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.     

Semantic memory involvement in the default mode network: a functional neuroimaging study using independent component analysis

The Default Mode Network (DMN) is a higher order functional neural network that displays activation during passive rest and deactivation during many types of cognitive tasks. Accordingly, the DMN is viewed to represent the neural correlate of internally-generated self-referential cognition. This hypothesis implies that the DMN requires the involvement of cognitive processes, like declarative memory. The present study thus examines the spatial and functional convergence of the DMN and the semantic memory system. Using an active block-design functional Magnetic Resonance Imaging (fMRI) paradigm and Independent Component Analysis (ICA), we trace the DMN and fMRI signal changes evoked by semantic, phonological and perceptual decision tasks upon visually-presented words. Our findings show less deactivation during semantic compared to the two non-semantic tasks for the entire DMN unit and within left-hemispheric DMN regions, i.e., the dorsal medial prefrontal cortex, the anterior cingulate cortex, the retrosplenial cortex, the angular gyrus, the middle temporal gyrus and the anterior temporal region, as well as the right cerebellum. These results demonstrate that well-known semantic regions are spatially and functionally involved in the DMN. The present study further supports the hypothesis of the DMN as an internal mentation system that involves declarative memory functions.     

Group independent component analysis of language fMRI from word generation tasks

Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be particularly relevant to pre-surgical planning, by falsely indicating low surgical risk in areas where no activations are shown. Furthermore, since the execution of language paradigms involves cognitive processes other than language function per se, the conventional general linear model (GLM) method may identify non-language-specific activations. In this study, we assessed an exploratory approach, independent component analysis (ICA), as a potential complementary method to the inferential GLM method in language mapping applications. We specifically investigated whether this approach might reduce type II error as well as generate more language-specific maps. Fourteen right-handed healthy subjects were studied with fMRI during two word generation tasks. A similarity analysis across tasks was proposed to select components of interest. Union analysis was performed on the language-specific components to increase sensitivity, and conjunction analysis was performed to identify language areas more likely to be essential. Compared with GLM, ICA identified more activated voxels in the putative language areas, and signals from other sources were isolated into different components. Encouraging results from one brain tumor patient are also presented. ICA may be used as a complementary tool to GLM in improving pre-surgical language mapping.     

Errors and artifacts encountered in the monitoring of fetal respiratory movements using untrasound.

A study is described of the possible sources of error and artifact arising in the monitoring of fetal respiratory movements using ultrasound, and their effect upon the clinical usefulness of the technique.Major artifacts arose from attempts to use the instrument controls to compensate for a poor choice of insonation direction, and from the use of a belt-mounted transducer.Critical requirements of the necessary instrumentation are discussed, together with the limitations of commercially-available equipment.     

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．     

基于独立元分析的脑磁图数据分析和处理(英文)

背景:诱发响应信号是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脑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,消除这些干扰成分,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。     

Automatic detection of emboli in the TCD RF signal using principal component analysis

The transcranial Doppler (TCD) radio-frequency (RF) signal can provide additional information on events recorded during ultrasonic monitoring. Embolic signals appear as uniform and predictable shapes within the RF signal, enabling pattern recognition and image processing techniques to be used for their automated detection. This paper uses principal component analysis (PCA) to characterise the typical variation in embolic signal shape, within the RF signal, using training sets of in vitro and in vivo data. PCA techniques are then utilised to discriminate between previously unseen embolic and artifact signals. Although the results of this study show that the algorithms described in this paper do not yet have the accuracy required for their use in a clinical setting, it does demonstrate that this novel technique has the potential to be developed further. (E-mail: dhe@le.ac.uk)     

基于独立成分分析和最小模解的眼电伪迹去除

目的:在采集到的脑电信号中分离并去除眼电伪迹,为临床应用和认知研究提供真实的脑电数据。方法:应用一种基于独立成分分析和最小模解的处理算法来去除眼电伪迹的影响。首先利用最小模解求解头表电位的皮质等效源分布,然后对皮质等效源进行独立成分分解,最后将分解后去除眼电伪迹的等效源还原为头皮数据。结果:利用独立成分分析分解等效源中眼电成分时比在头皮上更加准确,得到了所有电极在没有眼电成分即600～1600ms时间段内处理前后的数据相关系数。结论:基于独立成分分析和最小模解的处理算法可以实现在保证脑电信号完整的前提下完全的去除眼电伪迹,能够在临床和认知研究中应用。     

Spatiotemporal independent component analysis of event-related fMRI data using skewed probability density functions.

We introduce two independent component analysis (ICA) methods, spatiotemporal ICA (stICA) and skew-ICA, and demonstrate the utility of these methods in analyzing synthetic and event-related fMRI data. First, stICA simultaneously maximizes statistical independence over both time and space. This contrasts with conventional ICA methods, which maximize independence either over time only or over space only; these methods often yield physically improbable solutions. Second, skew-ICA is based on the assumption that images have skewed probability density functions (pdfs), an assumption consistent with spatially localized regions of activity. In contrast, conventional ICA is based on the physiologically unrealistic assumption that images have symmetric pdfs. We combine stICA and skew-ICA, to form skew-stICA, and use it to analyze synthetic data and data from an event-related, left-right visual hemifield fMRI experiment. Results obtained with skew-stICA are superior to those of principal component analysis, spatial ICA (sICA), temporal ICA, stICA, and skew-sICA. We argue that skew-stICA works because it is based on physically realistic assumptions and that the potential of ICA can only be realized if such prior knowledge is incorporated into ICA methods.     

Automatic motion compensation of free breathing acquired myocardial perfusion data by using independent component analysis

Images acquired during free breathing using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) exhibit a quasiperiodic motion pattern that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. In this work, we present a method to compensate this movement by combining independent component analysis (ICA) and image registration: First, we use ICA and a time-frequency analysis to identify the motion and separate it from the intensity change induced by the contrast agent. Then, synthetic reference images are created by recombining all the independent components but the one related to the motion. Therefore, the resulting image series does not exhibit motion and its images have intensities similar to those of their original counterparts. Motion compensation is then achieved by using a multi-pass image registration procedure. We tested our method on 39 image series acquired from 13 patients, covering the basal, mid and apical areas of the left heart ventricle and consisting of 58 perfusion images each. We validated our method by comparing manually tracked intensity profiles of the myocardial sections to automatically generated ones before and after registration of 13 patient data sets (39 distinct slices). We compared linear, non-linear, and combined ICA based registration approaches and previously published motion compensation schemes. Considering run-time and accuracy, a two-step ICA based motion compensation scheme that first optimizes a translation and then for non-linear transformation performed best and achieves registration of the whole series in 32±12s on a recent workstation. The proposed scheme improves the Pearsons correlation coefficient between manually and automatically obtained time-intensity curves from .84±.19 before registration to .96±.06 after registration.     

脑电图的临床应用——附800例异常脑电图的临床分析

目的探讨脑电图检查的适应证,使脑电图得到更广泛的临床应用。方法对近几年来我科所做的所有中重度异常的脑电图共800例进行回顾分析。结果800例中中度广泛性异常423例,痫样放电190例,重度广泛性异常110例,局灶性异常40例,阵发性节律波16例,电静息16例,高峰矢律5例。病因:癫痫287例,病毒性脑炎及脑系感染202例,外伤88例,脑出血40例,脑梗死32例,头疼27例,头晕14例,病因不明28,其他病因约30多种82例。结论对脑电图敏感常见的病因有癫痫、脑炎、外伤、脑血管病、头痛、头晕,另外还有约30多种病因在对脑功能产生损害时脑电图也有明显反应。     

Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data

The present study aims at finding the optimal inverse solution for the bioelectromagnetic inverse problem in the absence of reliable a priori information about the generating sources. Three approaches to tackle this problem are compared theoretically: the maximum-likelihood approach, the minimum norm approach, and the resolution optimization approach. It is shown that in all three of these frameworks, it is possible to make use of the same kind of a priori information if available, and the same solutions are obtained if the same a priori information is implemented. In particular, they all yield the minimum norm pseudoinverse (MNP) in the complete absence of such information. This indicates that the properties of the MNP, and in particular, its limitations like the inability to localize sources in depth, are not specific to this method but are fundamental limitations of the recording modalities. The minimum norm solution provides the amount of information that is actually present in the data themselves, and is therefore optimally suited to investigate the general resolution and accuracy limits of EEG and MEG measurement configurations. Furthermore, this strongly suggests that the classical minimum norm solution is a valuable method whenever no reliable a priori information about source generators is available, that is, when complex cognitive tasks are employed or when very noisy data (e.g., single-trial data) are analyzed. For that purpose, an efficient and practical implementation of this method will be suggested and illustrated with simulations using a realistic head geometry.     

A semi-automatic method to determine electrode positions and labels from gel artifacts in EEG/fMRI-studies

The analysis of simultaneous EEG and fMRI data is generally based on the extraction of regressors of interest from the EEG, which are correlated to the fMRI data in a general linear model setting. In more advanced approaches, the spatial information of EEG is also exploited by assuming underlying dipole models. In this study, we present a semi automatic and efficient method to determine electrode positions from electrode gel artifacts, facilitating the integration of EEG and fMRI in future EEG/fMRI data models.In order to visualize all electrode artifacts simultaneously in a single view, a surface rendering of the structural MRI is made using a skin triangular mesh model as reference surface, which is expanded to a “pancake view”. Then the electrodes are determined with a simple mouse click for each electrode. Using the geometry of the skin surface and its transformation to the pancake view, the 3D coordinates of the electrodes are reconstructed in the MRI coordinate frame.The electrode labels are attached to the electrode positions by fitting a template grid of the electrode cap in which the labels are known. The correspondence problem between template and sample electrodes is solved by minimizing a cost function over rotations, shifts and scalings of the template grid. The crucial step here is to use the solution of the so-called “Hungarian algorithm” as a cost function, which makes it possible to identify the electrode artifacts in arbitrary order. The template electrode grid has to be constructed only once for each cap configuration.In our implementation of this method, the whole procedure can be performed within 15 min including import of MRI, surface reconstruction and transformation, electrode identification and fitting to template. The method is robust in the sense that an electrode template created for one subject can be used without identification errors for another subject for whom the same EEG cap was used. Furthermore, the method appears to be robust against spurious or missing artifacts. We therefore consider the proposed method as a useful and reliable tool within the larger toolbox required for the analysis of co-registered EEG/fMRI data.     

婴幼儿癫痫的临床和脑电图特点分析

目的研究各型婴幼儿癫痫发作特征、脑电图、病因、神经影像学的改变,探讨早期诊断方法及预后。方法对我院2003年6月-2008年1月门诊和住院诊治的125例3岁以内起病的婴幼儿癫痫患儿进行临床观察及脑电图监测,并进行CT或MRI等神经影像学及血生化、尿遗传代谢病筛查等辅助检查,随访治疗效果和远期预后。结果本组1岁以内发病占54．4％。全面性发作占37．6％,局灶性发作占53．6％,不能明确分类的发作占8．8％。症状性癫痫占58．4％,其中围生期异常是第一位病因,占症状性癫痫的50．68％;特发性癫痫占41．6％。脑电图背景活动正常占53．6％,异常占46．4％,发作问期EEG正常占24．8％,异常占75．2％。影像学检查异常占38．4％。结论婴幼儿以症状性癫痫多见,局灶性发作所占的比例高,发作形式多样,应与非癫痫性发作鉴别,视频脑电图检查对诊断及鉴别有重要意义。     

Detection of consistently task-related activations in fMRI data with hybrid independent component analysis

fMRI data are commonly analyzed by testing the time course from each voxel against specific hypothesized waveforms, despite the fact that many components of fMRI signals are difficult to specify explicitly. In contrast, purely data-driven techniques, by focusing on the intrinsic structure of the data, lack a direct means to test hypotheses of interest to the examiner. Between these two extremes, there is a role for hybrid methods that use powerful data-driven techniques to fully characterize the data, but also use some a priori hypotheses to guide the analysis. Here we describe such a hybrid technique, HYBICA, which uses the initial characterization of the fMRI data from Independent Component Analysis and allows the experimenter to sequentially combine assumed task-related components so that one can gracefully navigate from a fully data-derived approach to a fully hypothesis-driven approach. We describe the results of testing the method with two artificial and two real data sets. A metric based on the diagnostic Predicted Sum of Squares statistic was used to select the best number of spatially independent components to combine and utilize in a standard regressional framework. The proposed metric provided an objective method to determine whether a more data-driven or a more hypothesis-driven approach was appropriate, depending on the degree of mismatch between the hypothesized reference function and the features in the data. HYBICA provides a robust way to combine the data-derived independent components into a data-derived activation waveform and suitable confounds so that standard statistical analysis can be performed.     

脑电非线性分析在心算和定向研究中的应用

目的探讨心算和定向测试过程中脑电非线性动力学特性的变化规律,以及脑电非线性分析在认知过程研究中的作用.方法用关联维数(D2)、近似熵(ApEn)对30名健康成年人在安静闭眼、闭眼心算和闭眼定向3种状态下的脑电数据进行分析.结果认知作业过程相对于安静状态,D2和ApEn明显增高( P＜0.01);不同作业状态大脑功能活动的复杂度和参与的脑区不同;脑电非线性分析可以清晰展示认知过程中激活脑区的分布情况,及与认知作业相关的脑区活跃程度的变化.结论脑电非线性动力学分析方法适用于认知过程脑功能活动变化规律的研究,有助于了解大脑的工作机制.     

Bayesian statistics in medical research: an intuitive alternative to conventional data analysis

Statistical analysis of both experimental and observational data is central to medical research. Unfortunately, the process of conventional statistical analysis is poorly understood by many medical scientists. This is due, in part, to the counter-intuitive nature of the basic tools of traditional (frequency-based) statistical inference. For example, the proper definition of a conventional 95% confidence interval is quite confusing. It is based upon the imaginary results of a series of hypothetical repetitions of the data generation process and subsequent analysis. Not surprisingly, this formal definition is often ignored and a 95% confidence interval is widely taken to represent a range of values that is associated with a 95% probability of containing the true value of the parameter being estimated. Working within the traditional framework of frequency-based statistics, this interpretation is fundamentally incorrect. It is perfectly valid, however, if one works within the framework of Bayesian statistics and assumes a 'prior distribution' that is uniform on the scale of the main outcome variable. This reflects a limited equivalence between conventional and Bayesian statistics that can be used to facilitate a simple Bayesian interpretation based on the results of a standard analysis. Such inferences provide direct and understandable answers to many important types of question in medical research. For example, they can be used to assist decision making based upon studies with unavoidably low statistical power, where non-significant results are all too often, and wrongly, interpreted as implying 'no effect'. They can also be used to overcome the confusion that can result when statistically significant effects are too small to be clinically relevant. This paper describes the theoretical basis of the Bayesian-based approach and illustrates its application with a practical example that investigates the prevalence of major cardiac defects in a cohort of children born using the assisted reproduction technique known as ICSI (intracytoplasmic sperm injection).     

Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data

The simultaneous acquisition and subsequent analysis of EEG and fMRI data is challenging owing to increased noise levels in the EEG data. A common method to integrate data from these two modalities is to use aspects of the EEG data, such as the amplitudes of event-related potentials (ERP) or oscillatory EEG activity, to predict fluctuations in the fMRI data. However, this relies on the acquisition of high quality datasets to ensure that only the correlates of neuronal activity are being studied. In this study, we investigate the effects of head-motion-related artefacts in the EEG signal on the predicted T2*-weighted signal variation. We apply our analyses to two independent datasets: 1) four participants were asked to move their feet in the scanner to generate small head movements, and 2) four participants performed an episodic memory task. We created T2*-weighted signal predictors from indicators of abrupt head motion using derivatives of the realignment parameters, from visually detected artefacts in the EEG as well as from three EEG frequency bands (theta, alpha and beta). In both datasets, we found little correlation between the T2*-weighted signal and EEG predictors that were not convolved with the canonical haemodynamic response function (cHRF). However, all convolved EEG predictors strongly correlated with the T2*-weighted signal variation in various regions including the bilateral superior temporal cortex, supplementary motor area, medial parietal cortex and cerebellum. The finding that movement onset spikes in the EEG predict T2*-weighted signal intensity only when the time course of movements is convolved with the cHRF, suggests that the correlated signal might reflect a BOLD response to neural activity associated with head movement. Furthermore, the observation that broad-spectral EEG spikes tend to occur at the same time as abrupt head movements, together with the finding that abrupt movements and EEG spikes show similar correlations with the T2*-weighted signal, indicates that the EEG spikes are produced by abrupt movement and that continuous regressors of EEG oscillations contain motion-related noise even after stringent correction of the EEG data. If not properly removed, these artefacts complicate the use of EEG data as a predictor of T2*-weighted signal variation.     

霍乱弧菌的PCR方法检测及耐药性分析

目的建立PCR检测霍乱弧菌的快速方法,应用于水源、海产品霍乱弧菌快速检测;了解霍乱弧菌的耐药性。方法根据霍乱弧菌的肠毒素基因A亚单位(ctxA)的保守序列,设计引物,以副溶血弧菌和沙门菌为对照,建立PCR检测霍乱弧菌的快速方法,用于霍乱弧菌的快速检测;应用美国德灵公司生产的Walkway40微生物鉴定和药敏分析系统对70株霍乱弧菌进行耐药性检测。结果70株霍乱弧菌出现特异性荧光,其他菌不出现荧光,灵敏度高、特异性强,可在8h内作出诊断。霍乱弧菌对大部分抗生素敏感,对复方新诺明耐药率较高。结论PCR检测霍乱弧菌灵敏度高特异性强可用于水源、海产品霍乱弧菌快速检测;霍乱弧菌对大部分抗生素敏感。