法研究大脑功能连通性

功能连通性是指大脑不同区域的神经细胞群活动之间的瞬时相干性，最常用的方法是估计成对电极信号之间的相关或相干。本文基于相位同步聚类方法，利用头表脑电信号，估计全局功能连通性。应用到空间选择性注意实验的视觉诱发脑电数据中，结果表明，该方法能检测到三个连通源，并且在注意和非注意状态下，功能连通性具有明显的差异。     

Patterns of Functional Connectivity in the Brain in Temporal and Frontal Epilepsy

Neuroscience and Behavioral Physiology - Objectives. To identify the pattern of functional connectivity (FC) between areas and networks in the brain in patients with frontal and temporal foci of...     

Functional and Anatomical Connectivity of the Brain in Poststroke Aphasia

Neuroscience and Behavioral Physiology - This review addresses studies of the functional connectivity of the brain in the resting state after stroke and one of its sequelae – aphasia....     

Dynamic Sculpting of Brain Functional Connectivity Is Correlated with Performance

In this study, we examined the relationship between cortical coupling, reflected in event related partial coherence (ERPC) and cognitive processing speed while subjects performed a set of Raven's Progressive Matrices (RPM), a task used to measure IQ. Fifty-five participants (29 males) performed a computerized version of the RPM where they were required to identify the shape (probe) that is consistent with a matrix of displayed shapes. Participants indicated a match or non-match by pressing a micro-switch with either the right or left hand. The steady state visually evoked potential (SSVEP) was elicited by a 13 Hz uniform visual flicker superimposed over the visual fields and the SSVEP event-related coherence (SSVEP-ERPC) calculated for all 2016 unique electrode pairs. The linear correlation between SSVEP-ERPC and processing speed (the inverse of reaction time) was calculated for all electrode pairs for all time points during the 3 sec interval that the probes were on the screen. Using correlation coeffident thresholds corresponding to p=0.001 we identified those electrode pairs where SSVEP-ERPC or neural synchronization was significantly correlated with processing speed. At a point 0.8 sec before the appearance of the probe we observed that the synchronization between specific prefrontal, frontal and central sites was correlated with processing speed. We suggest that this relationship may reflect the efficiency of working memory processes and speed of information processing.     

基于功能连接的逻辑计算认知任务脑默认功能网络的研究

目前功能连通(functional connectivity)已经发展为功能磁共振(functional MRI)研究脑认知活动的一个重要方法。我们采用时间相关方法,选取后扣带回(posterior cingulated cortex,PCC)为感兴趣区(regions ofinterest,ROI),提取ROI所有体素的平均时间信号,并与其他脑区血氧依赖的磁共振(BOLD)信号进行相关,同时去除全局效应和头动误差,对14例志愿者的计算和静息数据进行组内分析和组间分析,研究逻辑计算任务下默认网络(default mode network)改变情况。结果表明默认网络受到抑制,各脑区信号改变不一致,可能是由于逻辑计算状态下各脑区BOLD信号变化不同,与PCC时间相关性发生改变所致。     

Identification of Focal Epileptogenic Networks in Generalized Epilepsy Using Brain Functional Connectivity Analysis of Bilateral Intracranial EEG Signals

Simultaneous bilateral onset and bi-synchrony epileptiform discharges in electroencephalogram (EEG) remain hallmarks for generalized seizures. However, the possibility of an epileptogenic focus triggering rapidly generalized epileptiform discharges has been documented in several studies. Previously, a new multi-stage surgical procedure using bilateral intracranial EEG (iEEG) prior to and post complete corpus callosotomy (CC) was developed to uncover seizure focus in non-lateralizing focal epilepsy. Five patients with drug-resistant generalized epilepsy who underwent this procedure were included in the study. Their bilateral iEEG findings prior to complete CC showed generalized epileptiform discharges with no clear lateralization. Nonetheless, the bilateral ictal iEEG findings post complete CC indicated lateralized or localized seizure onset. This study hypothesized that brain functional connectivity analysis, applied to the pre CC bilateral iEEG recordings, could help identify focal epileptogenic networks in generalized epilepsy. The results indicated that despite diffuse epileptiform discharges, focal features can still be observed in apparent generalized seizures through brain connectivity analysis. The seizure onset localization/lateralization from connectivity analysis demonstrated a good agreement with the bilateral iEEG findings post complete CC and final surgical outcomes. Our study supports the role of focal epileptic networks in generalized seizures.     

抑郁症的客观判别:基于光学脑成像的静息态功能性连接检测和分析

近年来,基于功能性近红外光谱的静息态功能性连接逐渐用于精神疾病的研究。然而,由功能性近红外光谱得到的静息态功能性连接是否可以用于抑郁症的客观判别仍然是一个未知数。采用42通道的功能性近红外光谱技术,测量28个抑郁症患者和30个健康对照组的8 min前额皮层的自发血液动力活动。在独立成分分析和0.008～0.09 Hz的带通滤波器滤除不相关的成分后,计算前额皮层3个区域(额下回、额中回和额上回)左右半球连接性。然后,选择其中两个有显著性差异的参数作为样本的两个特征维度,并采用线性判别分析和支持向量机对随机抽取的75%样本进行训练,并对剩余的25%样本进行预测。最终均获得73%~74%的预测正确率和83%~87%的辨别率。这个结果支持由功能性近红外光谱技术得到的大脑静息态功能性连接在客观辨别抑郁症患者的可行性和有效性。     

基于自适应多尺度脑功能连接的局灶性癫痫发作检测方法研究

利用长时程脑电图检测癫痫发作是临床中较为广泛的应用,然而这项工作乏味、耗时,且很大程度上依赖于临床医生的自身经验和主观判断,准确性和可重复性也较低。针对长时程脑电图检测癫痫中存在的问题,提出一种基于自适应多尺度脑功能连接的癫痫发作检测方法(AMBFC),并选取10例局灶性癫痫患者的发作期和非发作期的样本作为研究对象。首先在一个滑动时间窗内,通过多元经验模态分解(MEMD)提取19通道脑电信号的7个本征模函数(IMF)分量及残差;然后建立多变量自回归(MVAR)模型,利用有向传递函数(DTF)提取流出信息强度,进行特征组合,并通过主成分分析(PCA)降维保留原始特征数目的85%;最后经代价敏感支持向量机(CSVM)分类区分发作期和非发作期脑电,并通过五重交叉验证进行癫痫发作检测算法的效果评价。结果表明,AMBFC算法检测脑电癫痫发作得到的平均准确率为98.6%,精确率为81.9%,召回率为81.4%,F2值为0.80。与各IMF分量、DTF-CSVM算法等检测结果相比,AMBFC算法更具有优越性。有望应用于长时程脑电的实时监测。     

基于脑网络的脑疲劳对不同工作记忆负荷的影响

工作记忆是认知行为的基础,脑疲劳会导致记忆力衰退、反应迟钝等认知表现下降的现象.利用N-back实验范式设计脑疲劳实验,采集15名被试正常态和疲劳态下4种工作记忆负荷的脑电数据,使用互信息算法构建功能性脑网络,计算并分析节点度、节点中间中心度、节点聚类系数、节点特征路径长度4个网络特征参数,对行为学数据(反应时间、正确...     

Connectivity Analysis and Feature Classification in Attention Deficit Hyperactivity Disorder Sub-Types: A Task Functional Magnetic Resonance Imaging Study

Attention deficit hyperactivity disorder (ADHD) is a pervasive neuropsychiatric disorder. Patients with different ADHD subtypes show different behaviors under different stimuli and thus might require differential approaches to treatment. This study explores connectivity differences between ADHD subtypes and attempts to classify these subtypes based on neuroimaging features. A total of 34 patients (13 ADHD-IA and 21 ADHD-C subtypes) underwent functional magnetic resonance imaging (fMRI) with six task paradigms. Connectivity differences between ADHD subtypes were assessed for the whole brain in each task paradigm. Connectivity measures of the identified regions were used as features for the support vector machine classifier to distinguish between ADHD subtypes. The effectiveness of connectivity measures of the regions were tested by predicting ADHD-related Diagnostic and Statistical Manual of Mental Disorders (DSM) scores. Significant connectivity differences between ADHD subtypes were identified mainly in the frontal, cingulate, and parietal cortices and partially in the temporal, occipital cortices and cerebellum. Classifier accuracy for distinguishing between ADHD subtypes was 91.18 % for both gambling punishment and emotion task paradigms. Linear prediction under the two task paradigms showed significant correlation with DSM hyperactive/impulsive score. Our study identified important brain regions from connectivity analysis based on an fMRI paradigm using gambling punishment and emotion task paradigms. The regions and associated connectivity measures could serve as features to distinguish between ADHD subtypes.     

Brain Reactions to Opening and Closing the Eyes: Salivary Cortisol and Functional Connectivity

Brain Topography - This study empirically assessed the strength and duration of short-term effects induced by brain reactions to closing/opening the eyes on a few well-known resting-state networks....     

Detecting Functional Connectivity in fMRI Using PCA and Regression Analysis

A fMRI connectivity analysis approach combining principal component analysis (PCA) and regression analysis is proposed to detect functional connectivity between the brain regions. By first using PCA to identify clusters within the vectors of fMRI time series, more energy and information features in the signal can be maintained than using averaged values from brain regions of interest. Then, regression analysis can be applied to the extracted principal components in order to further investigate functional connectivity. Finally, t-test is applied and the patterns with t-values lager than a threshold are considered as functional connectivity mappings. The validity and reliability of the presented method were demonstrated with both simulated data and human fMRI data obtained during behavioral task and resting state. Compared to the conventional functional connectivity methods such as average signal based correlation analysis, independent component analysis (ICA) and PCA, the proposed method achieves competitive performance with greater accuracy and true positive rate (TPR). Furthermore, the ‘default mode’ and motor network results of resting-state fMRI data indicate that using PCA may improve upon application of existing regression analysis methods in study of human brain functional connectivity.     

基于自回归模型和相位斜率指数的动态脑连接分析

随着脑网络理论的发展,人们越来越关注不同脑区域之间的功能性和效应性连接。在常见的脑活动无创检测方法中,脑电图（EEG）具有较高的时间分辨率,适于进行效应性连接分析。提出一种估计不同通道的EEG信号间的效应性连接的方法,即自回归相位斜率指数（AR-PSI）。该方法结合多元自回归（MVAR）模型对短时数据进行谱估计频率分辨率高的特点和相位斜率指数(PSI)对源信号混叠不敏感的特点。与传统的格兰杰因果模型相比,它可以有效地摒除由于容积导体效应造成的信号混叠所带来的干扰;与传统的PSI相比,它在短时数据上能够更准确地估计不同通道间EEG信号的效应性连接。首先,分别生成具有强效应性连接和相互独立的混叠噪声这两组信号进行模拟实验,结果表明AR-PSI方法确实能够更有效地检测出信号中存在的效应连接,排除信号线性混叠可能引起的误检。然后,应用此方法并结合滑动窗技术,对Stroop实验记录到的EEG数据进行网络连接的动态分析,发现250～500 ms和550～800 ms时间段内脑网络连接密度在两种条件下存在显著性差异。分析结果,显示语义和颜色不一致条件的刺激能够引起脑网络连接密度更迅速地增加,且连接跨度更大。     

基于独立成分分析和时间相关分析的脑功能连通研究方法[英文]

目的:利用静息状态功能磁共振成像(functional magnetic resonance imaging,fMRI)技术,提出联合独立成分分析(independent component analysis,ICA)和时间相关分析的人脑功能连通性研究方法。方法:首先采用空间ICA定位任务激活的脑区;然后选择一个激活区作为感兴趣区域(region of interest,ROI),采用时间相关分析方法检测静息状态大脑特定皮层的功能连通性,并通过检测人脑运动皮层的功能连通性验证方法的有效性。结果:大脑运动皮层功能连通网络包括初级运动区、辅助运动区、初级感觉皮层、背侧前运动区和后顶骨体觉区。实验结果表明,静息状态下,时间相关分析检测到的运动皮层的功能连通网络与已知的解剖连通相一致。结论:利用静息fMRI,结合空间ICA和时间相关分析方法,检测了静息时人脑运动皮层的功能连通网络,为脑区间功能连通的研究提供了一种简便的、无损的、有效的研究方法。     

基于独立成分分析和时间相关分析的脑功能连通研究方法

目的：利用静息状态功能磁共振成像（functional magnetic resonance imaging，fMRI）技术，提出联合独立成分分析（independent component analysis，ICA）和时间相关分析的人脑功能连通性研究方法。方法：首先采用空间ICA定位任务激活的脑区；然后选择一个激活区作为感兴趣区域（region of interest，ROI），采用时间相关分析方法检测静息状态大脑特定皮层的功能连通性，并通过检测人脑运动皮层的功能连通性验证方法的有效性。结果：大脑运动皮层功能连通网络包括初级运动区、辅助运动区、初级感觉皮层、背侧前运动区和后顶骨体觉区。实验结果表明，静息状态下。时间相关分析检测到的运动皮层的功能连通网络与已知的解剖连通相一致。结论：利用静息fMRI。结合空间ICA和时间相关分析方法。检测了静息时人脑运动皮层的功能连通网络。为脑区间功能连通的研究提供了一种简便的、无损的、有效的研究方法。     

事件相关电位在脑力疲劳研究中的应用

本文介绍了事件相关电位技术在脑力疲劳研究领域的应用情况。从一般认知负荷和特定环境下的脑力疲劳研究进展两个部分进行了综述,回顾了事件相关电位的电生理指标在认知加工活动中的作用,并分析了脑力疲劳对电生理指标的影响。