摘要
近年来,随着功能磁共振和神经电生理技术的开发与利用,人脑活动数据的采集已经成为可能。如何从脑活动数据中发现具有生物学意义的知识及规律,正在成为当前神经信息学理论与实践研究的热点与难点。与此同时,复杂网络的研究在快速发展,在众多领域得到应用,研究者采用相同量化参数,发现一些共有的拓扑结构。人脑功能需要不同的神经环路交互作用才能实现,在复杂网络研究框架下,以交互作用为网络连接,以解剖脑区或电极为网络节点,构造脑功能网络,研究不同状态下的网络拓扑特性,有助于揭示脑内信息加工机制。
本论文基于功能磁共振和脑电数据,从复杂网络角度,分别研究了静息状态、冥想状态、任务状态的脑功能网络拓扑特性。论文的主要研究工作及贡献包括以下几个方面：
1.研究静息态脑功能网络的非随机拓扑结构。基十静息态功能磁共振数据,采用标准脑模板和偏相关方法来构建人脑功能网络,并进行拓扑结构分析。以最大生成树的方法进行脑网络骨架可视化,结果发现树中央区域主要由默认模式网络和注意网络脑区组成。应用谱平分算法,整个脑网络以划分成大小不同的具有生物学意义的功能团结构,每个团具有相对独立的功能。研究脑网络节点和连接在功能团内部和外部的作用,结果发现处于拓扑中心的核心节点和桥共同形成脑网络信息整合的中心。本研究勾画出人脑功能网络骨架和功能团结构的图谱,有利于我们进一步理解脑内信息加工的功能分割和功能整合机制。
2.研究高低频段对静息态脑功能网络的影响。将静息态数据划分为高频段(0.027～0.073Hz)和低频段(0.01～0.027Hz),构建差异化脑功能网络,计算二组频段的低频振荡振幅、局部一致性、功能连通性等量化指标的差异。结果发现,频段差异主要集中在默认模式网络的中线位置和杏仁核功能区域。本研究的差异化脑功能网络的构建方法和频率对脑功能网络影响范围的发现拓展了脑功能网络研究思路。
3.冥想状态脑功能网络特性研究。比较一个月冥想训练前后的脑功能网络拓扑特性和连通模式,发现处于大脑自我调节中心的前扣带回脑区的效率呈现增加趋势,而辅助运动区网络指标值呈现下降趋势,这种网络特性的改变可能用米维持通过训练达到的冥想状态。本研究从新的网络视角提供了经验相关的脑功能网络特性改变的实证支持。
4.任务状态脑功能网络的差异性研究。基于事件相关电位数据,采用同步似然方法计算电极间的功能关系,分析和比较了在道德两难决策过程中涉及个人和非涉及个人情景下的脑功能网络特性的差别。研究发现,涉及个人情景在右脑有更多更强的长程连接,并且与额叶有关,揭示了脑功能网络的连通模式与道德两难决策中认知和情感间冲突程度的表征有着密切关系。本研究提出道德加工的分布式机制和发现高冲突道德两难的大脑右侧化现象,有助于为任务脑功能网络和道德认知神经科学的研究拓宽思路。
总之,本论文对几种典型状态的脑功能网络做了一次系统研究的重要尝试,获得了一些有价值的结果。论文所提的方法和研究结果将促进脑功能网络研究的发展和完善,为笔者未来研究工作奠定了坚实的基础。
In the past decade, a multitude of human brain activity data has been acquired with the fast development of functional magnetic resonance imaging and neural electrophysiological techniques. How to find valuable and biologically meaningful knowledge and rules behind these data is a critical problem and is a hot research topic in neuroinformatics. At the same time, complex networks have attracted great attention as a compelling framework with which complex systems are being studied in many fields. Many natural and man-made networks generated from different datasets have been exhibited common principles that govern network behavior and can be quantitatively characterized by the same parameters. The human brain generates large amount of oscillatory neural activity in support of brain function. The interplay between the activity of different brain regions and their integration is crucial to the organizational principles that underlie cognitive and brain function, and one strategy is quantitative analysis and characterization of these brain activity from a network perspective.
In the present dissertation, we investigate the organization rules behind multimodal experimental image data by combining computer science, neuroscience, imaging and statistics, as well as the psychology knowledge, and mainly focus on the topological properties of brain networks at rest or during tasks and meditation-related brain network plasticity. The main contents and contributions of the dissertation are as following.
1. Non-random organization of resting brain networks. We investigated the topological properties of brain networks derived from resting-state functional magnetic resonance imaging. A prior anatomical automatic labeling template was first used to parcellate the brain volume into90network nodes and functional connectivity was defined by partial correlation coefficients between the mean time series of each pair of anatomically unconnected regions. The connectivity backbone represented by a maximum spanning tree was then used to visualize network layout and most brain areas of the default and attention-related network activity were observed to be distributed at the center of the network layout as important functional hubs of information processing at rest. Newman's spectral optimization method was applied to reveal the intrinsically community architectures and the resulting nonrandom structure fit well some biologically meaningful functional systems of the brain. Both brain regions (nodes) and connections in the brain network were then classified into different categories, and several hub regions and bridge connections played pivotal roles in the global information transfer, whose lesions exhibited different impacts on network efficiency. These studies mapped the connectivity skeleton and community structure of brain networks and would help us understand functional specialization and functional integration during information processing in the human brain.
2. Frequency-related differences observed in functional brain networks. We used amplitude of low frequency fluctuation, regional homogeneity, functional connectivity and complex network to reveal distinctions in the default mode network and amygdale. The new method of constructing brain networks and the effects of frequency on brain networks would provide new insights in studies of functional brain networks.
3. Meditation-related brain plasticity observed in functional brain networks. We adopted meditation as a vehicle and examined changes of network topological properties due to training. The results indicated network properties of the anterior cingulate during resting state condition were altered by short-term meditation, and attempted to provide an interpretation of improvement in self-regulation. The study provided empirical support of experience-driven brain plasticity from a network perspective.
4. Research of brain networks derived from event-related potential (ERP). We applied synchronization likelihood analysis and graph theory-based network analysis of ERP data in moral decision making to study the personal/impersonal distinction in organization of functional connectivity. The results indicated that the personal task had some larger long-range connections involved in frontal regions and the right hemisphere, and higher network efficiency of some frontal electrodes than the impersonal. This may be related to brain resource reorganization contributing to efficient conflict resolution mediated by distributed processing.
引文
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