基于独立元分析的脑磁图数据分析和处理

背景：诱发响应信号足由刺激的时间锁定的，对于一些特定的刺激呈现小的个人差距，脑磁图数据中诱发响应的提取对人脑功能的认识很重要．目的：将独立元分析应用于分离混迭的脑磁图多通道信号中的信号源，提出一个简单有效的基于独立元分析的腑磁图数据分析和处理方法。设计：单一样本分析．单位：复旦大学电子工程系和复旦大学脑科学研究中心．对象：实验于2002—09在日本通信综合研究所关西先端研究中心完成，选择日本东京药科大学的健康志愿者1例，男性；年龄23岁。受试者自愿参加。方法：①对脑磁图进行必要的预处理，如低通滤波和主成分分解。②采用独立元分析的方法对取自148个通道的脑磁图的数据进行分析和处理，尤其是诱发反应的提取。③对提取的各独立成分进行周期平均。主要观察指标：应用独立元分析方法对脑磁图数据分析。结果：①脑磁图信号有较高的冗余度，信号能量的绝大部分集中在前30个主成分中，从前30个主成分中抽取干扰源和诱发响应活动源。②眼动干扰源仍被清楚地检测和分离在第1个独立元中，心电干扰被分离在第20个独立元中。③α波呈现在第2，3，7和9等独立元中。波（13-30Hz）呈现在第11和第12独立元中．④诱发响应是响应于刺激的周期性波形，集中在第5独立元中。结论：利用独立元分析，可从混迭的脑磁图数据中分离这些干扰源，更进一步，消除这些于扰成分，可得到净化的脑磁图数据。借助独立元分析，有效的分离α波、β波以及眼动、眨眼等神经活动源，有可能为它们的脑神经活动研究提供新的方法和途径．利用独立元分析方法成功的进行了听觉诱发反应的分离和提取．     

脑梗塞的脑磁图表现(附2例报告)

目的:分析2例脑梗塞患者的脑磁图表现,探讨脑磁图的研究现状以及在脑血管疾病方面的应用价值。方法:对2例经头颅CT或MRI确诊为脑梗塞的患者进行脑磁图检查。结果:患者于安静状态下行双侧腕部正中神经电刺激,所得手区体感皮质诱发反应,发现患侧半球M20诱发反应消失,M35反应波幅明显降低。结论:脑磁图能早期确定大脑功能损伤的程度和区域,对脑梗塞的早期诊断和适时治疗很有帮助。     

46例癫痫患者脑磁图和动态脑电图分析

脑磁图(MEG)是一种高效无侵袭性脑功能检测技术,近年来为癫痫患者的诊断和治疗提供了新的手段。目前临床常用的电生理检查包括常规脑电图、动态脑电图和视频脑电图等,其中以动态脑电图(AEEG)应用更为广泛,笔者总结本院46例癫痫患者MEG和AEEG资料,并将两种检查方法进行比较,旨在     

健康受试者脑磁图M100发生源大脑半球空间位置差异的研究

目的研究健康受试者脑磁图脑诱发磁场发生源大脑半球空间位置差异。方法对31例健康受试者(男27例,女4例)分别给予左、右耳纯音刺激,刺激强度为90dB,频率为2kHz,刺激间隔1s,持续时间8ms,记录脑听觉诱发磁反应(AEFs)。结果AEFs的主要波峰为M100,位于双侧大脑半球颞横回,两侧ECD位置由头坐标系统中的X、Y、Z轴表示,结果显示给予左耳纯音刺激,ECD在左、右侧大脑半球X、Z轴无显著性差异(P>0.05),ECD在左、右侧大脑半球Y轴有显著性差异(P<0.05),给予右耳纯音刺激,ECD在左、右侧大脑半球X、Z轴无显著性差异(P>0.05),ECD在左、右侧大脑半球Y轴有显著性差异(P<0.05)。结论健康青年人对纯音刺激的诱发磁反应左、右侧半球反应位置在Y轴存在显著性差异,M100位置均在Y轴显示不对称,即左侧M100位置位于颞横回偏后,右侧M100位置位于颞横回偏前。     

脑磁图对脑卒中后脑功能损害的评价作用

目的将近10年来脑磁图用于脑卒中研究的成果做一介绍.方法主要选择被Medline收录的外文文献和在国内核心杂志上发表的有关文献,就其内容进行分析、分类、归纳及总结.结果脑卒中后,其脑损伤部位出现异常低频磁活动,并有相应功能区受损表现.结论脑磁图能够对脑卒中损害的脑组织功能进行监测、定位及评估,并可用于受损神经功能重建和可塑性的研究.     

脑磁图表现对脑卒中患者脑功能损伤程度和区域的评估价值

目的:分析2例出血性脑卒中术后患者的脑磁图表现,探讨脑磁图的研究现状以及在脑血管疾病方面的应用价值。方法:2003-12/2004-01在广东三九脑科医院电生理中心脑磁图室对2例经CT确诊为脑出血并已行引流术的患者进行脑磁图检查。结果:患者于清醒、安静状态下行自发脑磁图检查,在患侧半球发现异常低频磁活动(ALFMA);手指电刺激所得体感皮质诱发反应,发现患侧半球反应波消失。结论:脑磁图能确定大脑功能损伤的程度和区域,对出血性脑卒中患者脑损伤的诊断及其指导早期康复治疗很有帮助。     

脑磁图在癫痫致痫灶及灶周脑功能区定位中的作用

目的：脑磁图是通过测定神经元兴奋产生的电流所伴随的磁场变化来确定异常放电的部位，了解其在癫痫致痫灶和痫灶周围脑功能区定位中的作用。资料来源：应用计算机检索Medline1998-07／2004-11与脑磁图和癫痫相关文章，检索词“Epilepsy，Magnetoencephalography，localization”，并限定文章语言种类为English。计算机检索VIP和CJFD1999／2004与脑磁图和癫痫相关的中文文章，检索词“癫痫，脑磁图，定位”。资料选择：对资料进行初审，纳入标准；①回顾性研究，无论是否设有对照组。②研究为随机抽样。开始查找全文，未排除是否为盲法。资料提炼：共收集到91篇相关文献（英文84篇，中文7篇）。54篇符合纳入标准。排除的37篇文章中34篇系重复研究，3篇为Meta分析。资料综合：54篇文章中14篇从偶极子定位方面阐述，13篇涉及脑磁图对癫痫患者手术前后评估，16篇与其他定位技术比较证明脑磁图的作用。11篇采取个案报道的形式证实脑磁图的定位价值。从中选取13篇有代表性的文献进行综述，认为作为无创性检测技术，脑磁图可检测到直径〈3．0mm的癫痫病理灶，分辨时相高达1．0ms，可以发现癫痫原发病灶与对侧对称位置出现的类似信号，即“镜像灶”，还能分辨发作间期一侧大脑半球的多个癫痫灶。脑磁图可用于癫痫发作期和发作间期致痫源的定位，还可对痫灶周围脑功能区进行定位，与脑电图比较，其使用更方便，更敏感。结论：脑磁图对各类癫痫的诊断，致痫灶、灶周脑功能区的定位，癫痫镜像灶的辨别具有明显的优势和广泛的应用前景。     

脑磁图对脑卒中后脑功能损害的评价作用

目的将近10年来脑磁图用于脑卒中研究的成果做一介绍。方法主要选择被Medline收录的外文文献和在国内核心杂志上发表的有关文献,就其内容进行分析、分类、归纳及总结。结果脑卒中后,其脑损伤部位出现异常低频磁活动,并有相应功能区受损表现。结论脑磁图能够对脑卒中损害的脑组织功能进行监测、定位及评估,并可用于受损神经功能重建和可塑性的研究。     

脑磁图在癫痫致痫灶及灶周脑功能区定位中的作用

目的:脑磁图是通过测定神经元兴奋产生的电流所伴随的磁场变化来确定异常放电的部位,了解其在癫痫致痫灶和痫灶周围脑功能区定位中的作用。资料来源:应用计算机检索Medline1998-07/2004-11与脑磁图和癫痫相关文章,检索词“Epilepsy,Magnetoencephalography,localization”,并限定文章语言种类为English。计算机检索VIP和CJFD1999/2004与脑磁图和癫痫相关的中文文章,检索词“癫痫,脑磁图,定位”。资料选择:对资料进行初审,纳入标准:①回顾性研究,无论是否设有对照组。②研究为随机抽样。开始查找全文,未排除是否为盲法。资料提炼:共收集到91篇相关文献(英文84篇,中文7篇)。54篇符合纳入标准。排除的37篇文章中34篇系重复研究,3篇为Meta分析。资料综合:54篇文章中14篇从偶极子定位方面阐述,13篇涉及脑磁图对癫痫患者手术前后评估,16篇与其他定位技术比较证明脑磁图的作用,11篇采取个案报道的形式证实脑磁图的定位价值。从中选取13篇有代表性的文献进行综述,认为作为无创性检测技术,脑磁图可检测到直径<3.0mm的癫痫病理灶,分辨时相高达1.0ms,可以发现癫痫原发病灶与对侧对称位置出现的类似信号,即“镜像灶”,还能分辨发作间期一侧大脑半球的多个癫痫灶。脑磁图可用于癫痫发作期和发作间期致痫源的定位,还可对痫灶周围脑功能区进行定位,与脑电图比较,其使用更方便,更敏感。结论:脑磁图对各类癫痫的诊断,致痫灶、灶周脑功能区的定位,癫痫镜像灶的辨别具有明显的优势和广泛的应用前景。     

脑磁图听觉诱发磁场等价电流偶极子强度在急性脑梗死的意义

目的:研究急性脑梗死时脑磁图听觉诱发磁场(AEFs)的等价电流偶极子(ECD)强度变化的意义。方法:应用脑磁图机对15例急性脑梗死患者进行AEFs检测,AEFs波峰由ECD评估。结果:AEFs的最主要波峰为M100,其ECD位于两侧颞横回,患侧ECD强度减小(P<0.01)。结论:急性脑梗死可使患侧AEFs的ECD强度下降,为评估听觉皮层功能受损提供客观、灵敏的指标。     

Linked independent component analysis for multimodal data fusion

In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data and searching for task- or disease-related changes in each modality separately. A major challenge in analysis is to find systematic approaches for fusing these differing data types together to automatically find patterns of related changes across multiple modalities, when they exist. Independent Component Analysis (ICA) is a popular unsupervised learning method that can be used to find the modes of variation in neuroimaging data across a group of subjects. When multimodal data is acquired for the subjects, ICA is typically performed separately on each modality, leading to incompatible decompositions across modalities. Using a modular Bayesian framework, we develop a novel "Linked ICA" model for simultaneously modelling and discovering common features across multiple modalities, which can potentially have completely different units, signal- and contrast-to-noise ratios, voxel counts, spatial smoothnesses and intensity distributions. Furthermore, this general model can be configured to allow tensor ICA or spatially-concatenated ICA decompositions, or a combination of both at the same time. Linked ICA automatically determines the optimal weighting of each modality, and also can detect single-modality structured components when present. This is a fully probabilistic approach, implemented using Variational Bayes. We evaluate the method on simulated multimodal data sets, as well as on a real data set of Alzheimer's patients and age-matched controls that combines two very different types of structural MRI data: morphological data (grey matter density) and diffusion data (fractional anisotropy, mean diffusivity, and tensor mode).     

A K-means multivariate approach for clustering independent components from magnetoencephalographic data

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering.     

Groupwise independent component decomposition of EEG data and partial least square analysis

This paper focuses on two methodological developments for analysis of neuroimaging data. The first is the derivation of robust spatiotemporal activity patterns across a group of subjects using a combination of principal component analysis (PCA) and independent component analysis (ICA). In applications to ERP data, the space dimension is typically represented in terms of scalp electrodes. The signal recorded by high density electrode caps is known to be highly correlated due in part to volume conduction. Consequently, this redundancy is also reflected in spatiotemporal patterns characterizing signal differences across experimental conditions. We present an alternative spatial representation and signal compression based on PCA for dimensionality reduction and ICA conducted across all subjects and conditions simultaneously. The second advancement is the use of partial least squares (PLS) analysis to assess task-dependent changes in the expression of the independent components. In an application to empirical ERP data, we derive an efficient number of independent component maps. Comparative PLS analysis on the independent components versus original electrode data shows that task effects are not only preserved under compression, but also enhanced statistically.     

基于经验模式分解与独立分量分析的心电信号消噪方法

针对小波独立分量分析法(W-ICA)在心电信号消噪中小波变换缺乏自适应性,且较难选取最优小波基的问题,提出了一种将经验模式分解与独立分量分析相结合的小波独立分量分析法.该方法结合经验模式分解与独立分量分析各自的优点,利用经验模式分解对心电信号进行自适应分解,然后应用独立分量分析法对选取的本征模态函数进行分离,将分离后的分量进行两层重构,从而得消噪后的心电信号.通过利用MIT-BIH心率失常数据库中的数据进行仿真实验,结果表明该方法可以较好地消除心电信号中的噪声,消噪后信号与原信号的相关系数可达0.96.     

基于独立分量分析提取仿真脑电诱发电位信号料

事件相关诱发电位信号的稳健提取一直是脑电信号处理领域的难题.独立分量分析算法是一种盲源分离技术,主要解决独立源的二维线性混合问题.文章设计了1组峭度不同的仿真脑电信号,采用扩展信息最大的独立分量分析算法提取仿真诱发电位信号.实验结果表明,仿真诱发电位信号分离前后的峭度接近,相关系数大于0.99.且分离后的诱发电位信号基本保持了原来波形的特征,能有效地将混合在诱发电位信号中的自发脑电信号、肌电干扰及工频干扰等信号分离开来,实现了微弱的诱发电位信号在强噪声中的有效提取,为真实事件相关诱发电位信号的提取提供了思路.     

A unified framework for group independent component analysis for multi-subject fMRI data

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT [Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151.] and tensor PICA [Beckmann, C.F., Smith, S.M., 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25, 294–311.], make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation–Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation.     

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.     

独立分量分析结合小波去噪算法提取诱发电位信号的仿真实验

采用结合独立分量分析和小波去噪算法的方法提取诱发电位信号.首先使用扩展信息最大的独立分量分析算法分析仿真的脑电信号,分离出诱发电位信号,自发脑电信号,肌电干扰与高斯噪声,然后使用小波阀值收缩算法滤除诱发电位信号中残留的一些高频噪声.仿真实验表明,基于独立分量分析的算法可以将混合在诱发电位信号中的干扰信号分离开来,而结合方法的提取结果在波形、相关系数指标等方面均优于单独使用独立分量分析算法的提取结果,这为实际临床应用中诱发电位的有效提取提供了思路.