多模态磁共振成像数据分析方法研究与应用
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摘要
磁共振成像最主要的一个优点就是它具有多种成像模态(或对比度),采用多模态磁共振成像已经成为多个研究领域特别是神经、精神疾病领域的一个重要研究手段。不过,如何有效地利用多模态磁共振成像研究神经、精神疾病的影像学标记尚缺乏系统性的研究,另外,虽然磁共振成像技术已经得到了广泛的应用,但是作为一种年轻的研究手段,其数据分析方法学上还存在很多局限性。因此,本文主要分两个部分来研究这些问题。
第一部分,我们系统地研究了如何有效地利用多模态磁共振成像进行神经、精神疾病的研究。首先,我们介绍了三种常用磁共振成像模态的成像基础及其敏感性,包括功能磁共振成像,弥散张量成像和高分辨T1加权结构像。然后,详细地研究了这三种成像模态的常用分析方法及其功用和缺陷,并且针对它们存在的缺陷提出了一些新的解决方案。特别地,我们研究了如何利用新颖的图论方法分析大脑静息态功能网络和大脑皮层解剖网络的拓扑属性；为了提高测量的准确性,我们提出了一种优化的基于形态学测量方法来研究弥散张量参数图(如FA图)之间的差异。另外,为了克服传统的基于个体空间的神经纤维束追踪方法的局限性,我们提出了一种基于张量配准的神经纤维束追踪方案。最后,我们重点是结合对脑中风疾病的具体研究来分析如何有效地应用各种成像模态及其分析方法。和以前的研究相比,我们观测到了很多重要的新发现,可能为揭示中风后手功能转归的神经生物学基础提供有价值的神经影像学标记。
第二部分,我们重点研究了两个磁共振成像数据分析方法学上的问题。(1)我们知道,功能连接分析中总会出现一些负相关,而且目前对负相关的生理意义还不清楚。在构建功能网络时,通常对负相关的处理策略是取其绝对值或者把它们设为零。那么负相关的不同处理策略对大尺度脑功能网络拓扑属性到底有怎样的影响?这个问题很少受到研究者的关注,因此,我们第一次比较了这两种常用的负相关处理条件下各个网络拓扑参数的差异,这不仅为负相关在脑功能网络拓扑配置中的角色提供了一个新的认识,而且也为负相关的研究提供一个新的角度。(2)利用传统单张量模型,我们经常观测到存在水肿脑区的FA值非常低,这很可能在一定程度上是归因于局部水肿的部分容积效应,而实际上水肿区域也可能存在一定的各向异性组织。为了克服传统单张量模型对存在水肿条件下进行扩散张量测量的局限性,我们提出了一种部分容积张量模型来拟合弥散加权信号,并且将该模型分别应用于缺血性脑中风和胶质瘤病人的弥散加权数据中。我们发现在病灶处使用该模型能够有效加强FA值,明显地高于采用传统的单张量模型得到的FA,从而有可能帮助病灶区域的神经追踪。另外,通过部分容积张量模型得到的f图能够很清晰地显示出病灶。我们的结果说明部分容积模型能够在一定程度上分离出病灶中水肿的成份,可能更适合于存在水肿脑区的弥散特性测量。
One of the most prominent advantages using magnetic resonance imaging (MRI) is capable of conducting multiple imaging modalities (or contrasts). It has become an important probing approach in many research fields, particularly in studying neurological and psychiatric disorders. However, how to effectively employ multimodal MRI for exploring neuroimaging markers in neurological and psychiatric disorders is a field under investigated. Although MRI has been widely used, it is yet a very young technology in its early development, and the related methodologies are very limited and under developed. In this study, we made efforts to investigate these critical issues along two axes, as detailed below.
In the first effort, we mainly focused on strategies of how to effectively utilize multimodal MRI for assisting investigations of neurological and psychiatric disorders. First, we introduced the fundamentals and the respective sensitivities of three MRI modalities, including functional MRI (fMRI), diffusion tensor imaging (DTI) and high-resolution Ti-weighted imaging. Second, we studied the commonly used analysis methods as well as their drawbacks for the three MRI modalities in detail. In addition, we also proposed strategies to resolve their drawbacks. Especially, we introduced an effective graph-theory analysis method for investigating the topological properties of both resting-state brain networks and anatomical networks of brain cortex; we proposed an optimal voxel-based morphometry approach to improving the accuracy in measuring the difference of diffusion derived indicies (e.g., FA-fractional anisotropy). Additionally, we also proposed a novel strategy for fiber tracking that is based on tensor-based registration for overcoming the drawbacks inherited in the traditional approach that has to be done within individual spaces. Finally, to demonstrate the effectiveness of our proposed strategy, we carried out a specific study using multimodal MRI data of stroke patients as an application instance. Comparing with previous studies, we observed many vital new findings, which may provide valuable neuroimaging markers for the pathophysiological fundamental of different outcomes in hand function after subcortical stroke.
In a second effort, we studied two methodological issues on MRI data analysis.(1) We know that negative correlations constantly exist in functional connectivity analysis. Unfortunately, the physiological underpinnings of negative correlations are not exactly clear. For reconstructing the brain network, negative correlations are usually treated using either the following two strategies:(a) adopting the absolute value of a negative correlation, or (b) setting all negative correlations to zero. However, little is known concerning the effect of taking the two different strategies for dealing with negative correlations on the topological properties of brain network. We therefore for the first time examined the differences in the topological properties of the resulting brain networks reconstructed using the two strategies. The work not only provides insight into the role of negative correlations in configuring the topology of brain functional network, but also offers a new view for studying the negative correlations.(2) Using traditional single tensor model (TSTM), we often observed extremely low FA at locations with edema in the brain, which might be partly attributed to the partial volume effect induced from edema. In fact, anisotropic tissue may also exsit at the loacations of edema. To resolve this issue, we proposed a partial-volume tensor model (PVTM) to approximate and model the diffusion weighted signals, and the model was applied in the diffusion-weighted datasets collected from stroke and glioma patients, respectively. We found that our PVTM can effectively enhance the FA measurement in the ischemic stroke and glioma tumor regions, significantly greater than that calculated using TSTM, which would therefore facilitate the success of fiber tracking within such regions. Moreover, the f map generated from PVTM can well display the lesion. Our results demonstrated that PVTM can isolate the component of edema from lesion to some extent and might be more suitable than TSTM for measuring diffusion properties in the regions of edema.
第一部分,我们系统地研究了如何有效地利用多模态磁共振成像进行神经、精神疾病的研究。首先,我们介绍了三种常用磁共振成像模态的成像基础及其敏感性,包括功能磁共振成像,弥散张量成像和高分辨T1加权结构像。然后,详细地研究了这三种成像模态的常用分析方法及其功用和缺陷,并且针对它们存在的缺陷提出了一些新的解决方案。特别地,我们研究了如何利用新颖的图论方法分析大脑静息态功能网络和大脑皮层解剖网络的拓扑属性；为了提高测量的准确性,我们提出了一种优化的基于形态学测量方法来研究弥散张量参数图(如FA图)之间的差异。另外,为了克服传统的基于个体空间的神经纤维束追踪方法的局限性,我们提出了一种基于张量配准的神经纤维束追踪方案。最后,我们重点是结合对脑中风疾病的具体研究来分析如何有效地应用各种成像模态及其分析方法。和以前的研究相比,我们观测到了很多重要的新发现,可能为揭示中风后手功能转归的神经生物学基础提供有价值的神经影像学标记。
第二部分,我们重点研究了两个磁共振成像数据分析方法学上的问题。(1)我们知道,功能连接分析中总会出现一些负相关,而且目前对负相关的生理意义还不清楚。在构建功能网络时,通常对负相关的处理策略是取其绝对值或者把它们设为零。那么负相关的不同处理策略对大尺度脑功能网络拓扑属性到底有怎样的影响?这个问题很少受到研究者的关注,因此,我们第一次比较了这两种常用的负相关处理条件下各个网络拓扑参数的差异,这不仅为负相关在脑功能网络拓扑配置中的角色提供了一个新的认识,而且也为负相关的研究提供一个新的角度。(2)利用传统单张量模型,我们经常观测到存在水肿脑区的FA值非常低,这很可能在一定程度上是归因于局部水肿的部分容积效应,而实际上水肿区域也可能存在一定的各向异性组织。为了克服传统单张量模型对存在水肿条件下进行扩散张量测量的局限性,我们提出了一种部分容积张量模型来拟合弥散加权信号,并且将该模型分别应用于缺血性脑中风和胶质瘤病人的弥散加权数据中。我们发现在病灶处使用该模型能够有效加强FA值,明显地高于采用传统的单张量模型得到的FA,从而有可能帮助病灶区域的神经追踪。另外,通过部分容积张量模型得到的f图能够很清晰地显示出病灶。我们的结果说明部分容积模型能够在一定程度上分离出病灶中水肿的成份,可能更适合于存在水肿脑区的弥散特性测量。
One of the most prominent advantages using magnetic resonance imaging (MRI) is capable of conducting multiple imaging modalities (or contrasts). It has become an important probing approach in many research fields, particularly in studying neurological and psychiatric disorders. However, how to effectively employ multimodal MRI for exploring neuroimaging markers in neurological and psychiatric disorders is a field under investigated. Although MRI has been widely used, it is yet a very young technology in its early development, and the related methodologies are very limited and under developed. In this study, we made efforts to investigate these critical issues along two axes, as detailed below.
In the first effort, we mainly focused on strategies of how to effectively utilize multimodal MRI for assisting investigations of neurological and psychiatric disorders. First, we introduced the fundamentals and the respective sensitivities of three MRI modalities, including functional MRI (fMRI), diffusion tensor imaging (DTI) and high-resolution Ti-weighted imaging. Second, we studied the commonly used analysis methods as well as their drawbacks for the three MRI modalities in detail. In addition, we also proposed strategies to resolve their drawbacks. Especially, we introduced an effective graph-theory analysis method for investigating the topological properties of both resting-state brain networks and anatomical networks of brain cortex; we proposed an optimal voxel-based morphometry approach to improving the accuracy in measuring the difference of diffusion derived indicies (e.g., FA-fractional anisotropy). Additionally, we also proposed a novel strategy for fiber tracking that is based on tensor-based registration for overcoming the drawbacks inherited in the traditional approach that has to be done within individual spaces. Finally, to demonstrate the effectiveness of our proposed strategy, we carried out a specific study using multimodal MRI data of stroke patients as an application instance. Comparing with previous studies, we observed many vital new findings, which may provide valuable neuroimaging markers for the pathophysiological fundamental of different outcomes in hand function after subcortical stroke.
In a second effort, we studied two methodological issues on MRI data analysis.(1) We know that negative correlations constantly exist in functional connectivity analysis. Unfortunately, the physiological underpinnings of negative correlations are not exactly clear. For reconstructing the brain network, negative correlations are usually treated using either the following two strategies:(a) adopting the absolute value of a negative correlation, or (b) setting all negative correlations to zero. However, little is known concerning the effect of taking the two different strategies for dealing with negative correlations on the topological properties of brain network. We therefore for the first time examined the differences in the topological properties of the resulting brain networks reconstructed using the two strategies. The work not only provides insight into the role of negative correlations in configuring the topology of brain functional network, but also offers a new view for studying the negative correlations.(2) Using traditional single tensor model (TSTM), we often observed extremely low FA at locations with edema in the brain, which might be partly attributed to the partial volume effect induced from edema. In fact, anisotropic tissue may also exsit at the loacations of edema. To resolve this issue, we proposed a partial-volume tensor model (PVTM) to approximate and model the diffusion weighted signals, and the model was applied in the diffusion-weighted datasets collected from stroke and glioma patients, respectively. We found that our PVTM can effectively enhance the FA measurement in the ischemic stroke and glioma tumor regions, significantly greater than that calculated using TSTM, which would therefore facilitate the success of fiber tracking within such regions. Moreover, the f map generated from PVTM can well display the lesion. Our results demonstrated that PVTM can isolate the component of edema from lesion to some extent and might be more suitable than TSTM for measuring diffusion properties in the regions of edema.
引文
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