Automatic independent component labeling for artifact removal in fMRI

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.     

Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis

The simultaneous recording of EEG and fMRI is a promising method for combining the electrophysiological and hemodynamic information on cerebral dynamics. However, EEG recordings performed in the MRI scanner are contaminated by imaging, ballistocardiographic (BCG) and ocular artifacts. A number of processing techniques for the cancellation of fMRI environment disturbances exist: the most popular is averaged artifact subtraction (AAS), which performs well for the imaging artifact, but has some limitations in removing the BCG artifact, due to the variability in cardiac wave duration and shape; furthermore, no processing method to attenuate ocular artifact is currently used in EEG/fMRI, and contaminated epochs are simply rejected before signal analysis. In this work, we present a comprehensive method based on independent component analysis (ICA) for simultaneously removing BCG and ocular artifacts from the EEG recordings, as well as residual MRI contamination left by AAS. The ICA method has been tested on event-related potentials (ERPs) obtained from a visual oddball paradigm: it is very effective in attenuating artifacts in order to reconstruct clear brain signals from EEG acquired in the MRI scanner. It performs significantly better than the AAS method in removing the BCG artifact. Furthermore, since ocular artifacts can be completely suppressed, a larger number of trials is available for analysis. A comparison of ERPs inside the magnetic environment with those obtained out of the MRI scanner confirms that no systematic bias in the ERP waveform is produced by the ICA method.     

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.     

利用独立成分分析实现成组的fMRI信号的盲分离

独立成分分析(ICA)作为盲源分离的一种有效方法已经被成功的用于处理功能磁共振成像(fMRI)数据,但是通常人们只是考虑处理单个被试的数据,对于多个被试的情况却很少有人考虑,本文中分析了目前国际上比较流行的三种用ICA来处理多个被试的fMRI数据的方法,并且利用其中最好的一种方法对我们实验中获得的fMRI数据进行处理,结果表明这种方法可以快速有效地处理多个被试的fMRI数据.     

基于扩展Infomax独立分量分析算法的脑电信号消噪

背景：脑电信号能够反映大脑不同的生理病理状态,但在采集和分析处理过程中极易受到各种噪声的干扰,如眼球运动、眨眼、心电、肌电等,这些噪声的存在严重影响了脑电信号的分析和处理。目的：介绍了一种基于扩展Infomax的独立分量分析方法,并用于脑电信号消噪。方法：通过扩展lnfomax算法的迭代求得分离矩阵,采用去除噪声分量后的独立成分重构需要记录的脑电信号,观察Matlab仿真得到的去噪后的脑电信号,同时比较去噪前后各导联脑电信号与眼电信号的相关陛。结果与结论：使用扩展Infomax独立分量分析算法能够成功地去除多导脑电信号中的眼电干扰。再比较去噪前后各导联脑电信号的功率谱,可以发现使用扩展lnfomax独立分量分析算法同时也能够成功地去除多导脑电信号中的工频干扰,且对脑电信号中的其他有用信号几乎没有破坏。     

Motion artifact in magnetic resonance imaging: implications for automated analysis

Automated measures of cerebral magnetic resonance images (MRI) often provide greater speed and reliability compared to manual techniques but can be particularly sensitive to motion artifact. This study employed an automatic MRI analysis program that quantified regional gray matter volume and created images for verification and quality control. Motion artifact was assessed on each image and assigned a rating of none, mild, moderate, or severe. Greater motion artifact was associated with smaller gray matter volumes. Severity of motion artifact is an important, but often overlooked, consideration in the interpretation of automated MRI measures.     

基于独立成分分析的精神分裂症患者动态脑功能网络分析新方法

目的 提出一种新的基于独立成分分析法进行动态脑功能网络分析的方法,并应用该方法探讨精神分裂症患者在动态全脑功能网络上的变异。方法 首先基于滑动时间窗方法计算正常被试和精神分裂症患者的动态全脑功能网络,然后使用组信息指导独立成分分析方法,提取每个被试的动态全脑功能网络的功能连接状态及相应的时间波动,比较正常被试和精神分裂症患者在功能连接状态上的差异。结果 两组的最重要功能连接状态的模式有相似性。正常被试在额叶、顶叶相关区域较精神分裂症患者具有更强的正功能连接;在小脑相关区域精神分裂症患者呈现出更多的正功能连接,而正常被试呈现出更多的负功能连接。结论 组信息指导独立成分分析方法可有效提取动态脑功能网络的功能连接状态,可揭示精神分裂症患者在动态脑功能网络的变异。     

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。     

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.     

基于链接独立成分分析的神经精神疾病多模态MRI研究进展

传统多模态磁共振分析通常是基于单模态数据,对其结果进行简单的比较或相关性分析,但各模态之间的先验交互信息未被充分利用。而链接独立成分分析(linked independent component analysis,LICA)是一种多模态数据融合方法,能够通用灵活地利用独立成分分析对多模态数据进行融合分析,允许每个模态组具有不同的单位、信噪比等,而且能够自动确定组内各模态的最佳权重,从而可以更充分地利用各模态之间的交互信息,在脑疾病研究中得到了广泛应用。本文就该方法在神经精神疾病的病理机制、临床诊断及分类识别等方面的MRI研究进展进行综述。     

基于独立成分分析的脑卒中感觉运动网络功能连接异常的研究

目的:运用独立成分分析(independent component analysis,ICA)探讨脑卒中偏瘫患者感觉运动网络功能连接变化。方法:收集33例慢性期左侧皮质下脑卒中患者和34例年龄、性别相匹配的健康志愿者的静息态功能磁共振成像(resting-state functional magnetic resonance imaging,rs-fMRI)数据,采用组ICA方法提取出健康对照组与脑卒中患者组的感觉运动网络,并运用双样本t检验(P0.05,AlphaSim校正)比较其组间差异。结果:与健康对照组相比,ICA提取出的感觉运动网络(包括背侧、腹侧、左侧和右侧感觉运动网络)在患者组均显示其内功能连接明显下降,具体涉及脑区有:背侧感觉运动网络内的左侧中央前回和右侧中央后回,腹侧感觉运动网络内的左侧中央后回,左侧感觉运动网络内的左侧辅助运动皮质和右侧中央后回,右侧感觉运动网络内的左侧中央前后回。结论:脑卒中偏瘫患者涉及全身感觉运动功能连接网络损伤,ICA为更加全面了解感觉运动功能损伤机制提供了一种新的有效途径。     

A new automated method for analysis of rCBF-SPECT images based on the active-shape algorithm: normal values

Most nuclear medicine clinicians use only visual assessment when interpreting regional cerebral blood flow (rCBF) from single-photon emission computed tomography (SPECT) images in clinical practice. The aims of this study were to develop a new, easy to use, automated method for quantification of rCBF-SPECT and to create normal values by using the method on a normal population. We developed a 3-dimensional method based on a brain-shaped model and the active-shape algorithm. The method defines the surface shape of the brain and then projects the maximum counts 0-1.5 cm deep for designated surface points. These surface projection values are divided into cortical regions representing the different lobes and presented relative to the whole cortex, cerebellum or cerebellar maximum. (99m) Tc-hexa methyl propylene amine oxime (HMPAO) SPECT was performed on 30 healthy volunteers with a mean age of 74 years (range 64-98). The ability of the active-shape algorithm to define the shape of the brain was satisfactory when visually scrutinized. The results of the quantification show rCBF values in the frontal, temporal and parietal lobes of 87-88% using cerebellum as the reference. There were no significant differences in normal rCBF values between male and female subjects and only a weak relation between rCBF and age. In conclusion, our new automated method was able to quantify rCBF-SPECT images and create normal values in ranges as expected. Further studies are needed to assess the clinical value of this method and the normal values.     

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.     

利用独立成分分析技术和静息fMRI数据对脑功能区进行定位

目的 验证可否利用独立成分分析(ICA)技术和静息fMRI数据对脑功能区进行定位.方法 利用ICA方法,通过研究静息状态的脑功能联结来获取功能区的定位.静息数据的采集采用短TR,在低通滤波(截止频率0.08 Hz)后可以去除生理噪声的主要影响.在数据分析中,对ICA结果进行了可复制性分析,只保留可复制性较高的成分,之后将ICA结果与传统的"种子像素"方法获得的结果进行定量的一致性分析.结果 ICA能够在不设定"种子像素"的情况下从静息fMRI数据中分解出运动系统和初级视觉系统的功能联结图,并在所有被试上都与"种子像素"方法有较高一致性.ICA在同一数据中可以同时分解出上述两个系统的功能联结图. 结论 ICA克服了"种子像素"方法的主观性,稳定、准确地从静息fMRI数据中分解出了脑功能联结图.本研究支持初级功能系统内的联系要明显强于系统间的联系的假设,显示ICA方法具有良好的临床应用潜力.     

Extraction of an input function from dynamic micro-PET images using wavelet packet based sub-band decomposition independent component analysis

Positron emission tomography (PET) can be used to quantify physiological parameters. However to perform quantification requires that an input function is measured, namely a plasma time activity curve (TAC). Image-derived input functions (IDIFs) are attractive because they are noninvasive and nearly no blood loss is involved. However, the spatial resolution and the signal to noise ratio (SNR) of PET images are low, which degrades the accuracy of IDIFs. The objective of this study was to extract accurate input functions from microPET images with zero or one plasma sample using wavelet packet based sub-band decomposition independent component analysis (WP SDICA). Two approaches were used in this study. The first was the use of simulated dynamic rat images with different spatial resolutions and SNRs, and the second was the use of dynamic images of eight Sprague-Dawley rats. We also used a population-based input function and a fuzzy c-means clustering approach and compared their results with those obtained by our method using normalized root mean square errors, area under curve errors, and correlation coefficients. Our results showed that the accuracy of the one-sample WP SDICA approach was better than the other approaches using both simulated and realistic comparisons. The errors in the metabolic rate, as estimated by one-sample WP SDICA, were also the smallest using our approach.     

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。     

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绍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。     

Lexical influences on speech perception: a Granger causality analysis of MEG and EEG source estimates

Behavioral and functional imaging studies have demonstrated that lexical knowledge influences the categorization of perceptually ambiguous speech sounds. However, methodological and inferential constraints have so far been unable to resolve the question of whether this interaction takes the form of direct top-down influences on perceptual processing, or feedforward convergence during a decision process. We examined top-down lexical influences on the categorization of segments in a /s/-/integral/ continuum presented in different lexical contexts to produce a robust Ganong effect. Using integrated MEG/EEG and MRI data we found that, within a network identified by 40 Hz gamma phase locking, activation in the supramarginal gyrus associated with wordform representation influences phonetic processing in the posterior superior temporal gyrus during a period of time associated with lexical processing. This result provides direct evidence that lexical processes influence lower level phonetic perception, and demonstrates the potential value of combining Granger causality analyses and high spatiotemporal resolution multimodal imaging data to explore the functional architecture of cognition.