Convolution Power Spectrum Analysis for fMRI Data Based on Prior Image Signal

Functional MRI (fMRI) data-processing methods based on changes in the time domain involve, among other things, correlation analysis and use of the general linear model with statistical parametric mapping (SPM). Unlike conventional fMRI data analysis methods, which aim to model the blood-oxygen-level-dependent (BOLD) response of voxels as a function of time, the theory of power spectrum (PS) analysis focuses completely on understanding the dynamic energy change of interacting systems. We propose a new convolution PS (CPS) analysis of fMRI data, based on the theory of matched filtering, to detect brain functional activation for fMRI data. First, convolution signals are computed between the measured fMRI signals and the image signal of prior experimental pattern to suppress noise in the fMRI data. Then, the PS density analysis of the convolution signal is specified as the quantitative analysis energy index of BOLD signal change. The data from simulation studies and in vivo fMRI studies, including block-design experiments, reveal that the CPS method enables a more effective detection of some aspects of brain functional activation, as compared with the canonical PS SPM and the support vector machine methods. Our results demonstrate that the CPS method is useful as a complementary analysis in revealing brain functional information regarding the complex nature of fMRI time series.      

Clustered components analysis for functional MRI

A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels. Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method. This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques. A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.     

A Bayesian hierarchical correlation model for fMRI cluster analysis

Data-driven cluster analysis is potentially suitable to search for, and discriminate between, distinct response signals in blood oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI), which appear during cerebrovascular disease. In contrast to model-driven methods, which test for a particular BOLD signal whose shape must be given beforehand, data-driven methods generate a set of BOLD signals directly from the fMRI data by clustering voxels into groups with correlated time signals. Here, we address the problem of selecting only the clusters that represent genuine responses to the experimental stimulus by modeling the correlation structure of the clustered data using a Bayesian hierarchical model. The model is empirically justified by demonstrating the hierarchical organization of the voxel correlations after cluster analysis. BOLD signal discrimination is demonstrated using: 1) simulations that contain multiple pathological BOLD response signals; and 2) fMRI data acquired during an event-related motor task. These demonstrations are compared with results from a model-driven method based on the general linear model. Our simulations show that the data-driven method can discriminate between the BOLD response signals, while the model-driven method only finds one signal. For fMRI, the data-driven method distinguishes between the BOLD signals appearing in the sensorimotor cortex and those in basal ganglia and putamen, while the model-driven method combines these signals into one activation map. We conclude that the proposed data-driven method provides an objective framework to identify and discriminate between distinct BOLD response signals.     

Bayesian approach to segmentation of statistical parametric maps

A contextual segmentation technique to detect brain activation from functional brain images is presented in the Bayesian framework. Unlike earlier similar approaches [Holmes and Ford (1993) and Descombes et al. (1998)], a Markov random field (MRF) is used to represent configurations of activated brain voxels, and likelihoods given by statistical parametric maps (SPM's) are directly used to find the maximum a posteriori (MAP) estimation of segmentation. The iterative segmentation algorithm, which is based on a simulated annealing scheme, is fully data-driven and capable of analyzing experiments involving multiple-input stimuli. Simulation results and comparisons with the simple thresholding and the statistical parametric mapping (SPM) approaches are presented with synthetic images, and functional MR images acquired in memory retrieval and event-related working memory tasks. The experiments show that an MRF is a valid representation of the activation patterns obtained in functional brain images, and the present technique renders a superior segmentation scheme to the context-free approach and the SPM approach.     

基于感兴趣脑区LASSO-Granger因果关系的脑电特征提取算法

该文将脑功能网络引入到脑电特征提取的研究中,提出一种基于感兴趣脑区LASSO-Granger因果关系的新方法,克服了当前基于孤立脑区的研究方法的不足。先利用主成分分析提取各感兴趣区的最大主成分,然后计算它们之间的LASSO-Granger因果度量,并将其作为特征向量,最后输入支持向量机分类器,对BCI Competition IV dataset 1中的4组数据进行分类识别。结果表明,基于感兴趣脑区间LASSO-Granger因果关系分析和支持向量机分类器的方法对不同的运动想象任务识别率较高,提供了新的研究思路。     

基于小波变换和空间相关性fMRI数据

提出了一种组合小波域统计分析和空间相关性检验的方法来检测fMRI功能激活区域.该方法首先利用Ruttimann等提出的小波方法检测到激活体素,然后逐体素分析它们与其三维空间26-邻域体素血流动力学响应的相关性,并进行空间相关性检验来得到最终激活区域.实验结果表明:该方法是一种快速可靠的fMRI功能激活区域检测方法.     

基于时间-空间相关特性分析功能MRI数据的方法

如何从低信噪比的序列图像中准确、可靠地检测功能激发信号成为功能磁共振(fMRI)数据分析的关键问题。当受检者接受同样的刺激或执行同样的任务时，激发体素具有相似的血流动力学响应时间过程，且激发体素常以空间聚类的形式出现。本文给出一种联合体素的时间自相关特性及空间相关特性分析fMRI数据的方法。该方法计算每个体素时间过程的最大时间自相关系数以及与其邻域体素时间过程的最大空间相关系数，利用主成分分析法得到一个时间—空间联合相关测度，并通过检验该测度的统计显著性检测功能激发信号。仿真实验及实际的。fMRI数据分析结果表明了提出的方法具有较高的准确性及可靠性。     

Analyzing functional brain connectivity by means of commute times: a new approach and its application to track event-related dynamics

There is growing interest in studying the association of functional connectivity patterns with particular cognitive tasks. The ability of graphs to encapsulate relational data has been exploited in many related studies, where functional networks (sketched by different neural synchrony estimators) are characterized by a rich repertoire of graph-related metrics. We introduce commute times (CTs) as an alternative way to capture the true interplay between the nodes of a functional connectivity graph (FCG). CT is a measure of the time taken for a random walk to setout and return between a pair of nodes on a graph. Its computation is considered here as a robust and accurate integration, over the FCG, of the individual pairwise measurements of functional coupling. To demonstrate the benefits from our approach, we attempted the characterization of time evolving connectivity patterns derived from EEG signals recorded while the subject was engaged in an eye-movement task. With respect to standard ways, which are currently employed to characterize connectivity, an improved detection of event-related dynamical changes is noticeable. CTs appear to be a promising technique for deriving temporal fingerprints of the brain's dynamic functional organization.     

Estimation of the cortical connectivity by high-resolution EEG and structural equation modeling: simulations and application to finger tapping data

Today, the concept of brain connectivity plays a central role in the neuroscience. While functional connectivity is defined as the temporal coherence between the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally between cortical sites. The most used method to estimate effective connectivity in neuroscience is the structural equation modeling (SEM), typically used on data related to the brain hemodynamic behavior. However, the use of hemodynamic measures limits the temporal resolution on which the brain process can be followed. The present research proposes the use of the SEM approach on the cortical waveforms estimated from the high-resolution EEG data, which exhibits a good spatial resolution and a higher temporal resolution than hemodynamic measures. We performed a simulation study, in which different main factors were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). Such factors were the signal-to-noise ratio and the duration of the simulated cortical activity. Since SEM technique is based on the use of a model formulated on the basis of anatomical and physiological constraints, different experimental conditions were analyzed, in order to evaluate the effect of errors made in the a priori model formulation on its performances. The feasibility of the proposed approach has been shown in a human study using high-resolution EEG recordings related to finger tapping movements.     

Probabilistic framework for brain connectivity from functional MR images

This paper unifies our earlier work on detection of brain activation (Rajapakse and Piyaratna, 2001) and connectivity (Rajapakse and Zhou, 2007) in a probabilistic framework for analyzing effective connectivity among activated brain regions from functional magnetic resonance imaging (fMRI) data. Interactions among brain regions are expressed by a dynamic Bayesian network (DBN) while contextual dependencies within functional images are formulated by a Markov random field. The approach simultaneously considers both the detection of brain activation and the estimation of effective connectivity and does not require a priori model of connectivity. Experimental results show that the present approach outperforms earlier fMRI analysis techniques on synthetic functional images and robustly derives brain connectivity from real fMRI data.     

Weighted regularization in electrical impedance tomography with applications to acute cerebral stroke

We apply electrical impedance tomography to detect and localize brain impedance changes associated with stroke. Forward solutions are computed using the finite-element method in two dimensions. We assume that baseline conductivity values are known for the major head tissues, and focus on changes in the brain compartment only. We use singular-value decomposition (SVD) to show that different impedance measurement patterns, which are theoretically equivalent by the reciprocity theorem, have different sensitivities to the brain compartment in the presence of measurement noise. The inverse problem is solved in part by standard means, using iterated SVD, and regularizing by truncation. To improve regularization we introduce a weighting scheme which normalizes the sensitivity matrix for voxels at different depths. This increases the number of linearly independent components which contribute to the solution, and forces the different measurement patterns to have similar sensitivity. When applied to stroke, this weighted regularization improves image quality overall.     

Nonparametric extraction of transient changes in neurotransmitter concentration from dynamic PET data

We have developed a nonparametric approach to the analysis of dynamic positron emission tomography (PET) data for extracting temporal characteristics of the change in endogenous neurotransmitter concentration in the brain. An algebraic method based on singular value decomposition (SVD) was applied to simulated data under both rest (neurotransmitter at baseline) and activated (transient neurotransmitter release) conditions. The resulting signals are related to the integral of the change in free neurotransmitter concentration in the tissue. Therefore, a specially designed minimum mean-square error (MMSE) filter must be applied to the signals to recover the desired temporal pattern of neurotransmitter change. To test the method, we simulated sets of realistic time activity curves representing uptake of [11C]raclopride, a dopamine (DA) receptor antagonist, in brain regions, under baseline and dopamine-release conditions. Our tests considered two scenarios: 1) a spatially homogeneous pattern with all voxels in the activated state presenting an identical DA signal; 2) a spatially heterogeneous pattern in which different DA signals were contained in different families of voxels. In the first case, we demonstrated that the timing of a single DA peak can be accurately identified to within 1 min and that two distinct neurotransmitter peaks can be distinguished. In the second case, separate peaks of activation separated by as little as 5 min can be distinguished. A decrease in blood flow during activation could not account for our findings. We applied the method to human PET data acquired with [11C]raclopride in the presence of transiently elevated DA due to intravenous (IV) alcohol. Our results for an area of the nucleus accumbens-a region relevant to alcohol consumption-agreed with a model-based method for estimating the DA response. SVD-based analysis of dynamic PET data promises a completely noninvasive and model-independent technique for determining the dynamics of a neurotransmitter response to cognitive or pharmacological stimuli. Our results indicate that the method is robust enough for application to voxel-by-voxel data.     

Modeling brain activation in fMRI using group MRF

Noise confounds present serious complications to functional magnetic resonance imaging (fMRI) analysis. The amount of discernible signals within a single dataset of a subject is often inadequate to obtain satisfactory intra-subject activation detection. To remedy this limitation, we propose a novel group Markov random field (GMRF) that extends each subject's neighborhood system to other subjects to enable information coalescing. A distinct advantage of GMRF over standard fMRI group analysis is that no stringent one-to-one voxel correspondence is required. Instead, intra- and inter-subject neighboring voxels are jointly regularized to encourage spatially proximal voxels to be assigned similar labels across subjects. Our proposed group-extended graph structure thus provides an effective means for handling inter-subject variability. Also, adopting a group-wise approach by integrating group information into intra-subject activation, as opposed to estimating a single average group map, permits inter-subject differences to be characterized and studied. GMRF can be elegantly implemented as a single MRF, thus enabling all subjects' activation maps to be simultaneously and collaboratively segmented with global optimality guaranteed in the case of binary labeling. We validate our technique on synthetic and real fMRI data and demonstrate GMRF's superior performance over standard fMRI analysis.     

Permutation testing made practical for functional magnetic resonance image analysis

We describe an efficient algorithm for the step-down permutation test, applied to the analysis of functional magnetic resonance images. The algorithm's time bound is nearly linear, making it feasible as an interactive tool. Results of the permutation test algorithm applied to data from a cognitive activation paradigm are compared with those of a standard parametric test corrected for multiple comparisons. The permutation test identifies more weakly activated voxels than the parametric test, always activates a superset of the voxels activated by this parametric method, almost always yields significance levels greater than or equal to those produced by the parametric method, and tends to enlarge activated clusters rather than adding isolated voxels. Our implementation of the permutation test is freely available as part of a widely distributed software package for analysis of functional brain images.     

基于变换数据空间的相干脑电源定位算法

不同脑区之间的相互协作对大脑完成认知任务具有重要意义.脑区电活动的相干性被认为是这种协作的表现形式.从头表脑电无创地三维定位相干源有助于了解大脑的内在机制.传统的MUSIC算法不能定位相干源.本文发展了一种在变换数据空间的MUSIC算法用于相干源定位.首先根据先验信息大致估计相干源区的范围,然后设计能压制相干源区的数据变换矩阵.最后在变换后的数据空间定位相干源.不同条件下的计算模拟实验表明,相比其它方法,这种方法具有更高的定位精度,运算速度也更快.     

Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.     

Neural networks approach to clustering of activity in fMRI data

Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity.     

Estimation of time-varying connectivity patterns through the use of an adaptive directed transfer function

Frequency-derived identification of the propagation of information between brain regions has quickly become a popular area in the neurosciences. Of the various techniques used to study the propagation of activation within the central nervous system, the directed transfer function (DTF) has been well used to explore the functional connectivity during a variety of brain states and pathological conditions. However, the DTF method assumes the stationarity of the neural electrical signals and the time invariance of the connectivity among different channels over the investigated time window. Such assumptions may not be valid in the abnormal brain signals such as seizures and interictal spikes in epilepsy patients. In the present study, we have developed an adaptive DTF (ADTF) method through the use of a multivariate adaptive autoregressive model to study the time-variant propagation of seizures and interictal spikes in simulated electrocorticogram (ECoG) networks. The time-variant connectivity reconstruction is achieved by the Kalman filter algorithm, which can incorporate time-varying state equations. We study the performance of the proposed method through simulations with various propagation models using either sample seizures or interictal spikes as the source waveform. The present results suggest that the new ADTF method correctly captures the temporal dynamics of the propagation models, while the DTF method cannot, and even returns erroneous results in some cases. The present ADTF method was tested in real epileptiform ECoG data from an epilepsy patient, and the ADTF results are consistent with the clinical assessments performed by neurologists.      

融合先验信息的脑功能图像分割

基于静息态功能磁共振图像(fMRI)的脑功能区分割广泛采用K均值聚类和谱聚类等无监督聚类算法。但这些算法对图像噪声较为敏感,可能会产生不可靠的脑区分割结果。文中提出了一种融合先验信息的半监督聚类算法,可以可靠地确定各子区间的边界,从而得到稳定的分割结果。提出的方法对人类右侧大脑的Broca区(BA44/45区)进行分割验证,实验结果表明,文中的方法不仅得到了可靠的功能子区边界,而且获得了较高的个体间一致性。     

Computed basis functions for finite element analysis based on tomographic data

In bioelectromagnetics, the structures in which the electromagnetic field is to be computed are sometimes defined by a fine grid of voxels (3-D cells) whose tissue types are obtained by tomography. A novel finite element method is proposed for such cases. A simple, regular mesh of cube elements is constructed, each containing the same, integer number of voxels. There may be several different tissues present within an element, but this is accommodated by computing element basis functions that approximately respect the interface conditions between different tissues. Results are presented for a test model of 128 (3) voxels, consisting of nested dielectric cubes, driven by specified charges. The electrostatic potential computed with the new method agrees well with that of a conventional finite element code: the rms difference along the sample line is 1.5% of the highest voltage. Results are also presented for the potential due to a current dipole placed in a brain model of 181 × 217 × 181 voxels, derived from MRI data. The new method gives potentials that are different to those obtained by treating each voxel as an element by 1% of the peak voltage, yet the global finite element matrix has a dimension which is more than 50 times smaller.