Neuroimaging for the Diagnosis and Study of Psychiatric Disorders [Life Sciences]

This paper summarizes the neuroimaging methods in the diagnostic assessment process that have been applied to neuropsychiatric studies. Here, we outline the signal processing challenges in structural and functional neuroimaging methods and discuss the specific difficulties in multisite data analysis for the diagnosis of neuropsychiatric disorders. Currently, fMRI and other brain imaging methods are used to aid in diagnosis primarily by revealing various types of pathology; e.g., evidence of brain tumors or multiple strokes. Significant research efforts have been directed toward understanding, analyzing, and diagnosing neuropsychiatric disorders.     

Unified Framework for Robust Estimation of Brain Networks From fMRI Using Temporal and Spatial Correlation Analyses

There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral $F$ hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data.      

Joint Detection-Estimation of Brain Activity in Functional MRI: A Multichannel Deconvolution Solution

Analysis of functional magnetic resonance imaging (fMRI) data focuses essentially on two questions: first, a detection problem that studies which parts of the brain are activated by a given stimulus and, second, an estimation problem that investigates the temporal dynamic of the brain response during activations. Up to now, these questions have been addressed independently. However, the activated areas need to be known prior to the analysis of the temporal dynamic of the response. Similarly, a typical shape of the response has to be assumed a priori for detection purpose. This situation motivates the need for new methods in neuroimaging data analysis that are able to go beyond this unsatisfactory tradeoff. The present paper raises a novel detection-estimation approach to perform these two tasks simultaneously in region-based analysis. In the Bayesian framework, the detection of brain activity is achieved using a mixture of two Gaussian distributions as a prior model on the “neural” response levels, whereas the hemodynamic impulse response is constrained to be smooth enough in the time domain with a Gaussian prior. All parameters of interest, as well as hyperparameters, are estimated from the posterior distribution using Gibbs sampling and posterior mean estimates. Results obtained both on simulated and real fMRI data demonstrate first that our approach can segregate activated and nonactivated voxels in a given region of interest (ROI) and, second, that it can provide spatial activation maps without any assumption on the exact shape of the Hemodynamic Response Function (HRF), in contrast to standard model-based analysis.     

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.     

非线性脑区相关性分析及动态脑网络构建方法

针对静息态脑功能磁共振的血氧依赖水平信号中的非线性特征,在基于滑动窗口的动态数据分析技术的基础上,本文重点对构建全脑动态特征矩阵过程中的不同非线性相关分析方法展开了对比研究,并给出了构建脑网络中的阈值确定方法。脑网络降维和状态聚类实验结果显示,在阈值参数选择合理的前提下,采用三种非线性分析方法对BOLD信号进行的相关分析均可得到结构规模相似的脑网络,并且其状态转换结果均显现出相似的规律性,从而为下一步展开脑网络的动态特性分析和演化过程研究奠定了基础。      

Effect of spatial alignment transformations in PCA and ICA of functional neuroimages

It has been previously observed that independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper, we seek to determine analytically the conditions under which this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case, we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (fMRI) data. Our analysis is applicable to both intersubject and intrasubject spatial normalization.     

A New General Linear Convolution Model for fMRI Data Process

General linear model (GLM) is the most popular method for functional magnetic resource imaging (fMRI) data analysis. However, its theory is imperfect. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises better. For the purpose of detecting brain function activation, according to the principle of GLM, a new convolution model is presented by a new dynamic function convolving with design-matrix, which combining with t-test can be used to detect brain active signal. The fMRI imaging result of visual stimulus experiment indicates that brain activities mainly concentrate among vland v2 areas of visual cortex, and also verified the validity of this technique.     

Volume rendering of multimodal images: application to MRI and PET imaging of the human brain

A procedure for combining and visualizing complementary structural and functional information from magnetic resonance imaging (MRI) and positron emission tomography (PET) is described. MR and PET images of the human brain were obtained and correlated to form three-dimensional volumes of image data. Volume rendering and solid-texturing concepts were combined to develop a new volume imaging technique for ;volume texture-mapping' brain glucose metabolism (from PET) onto brain anatomy (from MRI). The technique was used to produce sequences of three-dimensional views: these sequences were dynamically displayed in a ;cine-loop' to better visualize the three-dimensional relationship between brain structure and function. The techniques provide a means of presenting vast amounts of multidimensional data in a form that is easily understood, and the resulting images are essential to an understanding of the normal and pathologic states of the human brain.     

An efficient method for dynamic magnetic resonance imaging

Many magnetic resonance imaging applications require the acquisition of a time series of images. In conventional Fourier transform based imaging methods, each of these images is acquired independently so that the temporal resolution possible is limited by the number of spatial encodings (or data points in the Fourier space) collected, or one has to sacrifice spatial resolution for temporal resolution. Here, a generalized series based imaging technique is proposed to address this problem. This technique makes use of the fact that, in most time-sequential imaging problems, the high-resolution image morphology does not change from one image to another, and it improves imaging efficiency (and temporal resolution) over the conventional Fourier imaging methods by eliminating the repeated encodings of this stationary information. Additional advantages of the proposed imaging technique include a reduced number of radio frequency (RF) pulses for data collection, and thus lower RF power deposition. This method should prove useful for a variety of dynamic imaging applications, including dynamic studies of contrast agents and functional brain imaging.     

Estimation of the hemodynamic response of fMRI Data using RBF neural network

Functional magnetic resonance imaging (fMRI) is an important technique for neuroimaging. The conventional system identification methods used in fMRI data analysis assume a linear time-invariant system with the impulse response described by the hemodynamic responses (HDR). However, the measured blood oxygenation level-dependent (BOLD) signals to a particular processing task (for example, rapid event-related fMRI design) show nonlinear properties and vary with different brain regions and subjects. In this paper, radial basis function (RBF) neural network (a powerful technique for modelling nonlinearities) is proposed to model the dynamics underlying the fMRI data. The equivalence of the proposed method to the existing Volterra series method has been demonstrated. It is shown that the first- and second-order Volterra kernels could be deduced from the parameters of the RBF neural network. Studies on both simulated (using Balloon model) as well as real event-related fMRI data show that the proposed method can accurately estimate the HDR of the brain and capture the variations of the HDRs as a function of the brain regions and subjects.     

Analysis of FMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach

Clustering analysis is a promising data-driven method for analyzing functional magnetic resonance imaging (fMRI) time series data. The huge computational load, however, creates practical difficulties for this technique. We present a novel approach, integrating principal component analysis (PCA) and supervised affinity propagation clustering (SAPC). In this method, fMRI data are initially processed by PCA to obtain a preliminary image of brain activation. SAPC is then used to detect different brain functional activation patterns. We used a supervised Silhouette index to optimize clustering quality and automatically search for the optimal parameter p in SAPC, so that the basic affinity propagation clustering is improved by applying SAPC. Four simulation studies and tests with three in vivo fMRI datasets containing data from both block-design and event-related experiments revealed that functional brain activation was effectively detected and different response patterns were distinguished using our integrated method. In addition, the improved SAPC method was superior to the k -centers clustering and hierarchical clustering methods in both block-design and event-related fMRI data, as measured by the average squared error. These results suggest that our proposed novel integrated approach will be useful for detecting brain functional activation in both block-design and event-related experimental fMRI data.     

Wavelet analysis for brain-function imaging

The authors present a new algorithmic procedure for the analysis of brain images. This procedure is specifically designed to image the activity and functional organization of the brain. The authors' results are tested on data collected and previously analyzed with the technique known as in vivo optical imaging of intrinsic signals. The authors' procedure enhances the applicability of this technique and facilitates the extension of the underlying ideas to other imaging problems (e.g., functional MRI). The authors' thrust is two fold. First, they give a systematic method to control the blood vessel artifacts which typically reduce the dynamic range of the image. They propose a mathematical model for the vibrations in time of the veins and arteries and they design a new method for cleaning the images of the vessels with the highest time variations. This procedure is based on the analysis of the singularities of the images. The use of wavelet transform is of crucial importance in characterizing the singularities and reconstructing appropriate versions of the original images. The second important component of the authors' work is the analysis of the time evolution of the fine structure of the images. They show that, once the images have been cleaned of the blood vessel vibrations/variations, the principal component of the time evolutions of the signals is due to the functional activity following the stimuli. The part of the brain where this function takes place can be localized and delineated with precision.     

3-D SPECT simulations of a complex 3-D mathematical brain model: effects of 3-D geometric detector response, attenuation, scatter, and statistical noise

The quantitative imaging characteristics of ultrahigh-resolution parallel-hole SPECT, including 3-D geometric detector response, attenuation, scatter, and statistical noise, were investigated by simulations based on a complex digitized 3-D brain model of the gray and white matter distributions. The projection data resulting from a uniform distribution of gray and white matter radioactivity, in a ratio of 5:1, were simulated. The results demonstrate significant qualitative and quantitative artifacts in reconstructed human brain images. In the absence of attenuation, scatter, and noise, artifactual variation caused inaccuracies in regional radioactivity quantification. Inclusion of attenuation scatter, and noise in the simulation caused additional artifacts, and resulted in reconstructed images which qualitatively and quantitatively corresponded very closely to reconstructed images of the actual 3-D brain phantom which was constructed from the same set of data as the mathematical 3-D brain model. It is concluded that the major degrading factor in SPECT neuroimaging is the 3-D geometric detector response function.     

Characterization of dynamic 3-D PET imaging for functional brainmapping

Methods for optimizing the acquisition, reconstruction and analysis of positron emission tomography (PET) images for functional brain mapping have been investigated. The scatter fraction and noise-equivalent count rate characteristics were measured for the ECAT 951/31R PET scanner operating in septa-extended two-dimensional (2-D) and septa-retracted three-dimensional (3-D) modes. The 3-D mode is shown to provide higher signal-to-noise images than the 2-D mode at specific activities less than 30 kBq/ml. To enable increased temporal resolution in dynamic 3-D PET activation studies, a parallel version of the 3-D reconstruction algorithm was developed. Implementation of the reprojection algorithm on an 88 processor i860 supercomputer resulted in a more than tenfold increase in reconstruction speed compared to a single i860 processor system. An investigation of the optimal duration for imaging brain activations was undertaken in 12 normal subjects using repeated H₂¹⁵O slow infusions and a visually presented lexical decision task. The significance of change in regional cerebral blood flow (CBF) was determined using statistical parametric maps for images acquired during stimulation, immediately after stimulation, and commencing 1 min after cessation of the stimulus. Regions of CBF change were detected in all three images. Dynamic 3-D, or four-dimensional (4-D), PET activation scanning is shown to be practical and likely to further improve the sensitivity of PET for detection of subtle regional CBF changes in functional brain mapping research     

生物组织的光声成像技术及其在生物医学中的应用

简要介绍了光声成像技术的基本原理,采集系统和成像算法.重点阐述了光声成像技术在肿瘤的早期检测和疗效监测,脑成像和脑功能监测以及临床血管监测等生物医学领域的应用.对光声成像技术应用前景进行了展望.     

Imaging brain dynamics using independent component analysis

The analysis of electroencephalographic and magnetoencephalographic recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain     

A surface-based technique for warping three-dimensional images of the brain

The authors have devised, implemented, and tested a fast, spatially accurate technique for calculating the high-dimensional deformation field relating the brain anatomies of an arbitrary pair of subjects. The resulting three-dimensional (3-D) deformation map can be used to quantify anatomic differences between subjects or within the same subject over time and to transfer functional information between subjects or integrate that information on a single anatomic template. The new procedure is based on developmental processes responsible for variations in normal human anatomy and is applicable to 3-D brain images in general, regardless of modality. Hybrid surface models known as Chen surfaces (based on superquadrics and spherical harmonics) are used to efficiently initialize 3-D active surfaces, and these then extract from both scans the developmentally fundamental surfaces of the ventricles and cortex. The construction of extremely complex surface deformation maps on the internal cortex is made easier by building a generic surface structure to model it. Connected systems of parametric meshes model several deep sulci whose trajectories represent critical functional boundaries. These sulci are sufficiently extended inside the brain to reflect subtle and distributed variations in neuroanatomy between subjects. The algorithm then calculates the high-dimensional volumetric warp (typically with 3842x256x3 approximately 0.1 billion degrees of freedom) deforming one 3-D scan into structural correspondence with the other. Integral distortion functions are used to extend the deformation field required to elastically transform nested surfaces to their counterparts in the target scan. The algorithm's accuracy is tested, by warping 3-D magnetic resonance imaging (MRI) volumes from normal subjects and Alzheimer's patients, and by warping full-color 1024(3 ) digital cryosection volumes of the human head onto MRI volumes. Applications are discussed, including the transfer of multisubject 3-D functional, vascular, and histologic maps onto a single anatomic template; the mapping of 3-D brain atlases onto the scans of new subjects; and the rapid detection, quantification, and mapping of local shape changes in 3-D medical images in disease and during normal or abnormal growth and development.     

Improving PET-based physiological quantification through methods of wavelet denoising

The goal of this study was to evaluate methods of multidimensional wavelet denoising on restoring the fidelity of biological signals hidden within dynamic positron emission tomography (PET) images. A reduction of noise within pixels, between adjacent regions, and time-serial frames was achieved via redundant multiscale representations. In analyzing dynamic PET data of healthy volunteers, a multiscale method improved the estimate-to-error ratio of flows fivefold without loss of detail. This technique also maintained accuracy of flow estimates in comparison with the "gold standard," using dynamic PET with O15-water. In addition, in studies of coronary disease patients, flow patterns were preserved and infarcted regions were well differentiated from normal regions. The results show that a wavelet-based noise-suppression method produced reliable approximations of salient underlying signals and led to an accurate quantification of myocardial perfusion. The described protocol can be generalized to other temporal biomedical imaging modalities including functional magnetic resonance imaging and ultrasound.     

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。     

Electromagnetic brain mapping

There has been tremendous advances in our ability to produce images of human brain function. Applications of functional brain imaging extend from improving our understanding of the basic mechanisms of cognitive processes to better characterization of pathologies that impair normal function. Magnetoencephalography (MEG) and electroencephalography (EEG) (MEG/EEG) localize neural electrical activity using noninvasive measurements of external electromagnetic signals. Among the available functional imaging techniques, MEG and EEG uniquely have temporal resolutions below 100 ms. This temporal precision allows us to explore the timing of basic neural processes at the level of cell assemblies. MEG/EEG source localization draws on a wide range of signal processing techniques including digital filtering, three-dimensional image analysis, array signal processing, image modeling and reconstruction, and, blind source separation and phase synchrony estimation. We describe the underlying models currently used in MEG/EEG source estimation and describe the various signal processing steps required to compute these sources. In particular we describe methods for computing the forward fields for known source distributions and parametric and imaging-based approaches to the inverse problem