本文選題：網(wǎng)絡(luò)分析方法　切入點(diǎn)：腦電節(jié)律　出處：《清華大學(xué)》2012年博士論文　論文類型：學(xué)位論文

【摘要】：利用網(wǎng)絡(luò)分析方法可以研究大腦各個(gè)區(qū)域結(jié)構(gòu)或功能連接，為解釋大腦深層機(jī)理提供更多的線索。本論文使用網(wǎng)絡(luò)分析方法研究腦電節(jié)律信號(hào)，有別與傳統(tǒng)單區(qū)域分離的研究思路，網(wǎng)絡(luò)分析方法是一種大腦多區(qū)域聯(lián)合分析的研究思路。采用的方法為基于典型相關(guān)分析（canonical correlation analysis，CCA）的相關(guān)網(wǎng)絡(luò)計(jì)算方法和基于有向傳遞函數(shù)（directed transfer function，DTF）的腦功能模式特征計(jì)算方法。具體研究?jī)?nèi)容包括對(duì)誘發(fā)式節(jié)律穩(wěn)態(tài)視覺(jué)誘發(fā)電位（steady-statevisual evoked potential，SSVEP）和自主式節(jié)律想象運(yùn)動(dòng)（motor imagery，MI）兩種腦電節(jié)律信號(hào)的網(wǎng)絡(luò)分析。 本研究首先使用基于CCA的網(wǎng)絡(luò)分析方法研究半視野刺激誘發(fā)SSVEP下激活的腦相關(guān)網(wǎng)絡(luò)特征。結(jié)果表明，，基于CCA的網(wǎng)絡(luò)分析方法可以很好的分辨出半視野頻率的神經(jīng)交叉投影現(xiàn)象。在此基礎(chǔ)上，提出了頻率和空間聯(lián)合調(diào)制的腦-機(jī)接口（brain-computer interface，BCI）系統(tǒng)。該系統(tǒng)的特點(diǎn)是使用較少的頻率便可以呈現(xiàn)較多的刺激目標(biāo)。 目前，SSVEP的具體生理機(jī)制還不十分清晰，本文研究了不同頻率誘發(fā)下SSVEP所激勵(lì)的大腦網(wǎng)絡(luò)模式。結(jié)果發(fā)現(xiàn)枕頂葉（parietal）區(qū)域是一個(gè)激勵(lì)網(wǎng)絡(luò)的信息交互樞紐（hub），亦即連接皮層（connection cortex），對(duì)于視覺(jué)信息的處理有著重要的意義。在進(jìn)一步的分析中，本文首次提出了信息流增益（flow gain）的概念，利用該方法從腦信息連通的角度，以直觀的形式呈現(xiàn)腦網(wǎng)絡(luò)的信息流特征。 想象運(yùn)動(dòng)是BCI的典型范式，本文分析其基于腦電的腦功能網(wǎng)絡(luò)模式特征，對(duì)其中一名偏側(cè)中風(fēng)患者的功能連接模式圖進(jìn)行了較為細(xì)則的研究。與正常受試結(jié)果相比較，偏側(cè)中風(fēng)患者的腦電網(wǎng)絡(luò)模式特征呈現(xiàn)出較強(qiáng)的對(duì)側(cè)集中現(xiàn)象。這與該名患者一側(cè)運(yùn)動(dòng)區(qū)損傷有較好的對(duì)應(yīng)關(guān)系。利用想象運(yùn)動(dòng)腦-機(jī)接口對(duì)患側(cè)肢體進(jìn)行康復(fù)訓(xùn)練，結(jié)果證實(shí)該患者可以有效的使用想象運(yùn)動(dòng)來(lái)控制腦-機(jī)接口系統(tǒng)。此外，借由功能網(wǎng)絡(luò)特征模式圖觀測(cè)訓(xùn)練過(guò)程中大腦的結(jié)構(gòu)變化，初步結(jié)果表明該方法有可能為中風(fēng)患者康復(fù)評(píng)估提供一個(gè)有效、直接的途徑。 本文還對(duì)生成網(wǎng)絡(luò)做了一些探索研究，結(jié)果表明聯(lián)合使用網(wǎng)絡(luò)分析方法和圖論工具對(duì)于腦科學(xué)的研究有重要幫助。
[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.
【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2012
【分類號(hào)】：R741;R318.0

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