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。     

A new method to identify multiple sources of oscillatory activity from magnetoencephalographic data

Identifying the sources of oscillatory activity in the human brain is a challenging problem in current magnetoencephalography (MEG) and electroencephalography (EEG) research. The fluctuations in phase and amplitude of cortical oscillations preclude signal averaging over successive sections of the data without a priori assumptions. In addition, several sources at different locations often produce oscillatory activity at similar frequencies. For example, spontaneous oscillatory activity in the 8- to 13-Hz band is produced simultaneously at least in the posterior parts of the brain and bilaterally in the sensorimotor cortices. The previous approaches of identifying sources of oscillatory activity by dipole modeling of bandpass filtered data are quite laborious and require that multiple criteria are defined by an experienced user. In this work we introduce a convenient method for source localization using minimum current estimates in the frequency domain. Individual current estimates are calculated for the Fourier transforms of successive sections of continuous data. These current estimates are then averaged. The algorithm was tested on simulated and measured MEG data and compared with conventional dipole modeling. The main advantage of the proposed method is that it provides an efficient approach for simultaneous estimation of multiple sources of oscillatory activity in the same frequency band.     

Validating the independent components of neuroimaging time series via clustering and visualization

Recently, independent component analysis (ICA) has been widely used in the analysis of brain imaging data. An important problem with most ICA algorithms is, however, that they are stochastic; that is, their results may be somewhat different in different runs of the algorithm. Thus, the outputs of a single run of an ICA algorithm should be interpreted with some reserve, and further analysis of the algorithmic reliability of the components is needed. Moreover, as with any statistical method, the results are affected by the random sampling of the data, and some analysis of the statistical significance or reliability should be done as well. Here we present a method for assessing both the algorithmic and statistical reliability of estimated independent components. The method is based on running the ICA algorithm many times with slightly different conditions and visualizing the clustering structure of the obtained components in the signal space. In experiments with magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) data, the method was able to show that expected components are reliable; furthermore, it pointed out components whose interpretation was not obvious but whose reliability should incite the experimenter to investigate the underlying technical or physical phenomena. The method is implemented in a software package called Icasso.     

A simplicity-based fuzzy clustering approach for detection and extraction of murmurs from the phonocardiogram

Almost all cardiac abnormalities manifest themselves as murmurs in a phonocardiogram (PCG). The location of a murmur in the PCG over a cardiac cycle depends on the underlying cardiac abnormality. Locating murmurs with respect to the cardiac cycle is useful for diagnosing cardiac dysfunction. Locating murmurs is difficult due to spectral similarity and time overlap with other heart sounds. Moreover, the wide variation in murmur amplitudes and murmur spectral characteristics across different patients and abnormalities has hindered the design of a generic time segmentation algorithm to isolate murmurs within the PCG. In this paper, we present a method to locate cardiac murmurs within the PCG that is based on their visual simplicity, which does not depend upon their absolute amplitude and frequency characteristics, and hence, results in their better localization. Then, we identify fuzzy sets to cluster simplicity values, due to murmurs, to determine the time duration over which the murmur occurs. The overall accuracy of the proposed algorithm in detecting systolic murmurs is 80%. The sensitivity of the algorithm in locating systolic murmurs is 73% and its specificity is 100%. The isolated murmur can then be further processed to extract clinically relevant features to diagnose the nature of the cardiac abnormality. Experimental results with a variety of murmurs are provided.     

Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity

Following the development of increasingly precise measurement instruments and fine-grain analysis tools for electroencephalographic (EEG) data, analysis of single-trial event-related EEG has considerably widened the utility of this non-invasive method to investigate brain activity. Recently, independent component analysis (ICA) has become one of the most prominent techniques for increasing the feasibility of single-trial EEG. This blind source separation technique extracts statistically independent components (ICs) from the EEG raw signal. By restricting the signal analysis to those ICs representing the processes of interest, single-trial analysis becomes more flexible. Still, the selection-criteria for in- or exclusion of certain ICs are largely subjective and unstandardized, as is the actual selection process itself. We present a rationale for a bottom-up, data-driven IC selection approach, using clear-cut inferential statistics on both temporal and spatial information to identify components that significantly contribute to a certain event-related brain potential (ERP). With time-range being the only necessary input, this approach considerably reduces the pre-assumptions for IC selection and promotes greater objectivity of the selection process itself. To test the validity of the approach presented here, we present results from a simulation and re-analyze data from a previously published ERP experiment on error processing. We compare the ERP-based IC selections made by our approach to the selection made based on mere signal power. The comparison of ERP integrity, signal-to-noise ratio, and single-trial properties of the back-projected ICs outlines the validity of the approach presented here. In addition, functional validity of the extracted error-related EEG signal is tested by investigating whether it is predictive for subsequent behavioural adjustments.     

A multivariate approach for classifying hospitals and computing blended payment rates

Prospective payment for inpatient hospital care is based on the ideal that hospitals that produce similar outputs, as measured by the types of cases the hospital treats, should be paid similar prices. However, similar output is a multidimensional concept. Thus operationalization of this ideal will ultimately require a more complex framework for determining hospital payment rates than currently employed at either the federal or state level. This article illustrates a multidimensional approach to achieve this objective. This technique, called Grade of Membership, is used to generate a unique type of hospital group and to characterize individual hospitals in terms of their degree of similarity to these groups. In addition, a new concept of grouping is described, a variable set based on hospitals' internal cost structure is developed and used, and ordinary least squares regression is employed to compute prices for these groups. With the use of simulation analysis, these groups are compared with more conventional groups.     

Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data

Independent component analysis (ICA) decomposes fMRI data into spatially independent maps and their corresponding time courses. However, distinguishing the neurobiologically and biophysically reasonable components from those representing noise and artifacts is not trivial. We present a simple method for the ranking of independent components, by assessing the resemblance between components estimated from all the data, and components estimated from only the odd- (or even-) numbered time points. We show that the meaningful independent components of fMRI data resemble independent components estimated from downsampled data, and thus tend to be highly ranked by the method.     

Analysis of multivariate failure-time data from HIV clinical trials

We illustrate the use of marginal methods for the analysis of multivariate failure-time data using a large trial in HIV infection in which the composite endpoint of AIDS or death incorporates more than 20 events with varying severity. Multivariate failure-time methods are required to investigate whether treatment delays development of new AIDS events. AIDS events can be grouped and treatment effects estimated using only the first event to occur in each group for each individual. Alternatively, all events can be included by fitting a separate baseline hazard for development of each event, and restricting treatment effects to be common within groups of events. In either case, model-based or minimum-variance estimates of the overall effect of treatment can be constructed. The covariance matrix for the treatment-effect estimates can be used in multiple testing procedures. Results from the Delta trial suggest that combination antiretroviral therapy with AZT plus either ddI or ddC may delay progression to more severe AIDS events compared to AZT monotherapy. These late events are generally untreatable and prophylaxis is not available. Trials are not generally powered to detect treatment effects on individual events making up a composite endpoint, and therefore all analyses are exploratory rather than providing definitive evidence. However, marginal multivariate models provide an easily available approach for modeling the effect of covariates on multiple disease processes, and allow the likely effects of treatment to be presented in a manner which is easily understood. They can be used in a variety of ways to explore different patterns of treatment effects and are also useful for testing multiple hypotheses regarding treatment effects on several different composite endpoints.     

A new toolbox for combining magnetoencephalographic source analysis and cytoarchitectonic probabilistic data for anatomical classification of dynamic brain activity

Size and location of activated cortical areas are often identified in relation to their surrounding macro-anatomical landmarks such as gyri and sulci. The sulcal pattern, however, is highly variable. In addition, many cortical areas are not linked to well defined landmarks, which in turn do not have a fixed relationship to functional and cytoarchitectonic boundaries. Therefore, it is difficult to unambiguously attribute localized neuronal activity to the corresponding cortical areas in the living human brain. Here we present new methods that are implemented in a toolbox for the objective anatomical identification of neuromagnetic activity with respect to cortical areas. The toolbox enables the platform independent integration of many types of source analysis obtained from magnetoencephalography (MEG) together with probabilistic cytoarchitectonic maps obtained in postmortem brains. The probability maps provide information about the relative frequency of a given cortical area being located at a given position in the brain. In the new software, the neuromagnetic data are analyzed with respect to cytoarchitectonic maps that have been transformed to the individual subject brain space. A number of measures define the degree of overlap between and distance from the activated areas and the corresponding cytoarchitectonic maps. The implemented algorithms enable the investigator to quantify how much of the reconstructed current density can be attributed to distinct cortical areas. Dynamic correspondence patterns between the millisecond-resolved MEG data and the static cytoarchitectonic maps are obtained. We show examples for auditory and visual activation patterns. However, size and location of the postmortem brain areas as well as the inverse method applied to the neuromagnetic data bias the anatomical classification. Therefore, the adaptation to the respective application and a combination of the objective quantities are discussed.     

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.     

A method for functional network connectivity among spatially independent resting-state components in schizophrenia

Functional connectivity of the brain has been studied by analyzing correlation differences in time courses among seed voxels or regions with other voxels of the brain in healthy individuals as well as in patients with brain disorders. The spatial extent of strongly temporally coherent brain regions co-activated during rest has also been examined using independent component analysis (ICA). However, the weaker temporal relationships among ICA component time courses, which we operationally define as a measure of functional network connectivity (FNC), have not yet been studied. In this study, we propose an approach for evaluating FNC and apply it to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls. We examined the connectivity and latency among ICA component time courses to test the hypothesis that patients with schizophrenia would show increased functional connectivity and increased lag among resting state networks compared to controls. Resting state fMRI data were collected and the inter-relationships among seven selected resting state networks (identified using group ICA) were evaluated by correlating each subject's ICA time courses with one another. Patients showed higher correlation than controls among most of the dominant resting state networks. Patients also had slightly more variability in functional connectivity than controls. We present a novel approach for quantifying functional connectivity among brain networks identified with spatial ICA. Significant differences between patient and control connectivity in different networks were revealed possibly reflecting deficiencies in cortical processing in patients.     

Electrodiagnosis of diabetic peripheral polyneuropathy. A multivariate analytic approach

Traditional univariate comparisons of nerve conduction data against standard norms may produce conflicting estimates of the presence or absence of a diabetic neuropathy, depending upon the data obtained and the specific nerves sampled. Alternatively, a multivariate analytic approach, using discriminant functions, provides a useful single measure of the degree of neuropathy determined as a weighted combination of the available data. The weights are derived from the linear discriminant function, which maximizes statistical separation of diabetic and nondiabetic subject groups. In this study, 12 electrophysiologic attributes are used to generate a single discriminant function that clearly separates diabetic from nondiabetic subjects and is interpretable as a neuropathic index. Each individual's index of diabetic neuropathy (I) can be quantified as follows: (Formula: see text) where Ai is the original electrophysiologic attribute (i = 12), ai is the coefficient for each attribute that defines the discriminant function and C is a constant specific for that function. For the first time, the degree of diabetic neuropathy can thus be quantified for purposes of comparison and correlation with other quantifiable clinical/somatic measures of diabetes. The index allows for a higher percentage of type II diabetic patients to be classified as neuropathic than previously described and enables determination of degree of neuropathy is affected individuals by an interpolative method.     

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 combined bootstrap/histogram analysis approach for computing a lateralization index from neuroimaging data

Cerebral hemispheric specialization has traditionally been described using a lateralization index (LI). Such an index, however, shows a very severe threshold dependency and is prone to be influenced by statistical outliers. Reliability of this index thus has been inherently weak, and the assessment of this reliability is as yet not possible as methods to detect such outliers are not available. Here, we propose a new approach to calculating a lateralization index on functional magnetic resonance imaging data by combining a bootstrap procedure with a histogram analysis approach. Synthetic and real functional magnetic resonance imaging data was used to assess performance of our approach. Using a bootstrap algorithm, 10,000 indices are iteratively calculated at different thresholds, yielding a robust mean, maximum and minimum LI and thus allowing to attach a confidence interval to a given index. Taking thresholds into account, an overall weighted bootstrapped lateralization index is calculated. Additional histogram analyses of these bootstrapped values allow to judge reliability and the influence of outliers within the data. We conclude that the proposed methods yield a robust and specific lateralization index, sensitively detect outliers and allow to assess the underlying data quality.     

A hybrid approach to automatic clustering of white matter fibers

Recently, the tract-based white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide biologically meaningful results and facilitate the tract-based comparison across subjects. However, due to the lack of quantitative definition of WM bundle boundaries, the complexity of brain architecture and the variability of WM shapes, clustering WM fibers into anatomically meaningful bundles is nontrivial. In this paper, we propose a hybrid top-down and bottom-up approach for automatic clustering and labeling of WM fibers, which utilizes both brain parcellation results and similarities between WM fibers. Our experimental results show reasonably good performance of this approach in clustering WM fibers into anatomically meaningful bundles.