Integrated MEG and fMRI Model: Synthesis and Analysis

Summary: An integrated model for magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) is proposed. In the model, the neural activity is related to the Post Synaptic Potentials (PSPs) which is common link between MEG and fMRI. Each PSP is modeled by the direction and strength of its current flow which are treated as random variables. The overall neural activity in each voxel is used for equivalent current dipole in MEG and as input of extended Balloon model in fMRI. The proposed model shows the possibility of detecting activation by fMRI in a voxel while the voxel is silent for MEG and vice versa. Parameters of the model can illustrate situations like closed field due to non-pyramidal cells, canceling effect of inhibitory PSP on excitatory PSP, and effect of synchronicity. In addition, the model shows that the crosstalk from neural activities of the adjacent voxels in fMRI may result in the detection of activations in these voxels that contain no neural activities. The proposed model is instrumental in evaluating and comparing different analysis methods of MEG and fMRI. It is also useful in characterizing the upcoming combined methods for simultaneous analysis of MEG and fMRI. This work was supported in part by the Research Council of the University of Tehran, Tehran, Iran. The authors would like to thank Dr. John Moran from the Neurology Department, Henry Ford Health System, Detroit, Michigan, USA for his helpful discussions and kind assistance.     

独立成分分析在生物医学信号处理中的应用

独立成分分析(independentcomponentanalysis熏ICA)已经成功地应用到生物医学信号处理中,并被证明是一种分析生物医学信号的强有力的工具,近年来一直受到国内外学者的广泛关注。本文系统地介绍了独立成分分析在生物医学信号(EEG,MEG,fMRI)处理中的应用,分析了其应用方法,最后简要地探讨了独立成分分析应用到生物医学信号中的优势及存在的一些不足。     

一种基于独立成份分析的fMRI时-空模型数据处理方法

独立成份分析(ICA)是信号处理领域中斯近发展起来的一种很有应用前景的方法，而脑功能磁共振(fMRI)信号的有效分离与识别是一个正在研究和试验之中的技术领域。因此，发展基于ICA的fMRI数据处理方法具有明显的理论价值和应用前景。本文首先介绍了ICA原理，分析了现行ICA—fMRI方法采用的信号与噪声的空域分布相互独立的信号模型所存在的明显不足，然后提出了微域中的信号与噪声的时域过程相互独立的fMRI信号模型，从而建立了一种新的fMRI数据处理方法：邻域独立成份相关法。合理的fMRI实验数据处理结果验证了新方法的合理性。     

Localization of Broca's area using verb generation tasks in the MEG: Validation against fMRI

Functional MRI (fMRI) is routinely used to non-invasively localize language areas. Magnetoencephalography (MEG) is being explored as an alternative technique. MEG tasks to localize receptive language are well established although there are no standardized tasks to localize expressive language areas. We developed two expressive language tasks for MEG and validated their localizations against fMRI data. Ten right-handed adolescents (μ = 17.5 years) were tested with fMRI and MEG on two tasks: verb generation to pictures and verb generation to words. MEG and fMRI data were normalized and overlaid. The number of overlapping voxels activated in fMRI and MEG were counted for each subject, for each task, at different thresholding levels. For picture verb generation, there was 100% concordance between MEG and fMRI lateralization, and for word verb generation, there was 75% concordance. A count showed 79.6% overlap of voxels activated by both MEG and fMRI for picture verb generation and 50.2% overlap for word verb generation. The percentage overlap decreased with increasingly stringent activation thresholds. Our novel MEG expressive language tasks successfully identified neural regions involved in language production and showed high concordance with fMRI laterality. Percentage overlap of activated voxels was also high when validated against fMRI, but showed task-specific and threshold-related effects. The high concordance and high percentage overlap between fMRI and MEG activations confirm the validity of our new MEG task. Furthermore, the higher concordance from the picture verb generation task suggests that this is a promising task for use in the young clinical population.     

新的独立成分分析算法结合主成分分析实现fMRI信号的盲分离

用ICA算法来实现fMRI信号的盲源分离,可以提取出产生fMRI信号的多种源信号。但是在处理过程中存在两个困难:(1)fMRI数据的规模比较大,计算耗时;(2)计算量太大难免产生误差,给结果的分析带来不便。所以我们考虑对数据进行降维,但是如何确定源信号的个数也是一个难题。我们利用信息论的方法来估计源信号的个数,再使用主成分分析对数据进行降维。通过这样的处理,有效地确定了源信号的个数,减少了计算量。然后将一种新的ICA算法(New fixed-point,NewFP)用于处理降维后的数据。最后通过对实际的fMRI信号进行处理,结果表明新算法可以快速有效的分离fMRI信号,且准确性优于FastICA算法。     

An Improved Fixed-point Algorithm for Independent Component Analysis of Functional MRI Data

The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis （ICA）. However, it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging （fMRI） data. In this paper, an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed. In the new algorithms a small step size is added to increase the stability. In order to accelerate the convergence, an improvement on Newton method is made, which makes cubic convergence for the new algorithm. Applying the algorithm and two other algorithms to invivo fMRI data, the results show that the new algorithm separates independent components stably, which has faster convergence speed and less computation than the other two algorithms. The algorithm has obvious advantage in processing fMRI signal with huge data.     

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short periodȁ9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.     

基于多目标优化约束独立成分分析方法的fMRI数据分析研究

约束独立成分分析(CICA)通过加入先验信息,可极大地提高独立成分分析(ICA)的盲源信号分析性能,但还存在先验信息难以获取、先验信息约束条件阈值参数难以选择以及先验信息难以被有效利用等问题,需要进一步研究和解决.在多目标优化框架的基础上,建立一种同时融合时空先验信息的CICA模型,可有效规避CICA中阈值参数选择的问...     

人脑对握力刺激响应特征的数值计算分析方法

为研究人脑对握力刺激的响应特征,提出一种新的数值计算分析方法：结合独立成分分析和云模型,对握力刺激脑响应特征进行数值计算。采集10名健康受试者不同握力任务下的功能磁共振（fMRI）数据并进行预处理,应用独立成分分析获取不同握力刺激条件下的脑激活区域位置和大小,然后通过云模型计算脑激活区域内的数据分布特征。结果表明,握力刺激的脑激活区域主要分布在对侧大脑Brodmann 2、3、4、6区和同侧小脑,并且随着握力强度的增加,中央前回、中央后回等激活区域增大(激活簇体素个数分别为4 075、4 218、4 965);在不同握力刺激条件下,激活区域的任务态与非任务态间的期望、熵、超熵（Ex、En、He）均有明显的统计学差异,Ex（P<0001）和En(P<0.005)增大,He(P<0.005)减小;不同握力刺激间三个参数的差异不明显,并且非激活区域内任务状态与非任务状态间的期望、熵、超熵均无统计学差异。该方法可为不同任务下大脑激活区域的数据分布特征研究提供一种新的分析手段。     

Analysis of fMRI data by blind separation of data in a tiny spatial domain into independent temporal component

Independent Component Analysis (ICA) is a promising tool for the analysis of functional magnetic resonance imaging (fMRI) time series. In these studies, mostly assumed is a spatially independent component map of fMRI data (spatial ICA). In this paper, we assume that the temporal courses of the signal and noises are independent within a Tiny spatial domain (temporal ICA). Then with fast-ICA algorithm, spatially neighboring fMRI data were blindly separated into several temporal courses and were preassumed to be formed by a signal time course and several noise time courses where the signal has the largest correlation coefficient with the reference signal. The final functional imaging was completed for the signals obtained from each voxel. Simulations showed that compared with the spatial ICA method, the new temporal ICA method is more effective than the spatial ICA in detecting weak signal in a fMRI dataset. As background noise, the simulations include simulated Gaussian noise and fMRI data without stimulation. Finally, vivo fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex and that evoked by auditory stimuli are mainly in the region of the primary temporal cortex.     

Reduction of noise from magnetoencephalography data

A noise reduction method for magnetoencephalography (MEG) data is proposed. The method is a combination of Kalman filtering and factor analysis. A statespace model for a Kalman filter was constructed using the forward problem in MEG measurement. Factor analysis provide estimations of noise covariances required by the Kalman filter to eliminate independent additive sensor noise. The proposed method supports independent component analysis (ICA), which is difficult to use in MEG analysis owing to the sensor noise. Numerical experiments were conducted to investigate the performance of the proposed method. In a single dipole case where the maximum signal-to-noise ratio (SNR) was — 10 dB, approximately equivalent to raw MEG data, noise-free signals were successfully estimated from noisy data; a 0.02 s delay of the peak latency and 15–40% of attenuation of the peak amplitude were observed. Moreover, in a multiple dipole case, independent components preprocessed with the proposed method had high correlation, 0.88 at the lowest, with correlation of 0.69 and 0.52 for those preprocessed with conventional bandpass filters. The results show that the noise reduction method reduces sensor noise effectively. High SNR-independent components are obtained by the proposed method. Real MEG data analysis was also demonstrated. The proposed method extracted auditory evoked responses from unaveraged single-trial data.     

基于独立成分分析的时间自相关方法在功能磁共振激活区自适应提取中的应用

传统基于ICA的激活区检测手段是将分离后的独立成分与参考信号做相关性分析。实际问题中,不同区域的脑血流动力学响应情况不同,因此往往得不到标准的参考信号。针对此类问题,提出时间自相关方法(TSC)与ICA方法结合,在不需要参考信号的情况下,通过检测体素点各周期的时间序列相关性,对fMRI数据进行激活区提取。应用5 邻域ICA方法对fMRI数据逐点处理,然后应用时间自相关算法检测各时间序列周期间的相关性,选择最大的自相关系数作为该体素点的信号值。再通过Z变换将相关系数分布转换为服从N(0,1)的Z分布,提取出具有显著性差异（a=0.05）的激活区。将自相关算法应用于仿真数据和12组双手握拳运动的真实fMRI数据的处理,结果表明该方法能够准确提取出仿真数据中的激活区。对真实数据的处理,该方法在空间准确性上与GLM方法无显著性差别(0.4653±0.1368 vs 0.4905±0.1341),在时间准确性上显著优于GLM方法 (0.6364±0.0111 vs 0.3692±0.0109),具有良好的脑功能激活区检测及空间定位能力。     

Decomposition of working memory-related scalp ERPs: crossvalidation of fMRI-constrained source analysis and ICA.

Both functional magnetic resonance imaging (fMRI)-constrained source analysis and independent component analysis (ICA) claim to estimate the neuronal sources of electroencephalographic (EEG) scalp signals. In fMRI-constrained source analysis, event-related potential (ERP) generator locations are defined by fMRI activation patterns, and their contribution to the scalp ERP signal is probed. In contrast, ICA assumes that networks of cortical generators can be separated on the basis of their statistical independence. While good arguments can be put forward to justify both approaches, it is unclear how results from both methods compare. A clarification of these issues is of utmost importance to reconcile findings made using identical paradigms but these two complementary analysis methods. As both methods share the concept of spatially static sources a natural space to compare both methods and to crossvalidate the respective findings is at the level of source activity in the form of dipole source waves and independent component time courses and their corresponding maps. We used fMRI-constrained source analysis and ICA followed by clustering using the Kuhn-Munkres algorithm to analyze data from a working memory experiment. We demonstrate that crossvalidation is indeed possible using an appropriate statistical test. However, the sensitivity of this crossvalidation approach is ultimately limited by the low number of dimensions that contribute significant variance to the grand average scalp ERP. We conclude that testing at the single-subject level is preferable for crossvalidation purposes if the signal-to-noise ratio of the data allows for this approach.     

Two Dimensional Inverse Imaging (2DII) of Current Sources in Magnetoencephalography

A new magnetoencephalographic (MEG) technique for imaging the cortical distribution of neuronal activity is described. An iterative algorithm is employed, which successively alters an initial estimate of cortical source structure until it corresponds to the measured magnetic field data. In this new technique, the continuum of electrical activity across the cortical surface is modeled as a dense grid of thousands of single equivalent current dipoles. MEG imaging of both compact and extended sources is facilitated by a wavelet-like transformation of the source space into a sequence of successively smaller composite source structures. Two of these composite source structures are combined during each iterative step to generate an improved estimate of the cortical source structure. Thus, inversion of the complete gain matrix corresponding to thousands of cortical sources is not performed. The technique requires only moderate PC based resources even for very large source grids. In contrast to minimum norm MEG imaging methods, this new algorithm is insensitive to random noise in the data. If available, prior knowledge of source structure from other imaging techniques, such as PET, MRI and fMRI, is easily incorporated as additional constraints on the source structure solution. Source images solutions corresponding to simulated data are presented. In addition, the technique is applied to source imaging of real MEG data incorporating cortical structure from volumetric MRI data. These results demonstrate the capability of our new technique for imaging combinations of compact and extended source structures.     

Reliability and Validity of Functional Neuroimaging Techniques for Identifying Language-Critical Areas in Children and Adults

Advances in neuroimaging technologies over the last 15 years have prompted their relatively widespread use in the study of brain mechanisms supporting language function in children and adults. We reviewed reliability and external validity studies of 3 of the most common functional imaging methods, functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and positron emission tomography (PET). Although reliability and validity reports for fMRI are generally quite favorable, significant variability was found across studies with respect to methodology, preventing in some cases either the assessment of the reliability of individual datasets, or cross-study comparisons. Reliability and validity reports of MEG are strong, yet methodological questions regarding optimal modeling techniques remain. PET investigators report good concordance of language maps with data from more invasive brain mapping techniques, but its use of radioactive tracers and poorer spatial and temporal resolution make it the least optimal of the 3 methods for language mapping. Investigations of the cortical networks supporting language function during development and into adulthood should be viewed in the context of the validity and reliability of the methods used, with careful attention to details regarding the methodologies employed in the acquisition and analysis of statistical maps.     

Reliability and validity of functional neuroimaging techniques for identifying language-critical areas in children and adults

Advances in neuroimaging technologies over the last 15 years have prompted their relatively widespread use in the study of brain mechanisms supporting language function in children and adults. We reviewed reliability and external validity studies of 3 of the most common functional imaging methods, functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and positron emission tomography (PET). Although reliability and validity reports for fMRI are generally quite favorable, significant variability was found across studies with respect to methodology, preventing in some cases either the assessment of the reliability of individual datasets, or cross-study comparisons. Reliability and validity reports of MEG are strong, yet methodological questions regarding optimal modeling techniques remain. PET investigators report good concordance of language maps with data from more invasive brain mapping techniques, but its use of radioactive tracers and poorer spatial and temporal resolution make it the least optimal of the 3 methods for language mapping. Investigations of the cortical networks supporting language function during development and into adulthood should be viewed in the context of the validity and reliability of the methods used, with careful attention to details regarding the methodologies employed in the acquisition and analysis of statistical maps.     

An image fusion algorithm based on multi-resolution decomposition for functional magnetic resonance images

This paper presents a new functional image fusion algorithm which is the combination of SPM and ICA using multi-resolution decomposition. Firstly, we designed the fMRI experiments and obtained the fMRI image data from different experimental conditions. The brain activated regions were extracted by the SPM and ICA methods respectively. Secondly, by constructing the Laplacian pyramids of the source image, a new fusion rule based on the salience and matching measure is proposed in various resolutions. Finally, the fused functional images are reconstructed by the inverse Laplacian pyramid transformation. The results show that the algorithm can retain the details of the source images and pinpoint exactly the brain functional area associated with the hand action, thus outperforming SPM or ICA for functional regions extraction.     

Robust extraction of P300 using constrained ICA for BCI applications

P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.     

空间ICA在fMRI数据上的应用与分析

独立成分分析(ICA)技术试图将多维数据分解成若干个相互统计独立的分量。时间ICA和空间ICA都可以用于分析功能核磁共振成像(fMRI)数据。但由于fMRI数据空间维数远远大于时间维数，为计算方便，在分析fMRI数据时。则更多的使用空间ICA方法。本文在单任务激励实验中，利用ICA方法从fMRI数据中分离出若干个与任务相关的独立分量，其中包括与任务相关的恒定分量(CTR)和与任务相关的暂态分量(TTR)；通过将这些独立分量进行空间映射，得到了与任务相关的脑部激活区域。将此结果与SPM的分析比较，得到了一致的结果。在对结果的分析中，我们进一步指出了ICA方法的特点和局限性。     

基于卷积混合盲源分离技术的表面肌电信号分解研究

根据表面肌电信号（SEMG）形成的生理学特性，采用一种基于卷积混合过程的盲源分离技术来分析隐含在SEMG信号中的运动单位动作电位信息，利用仿真的SEMG信号对这种算法的分解性能进行实验研究，并与采用瞬时混合过程的独立分量分析（ICA）算法的分解性能进行比较，同时将该算法应用于真实SEMG信号的分解实验。研究结果表明，无论是对模拟SEMG信号还是真实SEMG信号，采用卷积混合盲源分离技术的分解方法均能得到较明显的分解效果，且该方法较符合表面肌电信号的形成过程，因而具有重要的研究价值。