摘要
大脑是迄今为止人类所知最为复杂，最为精细的系统，它由解剖上相连、功能上既相互独立又相互影响的脑区所组成。脑网络研究是目前神经科学的研究热点之一，它能够从系统水平上研究脑区间的连接机制，揭示大脑的内在组织模式。近年，脑成像技术，尤其是磁共振成像技术（magnetic resonance imaging, MRI）的出现和快速发展提供了获取大脑各种数据的影像学工具，而基于图论的复杂网络分析则为脑网络的研究提供了强有力的技术手段。本文将以脑网络分析方法的发展和应用为研究重点，以磁共振成像技术中最热门的两大成像技术—功能磁共振成像（functional magnetic resonance imaging, fMRI）和弥散张量成像（diffusion tensorimaging, DTI）为研究工具，对大脑的功能连接网络和结构连接网络进行研究，结合图论分析探测大脑的内在组织特性，并进一步将其应用于临床大脑疾病中，从系统水平层面探测大脑疾病的病理生理机制。本文的主要研究工作包括如下几个部分：
1.利用独立成分分析（independent component analysis, ICA）和图论分析对健康被试的静息态功能磁共振成像（fMRI）数据进行分析，研究大脑静息态网络（resting-state networks, RSNs）的拓扑特性。结果表明静息态网络（RSNs）具有高效的“小世界”组织特性。更重要的是，我们发现高级认知网络（如注意、中央执行和默认网络）和初级感知网络（如视觉、听觉和感觉运动网络）的网络拓扑指标存在显著差异，首次从网络属性上定量地证明了高级认知和初级感知网络间的分级现象。该方法可扩展到临床应用，尤其对于特定功能子网络（如RSNs）异常的大脑疾病具有较好的应用价值和意义。
2.采用基于全脑大尺度功能连接分析方法对社交焦虑障碍（social anxietydisorder, SAD）患者的功能连接网络进行研究。结果发现，与健康对照相比，SAD患者异常的功能连接，主要存在于额叶皮层和枕叶皮层。此外，部分异常脑区功能连接与社交焦虑症状的严重程度存在相关性。该研究旨在从整体水平上研究病理状态下脑功能连接网络的异常，所发现的异常功能连接可能作为SAD患者早期诊断的生物学标记。
3.融合基于静息态功能磁共振成像（fMRI）的功能连接和基于弥散张量成像（DTI）的结构连接分析对心因性非癫痫发作（psychogenic non-epilepsy seizures,PNES）的脑功能网络和结构网络进行研究。图论分析的结果表明PNES患者的功能连接和结构连接网络的组织特性遭受破坏，并趋向规则化网络发展。此外，PNES患者结构连接网络的节点属性发生了异常改变，涉及的脑区与注意网络、感觉运动网络、皮层下脑区和默认网络有关。进一步，我们还发现PNES患者的功能和结构连接的耦合强度显著降低，且降低的耦合强度与患者的病程呈现负相关。ROC（receiver operating characteristic, ROC）曲线分析发现耦合强度这一指标具有较高的敏感性和特异性来区分PNES患者和健康被试。该研究证实了功能连接和结构连接网络的融合分析能够为揭示PNES的内在病理生理机制提供一个新的思路。
4.利用一种快速的基于体素的数据驱动方法—功能连接密度（functionalconnectivity density, FCD）分析探测PNES患者的短程功能连接密度（short-rangeFCD）和长程功能连接密度（long-range FCD）。结果显示PNES患者的功能连接密度发生异常的脑区主要涉及注意、情感和感觉运动系统。此外，异常的功能连接密度还涉及枕叶皮层，且部分异常的脑区与患者的病程具有相关性，我们推侧这可能与PNES患者对外界的警觉性和响应增强的泛化有关。该研究从功能连接密度的角度进一步拓展了我们对PNES病理生理机制的了解。
The human brain is so far considered to the most complex, most delicate object inthe universe, which is organized into parallel, interacting systems of anatomicallyconnected areas. Nowadays, brain network research is one of the hot topics in the fieldof neuroscience, which can investigate the connection mechanism between brainregions on the level of system, and further reveal the underlying organization of thebrain. In recent years, the invention and rapid development of brain imagingtechnologies, especially magnetic resonance imaging (MRI), provide imaging tool toacquire various brain data; while complex network analysis based on graph theoryprovides a valuable technical means for brain network research. Focusing on thedevelopment and clinical applications of brain network analysis, this dissertationcombined two popular imaging technologies based on MRI, that is, functional magneticresonance imaging (fMRI) and diffusion tensor imaging (DTI), and graph theoreticalanalysis to investigate brain functional connectivity and structural connectivitynetworks, reveal the underlying topological organization of the brain in the healthysubjects, and further detect the pathophysiological mechanism of clinical brain diseasesfrom the system level. The main works and contributions of this dissertation are asfollows:
1. Combining independent component analysis (ICA) and graph theoreticalanalysis to deal with resting-state fMRI (rs-fMRI) data in healthy subjects andinvestigate the topological properties of resting-state networks (RSNs). Our resultsshowed that each RSN had robust small-world properties. More important, the networktopological properties were significantly different between higher cognitive networks(dorsal attention network, central-executive network and default mode network) andperceptual networks (visual network, auditory network and somato-motor network).These findings for the first time provide quantitative evidence for the topologicalfractionation between higher cognitive and perceptual networks. Our approach toinvestigate topological properties in RSNs may be extended to clinical research, especially to diseases that show selective abnormal connectivity in specific brainnetworks
2. Using large-scale functional connectivity analysis to investigate whole-brainfunctional connectivity network in social anxiety disorder (SAD). Compared withhealthy controls, SAD patients exhibited abnormal functional connectivity, especiallyinvolving the frontal cortex and occipital cortex. In addition, correlations were detectedbetween changes in resting functional connectivity and social anxiety symptom severityin some cases. The aim of this study was to investigate the abnormal functionalconnectivity under pathologic state from the whole-brain level, and our findings couldrepresent an early imaging biomarker for SAD.
3. Combining functional connectivity based on resting-state fMRI signalcorrelations and structural connectivity based on diffusion tensor imaging tractographyto investigate brain functional and structural networks in psychogenic non-epilepsyseizures (PNES). Using graph theoretical analysis, we found that PNES patients lostoptimal topological organization in functional connectivity and structural connectivitynetworks reflected by a shift towards more regular brain architecture. In addition,structural connectivity networks exhibited altered regional characteristics in some keyregions associated with attention, sensorimotor, subcortical and default-mode systems inPNES patients. Most importantly, we found that the coupling strength betweenfunctional and structural connectivity was significantly decreased, and this decrease wasmore marked in patients with a longer duration of disease. When taking couplingstrength as an index for plotting receiver operating characteristic (ROC) curves, PNESpatients can be differentiated from healthy controls with high sensitivity and specificity.These results may provide new insights into our understanding of thepathophysiological mechanisms of PNES.
4. Using functional connectivity density (FCD) mapping, an ultrafast andvoxelwise data-driven approach, to measure short-and long-range FCD in PNESpatients. Compared with health controls, PNES patients showed abnormal FCD inregions associated with attention, emotion and sensorimotor systems. In addition, someregions in occipital cortex also exhibited abnormal long-range FCD, and were correlatedwith duration of disease, which could be related to generalization of greater vigilance orresponsivity to environmental stimuli in PNES patients. The findings further improve our understanding of the pathophysiological mechanisms of PNES using functionalconnectivity density mapping.
引文
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