基于磁共振成像的脑连接方法学及应用研究
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摘要
人类大脑由约1011个神经元组成,包含1015个的神经突触连接,是目前所知宇宙间最为复杂的体系之一。虽然如此,却可以从两个基本法则去理解大脑的运行规律:功能分离(functional segregation)及功能整合(functional integration)。前者指大脑每一个神经集合(脑区)对应负责一个具体的行为功能,是功能定位的假说基础;而后者则指每一个具体的行为是由多个神经集合共同协调完成,则可以理解成是脑连接、脑网络的理论基础。这两个法则表面上看似相悖,却相辅相成。对大脑的功能整合研究,通常基于功能连接(functional connectivity)、效应连接(effective connectivity)及结构连接(structural connectivity)的概念。本文主要以静息态功能磁共振成像(functional magnetic resonance imaging, fMRI)为载体,以脑连接方法学发展为主线,并在不同程度上对这些方法加以发展和创新以及临床应用,以更准确地对人类脑连接进行表达和分析。其主要内容包括以下三个部分:
第一部分,效应连接网络研究,也即方法学发展部分。提出了非线性Granger预测分析(Granger causality analysis, GCA)脑网络方法,更有效地揭示了脑功能活动的效应连接。
首先,发展核函数Granger预测模型于运动想象任务,以适应信号的线性和非线性特点,从而扩展线性GCA模型,他忽略了脑区生理特点是非线性特点,将很好的解决人脑内非线性效应连接探测问题,用以揭示脑功能区域间脑功能活动信息的线性和非线性效应连接。
其次,提出中尺度脑功能网络间的空间独立成分分析(independent component analysis, ICA)—GCA(ICA—GCA)模型,实现了静息态脑功能模块空间模式和对应时间特征信号分离,揭示了脑功能模块之间加工过程的信息转换和整合关系。
最后,首次提出了全脑大尺度效应连接预测分析模型,实现全脑90个解剖感兴趣区域之间脑功能活动的有效连接,拓展了传统的相关方法得到的脑功能活动的功能连接。本文对静息状态下脑功能区域间信息整合的效应连接进行研究,探索是否一些扮演了信息转换角色的重要脑区;整个大脑效应连接网络是否具有相对独立而又相互联系的模块;人脑的效应连接网络是否具有“小世界”特性,本文方法及结果为理解人脑效应连接网络及其网络拓扑性质提供了全新的思路。
第二部分,多模态癫痫脑网络研究,也即神经疾病应用部分。提出了癫痫脑网络多模态分析方法,全面地揭示癫痫脑网络机制;发现癫痫脑功能连接及结构连接网络拓扑性质异常,且发现功能—结构耦合性反映该疾患进展程度。
首先,对于内侧颞叶癫痫(mesial temporal lobe epilepsy, mTLE)患者,采用全脑大尺度静息态功能连接网络分析方法,探测其脑功能网络拓扑性质异常;该方法所示的脑局部连接及全脑大尺度网络拓扑性质可能成为甄别内侧颞叶癫痫的生物标记。
其次,针对内侧颞叶癫痫患者默认网络功能连接异常,我们组合功能磁共振成像和弥散张量成像数据,融合功能连接—结构连接方法发现内侧颞叶癫痫患者默认网络功能连接和结构连接异常。本文中,以内侧颞叶癫痫病人磁共振数据为载体,从另一个方面证实了将功能连接和结构连接分析方法结合起来并作为一种新的研究手段的重要意义。同时这种方法也会越来越广泛地被应用到其它磁共振研究领域。
最后,针对特发性全面性癫痫(idiopathic generalized epilepsy, IGE)的全局病理特性,我们在全脑大尺度网络层面上,利用功能连接网络—结构连接网络,并结合网络图论分析方法,探测全面性癫痫的全局网络拓扑属性。更为重要的是,我们利用功能—结构连接网络耦合性这一指标发现患者的功能—结构连接网络耦合性显著降低,并且和病程负相关,这样结果显示功能—结构连接网络耦合反映了该疾患的进展程度。这种指标也许能用来作为特发性全面性癫痫的生物学标记,有助于影像学诊断及其机制理解。
第三部分,多模态社交焦虑障碍(social anxiety disorder, SAD)脑网络研究,也即精神疾病应用部分。提出了社交焦虑障碍多模态分析方法,发现社交焦虑障碍脑功能—结构异常变化特征,及杏仁核效应连接环路异常。
首先采用空间独立成分分析和利用发展的脑网络分析方法探究社交焦虑障碍患者的听觉网络、视觉网络、感觉运动网络、背侧注意网络、中央执行网络、自我参照网络、核心网络、默认网络,然后将这些网络模式和正常对照进行比较,以确定社交焦虑障碍引起的网络连接异常。本文也从功能体系的角度提供了一种研究社交焦虑障碍的神经、病理生理机制的新途径。
其次,采用GCA方法探测社交焦虑障碍患者杏仁核环路的静息态效应连接异常,寻找特异的异常效应连接,使其能够区分和理解社交焦虑障碍患者的病理机制。
最后,首次融合基于体素的形态学测量,静息态功能连接及白质纤维束结构连接这三种模态,发现社交焦虑障碍引起脑形态、功能连接及结构连接体系异常。该研究提供了一种利用形态学体积、静息态功能连接及白质纤维束追踪的串行多模态影像学融合方法,为全面地理解该疾患的生理病理机制夯实了基础。
Joint together 100 billion neurons—with 100 trillion connections—the human brain is known as one of the complex system. Nevertheless, functional segregation and functional integration are related to the dialectic between localizationism and connectionism, which dominates ideas about brain’s functional architecture and operational principles. The functional segregation refers that a cortical area is specialized for some aspects of perceptual or motor processing, and this specialization is anatomically segregated within the cortex. However, the functional integration suggests that many neuronal or distributed brain areas collaborate with each other to complete specific behavioral function, which underlies the brain connectivity. These two distinct principles supplement each other. Various approaches to characterizing the functional integration are in terms of functional connectivity, effective connectivity and structural connectivity.
Following methodological development, the current work investigated the human brain network on functional magnetic resonance imaging (fMRI) in detail. We aim to develop these methods and translate then into clinical applications for comprehensively and exactly understanding the human brain connectivity network. Three aspects of this dissertation have been put forward:
The first part is investigation of effective connectivity network, which is an also methodological development. In this part, we addressed a kernel Granger causality analysis (GCA) to describe nonlinear effective connectivity of the human brain, for availably describing brain dynamics.
First, although it is accepted that linear GCA can reveal effective connectivity, the issue of detecting nonlinear connectivity has hitherto not been considered. In the present work, we addressed kernel GCA to describe effective connectivity in real fMRI data of a motor imagery task. Kernel GCA performs linear Granger causality in the feature space of suitable kernel functions, assuming an arbitrary degree of nonlinearity. Our aim is to demonstrate that kernel GCA captures effective couplings not revealed by the linear case.
Additionally, for the first time, we combined ICA, a data-driven approach, to characterize resting state networks (RSNs), and conditional GCA to gain information about the causal influences among these RSNs. We focused on evaluating and understanding the possible effective connectivity within RSNs at meso-scale.
Thirdly, we aimed to reveal network architectures of effective connectivity brain network at maro-scale. We proposed a multivariate GCA and graph theoretical analysis on large-scale resting-state fMRI recordings. We aimed to found that some brain regions acted as pivotal hubs, either being influenced by or influencing other regions, and thus could be considered as information convergence regions. Furthermore, we also aim to examine that this effective connectivity network has a modular structure and a prominent small-world topological property.
In the second part, we devolved a combination of functional and structural connectivity networks to investigate the patients with epilepsy, which is an also neurological disease application. We found that the functional and structural brain network of epilepsy showed aberrant topological attributes, and the functional-structural coupling negatively correlated with duration may associated with the progress of epilepsy.
First, little is known about the changes in functional connectivity and in topological properties of functional networks, associated with patients with mesial temporal lobe epilepsy (mTLE). To this end, we constructed large-scale undirected brain networks derived from functional connectivity among brain regions that was measured by temporal correlation. We suggest that the mTLE alterations observed in functional connectivity and topological properties may be used to define tentative disease markers.
Secondly, according to disrupted functional connectivity in the default mode network (DMN) in mTLE patients as previously suggested, we combined functional and structural connectivity to study the DMN of those patients. We found that functional and structural connectivity between posterior cingulate cortex and bilateral hippocampus were both decreased. Moreover, combining these two modalities is effective and reliable in MRI data processing, and thus provides us a new way in brain research.
Finally, as a global feature of the pathophysiology that widespread brain regions and extensive networks are involved in idiopathic generalized epilepsy (IGE), complex brain network investigation derived from functional and structural connectivity networks based on graph theoretical analysis might be more valuable than local connectional investigations to understand the mechanism of IGE. Importantly, we found deceased functional–structural connectivity network coupling in IGE, and this decoupling was related to duration of the disorder, suggesting that the functional–structural connectivity network coupling may reflect the progress of IGE. Overall, the present study demonstrates for the first time that the IGE is associated with a disrupted topological organization in large-scale brain structural and functional network, opening up new avenues to a better understanding of this disorder.
In the third part of this dissertation, we devolved a combination of functional and structural connectivity to investigate alterations on the patients with social anxiety disorder (SAD), which is an also psychiatric disorder application. We found that the SAD showed an aberrant functional and structural brain network, and abnormal effective connectivity network associated with the amygdale.
First of all, comparing to patients with SAD and healthy controls, combining spatial independent component analysis (ICA) and brain network analysis, we identified and investigated statistical differences on eight RSNs, such as auditory, visual, somato-motor, dorsal attention, central executive, self-referential, core and default mode networks. Our findings might supply a novel way to look into neuro-pathophysiological mechanisms in SAD patients.
In addition, the amygdala is often found to be abnormally recruited in SAD patients. To address this issue, we investigated a network of effective connectivity associated with the amygdala using GCA on resting-state fMRI data at mrico-scale. Our results lend neurobiological support towards SAD.
Finally, we integrated voxel-based morphometry, resting-state functional connectivity analysis, and diffusion tensor imaging tractography to investigate brain morphometric, functional, and structural architecture of SAD. For the first time, this work and findings may provide a valuable basis for future studies combining morphometric, functional and anatomical data in the search for a comprehensive understanding of the neural circuitry underlying SAD.
第一部分,效应连接网络研究,也即方法学发展部分。提出了非线性Granger预测分析(Granger causality analysis, GCA)脑网络方法,更有效地揭示了脑功能活动的效应连接。
首先,发展核函数Granger预测模型于运动想象任务,以适应信号的线性和非线性特点,从而扩展线性GCA模型,他忽略了脑区生理特点是非线性特点,将很好的解决人脑内非线性效应连接探测问题,用以揭示脑功能区域间脑功能活动信息的线性和非线性效应连接。
其次,提出中尺度脑功能网络间的空间独立成分分析(independent component analysis, ICA)—GCA(ICA—GCA)模型,实现了静息态脑功能模块空间模式和对应时间特征信号分离,揭示了脑功能模块之间加工过程的信息转换和整合关系。
最后,首次提出了全脑大尺度效应连接预测分析模型,实现全脑90个解剖感兴趣区域之间脑功能活动的有效连接,拓展了传统的相关方法得到的脑功能活动的功能连接。本文对静息状态下脑功能区域间信息整合的效应连接进行研究,探索是否一些扮演了信息转换角色的重要脑区;整个大脑效应连接网络是否具有相对独立而又相互联系的模块;人脑的效应连接网络是否具有“小世界”特性,本文方法及结果为理解人脑效应连接网络及其网络拓扑性质提供了全新的思路。
第二部分,多模态癫痫脑网络研究,也即神经疾病应用部分。提出了癫痫脑网络多模态分析方法,全面地揭示癫痫脑网络机制;发现癫痫脑功能连接及结构连接网络拓扑性质异常,且发现功能—结构耦合性反映该疾患进展程度。
首先,对于内侧颞叶癫痫(mesial temporal lobe epilepsy, mTLE)患者,采用全脑大尺度静息态功能连接网络分析方法,探测其脑功能网络拓扑性质异常;该方法所示的脑局部连接及全脑大尺度网络拓扑性质可能成为甄别内侧颞叶癫痫的生物标记。
其次,针对内侧颞叶癫痫患者默认网络功能连接异常,我们组合功能磁共振成像和弥散张量成像数据,融合功能连接—结构连接方法发现内侧颞叶癫痫患者默认网络功能连接和结构连接异常。本文中,以内侧颞叶癫痫病人磁共振数据为载体,从另一个方面证实了将功能连接和结构连接分析方法结合起来并作为一种新的研究手段的重要意义。同时这种方法也会越来越广泛地被应用到其它磁共振研究领域。
最后,针对特发性全面性癫痫(idiopathic generalized epilepsy, IGE)的全局病理特性,我们在全脑大尺度网络层面上,利用功能连接网络—结构连接网络,并结合网络图论分析方法,探测全面性癫痫的全局网络拓扑属性。更为重要的是,我们利用功能—结构连接网络耦合性这一指标发现患者的功能—结构连接网络耦合性显著降低,并且和病程负相关,这样结果显示功能—结构连接网络耦合反映了该疾患的进展程度。这种指标也许能用来作为特发性全面性癫痫的生物学标记,有助于影像学诊断及其机制理解。
第三部分,多模态社交焦虑障碍(social anxiety disorder, SAD)脑网络研究,也即精神疾病应用部分。提出了社交焦虑障碍多模态分析方法,发现社交焦虑障碍脑功能—结构异常变化特征,及杏仁核效应连接环路异常。
首先采用空间独立成分分析和利用发展的脑网络分析方法探究社交焦虑障碍患者的听觉网络、视觉网络、感觉运动网络、背侧注意网络、中央执行网络、自我参照网络、核心网络、默认网络,然后将这些网络模式和正常对照进行比较,以确定社交焦虑障碍引起的网络连接异常。本文也从功能体系的角度提供了一种研究社交焦虑障碍的神经、病理生理机制的新途径。
其次,采用GCA方法探测社交焦虑障碍患者杏仁核环路的静息态效应连接异常,寻找特异的异常效应连接,使其能够区分和理解社交焦虑障碍患者的病理机制。
最后,首次融合基于体素的形态学测量,静息态功能连接及白质纤维束结构连接这三种模态,发现社交焦虑障碍引起脑形态、功能连接及结构连接体系异常。该研究提供了一种利用形态学体积、静息态功能连接及白质纤维束追踪的串行多模态影像学融合方法,为全面地理解该疾患的生理病理机制夯实了基础。
Joint together 100 billion neurons—with 100 trillion connections—the human brain is known as one of the complex system. Nevertheless, functional segregation and functional integration are related to the dialectic between localizationism and connectionism, which dominates ideas about brain’s functional architecture and operational principles. The functional segregation refers that a cortical area is specialized for some aspects of perceptual or motor processing, and this specialization is anatomically segregated within the cortex. However, the functional integration suggests that many neuronal or distributed brain areas collaborate with each other to complete specific behavioral function, which underlies the brain connectivity. These two distinct principles supplement each other. Various approaches to characterizing the functional integration are in terms of functional connectivity, effective connectivity and structural connectivity.
Following methodological development, the current work investigated the human brain network on functional magnetic resonance imaging (fMRI) in detail. We aim to develop these methods and translate then into clinical applications for comprehensively and exactly understanding the human brain connectivity network. Three aspects of this dissertation have been put forward:
The first part is investigation of effective connectivity network, which is an also methodological development. In this part, we addressed a kernel Granger causality analysis (GCA) to describe nonlinear effective connectivity of the human brain, for availably describing brain dynamics.
First, although it is accepted that linear GCA can reveal effective connectivity, the issue of detecting nonlinear connectivity has hitherto not been considered. In the present work, we addressed kernel GCA to describe effective connectivity in real fMRI data of a motor imagery task. Kernel GCA performs linear Granger causality in the feature space of suitable kernel functions, assuming an arbitrary degree of nonlinearity. Our aim is to demonstrate that kernel GCA captures effective couplings not revealed by the linear case.
Additionally, for the first time, we combined ICA, a data-driven approach, to characterize resting state networks (RSNs), and conditional GCA to gain information about the causal influences among these RSNs. We focused on evaluating and understanding the possible effective connectivity within RSNs at meso-scale.
Thirdly, we aimed to reveal network architectures of effective connectivity brain network at maro-scale. We proposed a multivariate GCA and graph theoretical analysis on large-scale resting-state fMRI recordings. We aimed to found that some brain regions acted as pivotal hubs, either being influenced by or influencing other regions, and thus could be considered as information convergence regions. Furthermore, we also aim to examine that this effective connectivity network has a modular structure and a prominent small-world topological property.
In the second part, we devolved a combination of functional and structural connectivity networks to investigate the patients with epilepsy, which is an also neurological disease application. We found that the functional and structural brain network of epilepsy showed aberrant topological attributes, and the functional-structural coupling negatively correlated with duration may associated with the progress of epilepsy.
First, little is known about the changes in functional connectivity and in topological properties of functional networks, associated with patients with mesial temporal lobe epilepsy (mTLE). To this end, we constructed large-scale undirected brain networks derived from functional connectivity among brain regions that was measured by temporal correlation. We suggest that the mTLE alterations observed in functional connectivity and topological properties may be used to define tentative disease markers.
Secondly, according to disrupted functional connectivity in the default mode network (DMN) in mTLE patients as previously suggested, we combined functional and structural connectivity to study the DMN of those patients. We found that functional and structural connectivity between posterior cingulate cortex and bilateral hippocampus were both decreased. Moreover, combining these two modalities is effective and reliable in MRI data processing, and thus provides us a new way in brain research.
Finally, as a global feature of the pathophysiology that widespread brain regions and extensive networks are involved in idiopathic generalized epilepsy (IGE), complex brain network investigation derived from functional and structural connectivity networks based on graph theoretical analysis might be more valuable than local connectional investigations to understand the mechanism of IGE. Importantly, we found deceased functional–structural connectivity network coupling in IGE, and this decoupling was related to duration of the disorder, suggesting that the functional–structural connectivity network coupling may reflect the progress of IGE. Overall, the present study demonstrates for the first time that the IGE is associated with a disrupted topological organization in large-scale brain structural and functional network, opening up new avenues to a better understanding of this disorder.
In the third part of this dissertation, we devolved a combination of functional and structural connectivity to investigate alterations on the patients with social anxiety disorder (SAD), which is an also psychiatric disorder application. We found that the SAD showed an aberrant functional and structural brain network, and abnormal effective connectivity network associated with the amygdale.
First of all, comparing to patients with SAD and healthy controls, combining spatial independent component analysis (ICA) and brain network analysis, we identified and investigated statistical differences on eight RSNs, such as auditory, visual, somato-motor, dorsal attention, central executive, self-referential, core and default mode networks. Our findings might supply a novel way to look into neuro-pathophysiological mechanisms in SAD patients.
In addition, the amygdala is often found to be abnormally recruited in SAD patients. To address this issue, we investigated a network of effective connectivity associated with the amygdala using GCA on resting-state fMRI data at mrico-scale. Our results lend neurobiological support towards SAD.
Finally, we integrated voxel-based morphometry, resting-state functional connectivity analysis, and diffusion tensor imaging tractography to investigate brain morphometric, functional, and structural architecture of SAD. For the first time, this work and findings may provide a valuable basis for future studies combining morphometric, functional and anatomical data in the search for a comprehensive understanding of the neural circuitry underlying SAD.
引文
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