基于脑电节律的脑网络研究及应用

发布时间：2018-03-09 15:54

本文选题：网络分析方法　切入点：脑电节律　出处：《清华大学》2012年博士论文　论文类型：学位论文

【摘要】：利用网络分析方法可以研究大脑各个区域结构或功能连接，为解释大脑深层机理提供更多的线索。本论文使用网络分析方法研究脑电节律信号，有别与传统单区域分离的研究思路，网络分析方法是一种大脑多区域联合分析的研究思路。采用的方法为基于典型相关分析（canonical correlation analysis，CCA）的相关网络计算方法和基于有向传递函数（directed transfer function，DTF）的脑功能模式特征计算方法。具体研究内容包括对诱发式节律稳态视觉诱发电位（steady-statevisual evoked potential，SSVEP）和自主式节律想象运动（motor imagery，MI）两种脑电节律信号的网络分析。本研究首先使用基于CCA的网络分析方法研究半视野刺激诱发SSVEP下激活的脑相关网络特征。结果表明，，基于CCA的网络分析方法可以很好的分辨出半视野频率的神经交叉投影现象。在此基础上，提出了频率和空间联合调制的脑-机接口（brain-computer interface，BCI）系统。该系统的特点是使用较少的频率便可以呈现较多的刺激目标。目前，SSVEP的具体生理机制还不十分清晰，本文研究了不同频率诱发下SSVEP所激励的大脑网络模式。结果发现枕顶叶（parietal）区域是一个激励网络的信息交互枢纽（hub），亦即连接皮层（connection cortex），对于视觉信息的处理有着重要的意义。在进一步的分析中，本文首次提出了信息流增益（flow gain）的概念，利用该方法从脑信息连通的角度，以直观的形式呈现脑网络的信息流特征。想象运动是BCI的典型范式，本文分析其基于脑电的脑功能网络模式特征，对其中一名偏侧中风患者的功能连接模式图进行了较为细则的研究。与正常受试结果相比较，偏侧中风患者的脑电网络模式特征呈现出较强的对侧集中现象。这与该名患者一侧运动区损伤有较好的对应关系。利用想象运动脑-机接口对患侧肢体进行康复训练，结果证实该患者可以有效的使用想象运动来控制脑-机接口系统。此外，借由功能网络特征模式图观测训练过程中大脑的结构变化，初步结果表明该方法有可能为中风患者康复评估提供一个有效、直接的途径。本文还对生成网络做了一些探索研究，结果表明联合使用网络分析方法和图论工具对于脑科学的研究有重要帮助。
[Abstract]:Network analysis can be used to study the structure or functional connection of various regions of the brain, which provides more clues for explaining the deep mechanism of brain. In this paper, network analysis is used to study EEG rhythmic signals. Different from the traditional single-region separation, The method of network analysis is a kind of thinking of joint analysis of multiple regions of the brain, which is based on canonical correlation analysis of canonical correlation analysis and functional pattern of brain based on directed transfer function. The detailed contents of this study include the network analysis of steady-state evoked potentialSSVEP (Steady-statevisual evoked potentialSSVEP) and motor imageryMi (autonomous rhythmic motor imageryMi) in the steady-state visual evoked potential (VEP) of evoked rhythms. In this study, CCA based network analysis was first used to study the characteristics of brain related networks activated under SSVEP induced by half-field stimulation. The network analysis method based on CCA can distinguish the neural cross-projection phenomenon of half-field frequency. A brain-computer interface (BCI) system with frequency and spatial joint modulation is proposed, which is characterized by the fact that less frequency can be used to present more stimulative targets. At present, the specific physiological mechanism of SSVEP is not very clear. This paper studies the pattern of brain network excited by SSVEP at different frequencies. The results show that the parietalregion of occipito-parietal lobe is a hub of information interaction of the excitation network, which is connected with cortical connection and plays an important role in the processing of visual information. In further analysis, In this paper, the concept of information flow gain is proposed for the first time, and the information flow characteristics of brain network are presented intuitively from the point of view of brain information connectivity using this method. Imaginary motion is a typical paradigm of BCI. This paper analyzes the characteristics of brain functional network model based on EEG, and makes a detailed study on the functional connection pattern of one of the patients with hemiplegic stroke, which is compared with the results of normal subjects. The characteristics of EEG network pattern in patients with hemiplegic stroke showed a strong contralateral concentration phenomenon, which had a good relationship with the injury of one side of motor area. The rehabilitation training of the affected limbs was carried out by using the imaginary motor brain-computer interface. The results showed that the patient could effectively control the brain-computer interface system by using imaginative motion. In addition, the structural changes of the brain during the training were observed by the functional network characteristic pattern diagram. Preliminary results suggest that this method may provide an effective and direct approach to the assessment of stroke rehabilitation. The results show that the combined use of network analysis methods and graph theory tools is of great help to the research of brain science.
【学位授予单位】：清华大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R741;R318.0

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