本文關(guān)鍵詞：基于大鼠的SSVEP網(wǎng)絡(luò)機(jī)制研究　出處：《電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 穩(wěn)態(tài)視覺誘發(fā)電位 腦網(wǎng)絡(luò) 雙柱模型 偏有向相干分析 粒子群

【摘要】：穩(wěn)態(tài)視覺誘發(fā)電位(Steady state visual evoked potential,SSVEP)具有較高信噪比和穩(wěn)定的頻譜特征等優(yōu)點(diǎn),目前得到了廣泛地應(yīng)用。雖然已有的研究揭示SSVEP的產(chǎn)生涉及到多個腦區(qū),但是其神經(jīng)機(jī)制目前來說還不是很清楚,制約了相關(guān)領(lǐng)域的研究。在本文的研究中,采用了多通道,且兼有高時間、高空間分辨率的顱內(nèi)記錄方式,以此來記錄麻醉狀態(tài)下的大鼠腦電數(shù)據(jù)(electroencephalograph,EEG),從網(wǎng)絡(luò)的層面上研究SSVEP的神經(jīng)機(jī)制。本論文的主要工作如下:1.采用基于圖論的腦網(wǎng)絡(luò)分析方法,針對不同的刺激頻率,我們研究了SSVEP的振幅與網(wǎng)絡(luò)拓?fù)鋵傩灾g的關(guān)系。對于單只大鼠,具體分析了隨時間動態(tài)變化的SSVEP的振幅與其對應(yīng)的網(wǎng)絡(luò)拓?fù)鋵傩灾g的關(guān)系,而在組間,具體分析了其空閑的基線網(wǎng)絡(luò)屬性與SSVEP響應(yīng)的網(wǎng)絡(luò)屬性之間的關(guān)系,以及與SSVEP響應(yīng)強(qiáng)度的關(guān)系。我們的結(jié)果首次揭示了SSVEP的產(chǎn)生與節(jié)點(diǎn)分布于全腦的網(wǎng)絡(luò)重組有著密切的關(guān)系。在SSVEP的產(chǎn)生中,控制態(tài)的網(wǎng)絡(luò)重組扮演著重要的角色,它可以為SSVEP的產(chǎn)生提供一個重要的預(yù)測指標(biāo)。并且在與其他狀態(tài)下的網(wǎng)絡(luò)對比中發(fā)現(xiàn),能夠誘發(fā)出較強(qiáng)SSVEP的8Hz刺激下的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)連接更加緊密,并且枕葉與額葉之間的長程連接明顯增多,所以我們推測,枕葉和額葉之間的信息交互在SSVEP的生成中扮演著重要角色。2.首次利用基于神經(jīng)參數(shù)集總模型耦合構(gòu)成的雙柱模型,對在SSVEP響應(yīng)中的額葉和枕葉間的信息交互進(jìn)行了研究,揭示了不同頻率刺激下SSVEP響應(yīng)的機(jī)制,并且利用偏有向相干分析方法(Partial directed coherence,PDC),對真實的EEG數(shù)據(jù)作有向網(wǎng)絡(luò)分析,對雙柱模型的結(jié)果進(jìn)行驗證。在本文的研究中,我們采用粒子群方法(Particle Swarm Optimization,PSO)對不同刺激狀態(tài)下的模型參數(shù)進(jìn)行估計,采用與真實腦電數(shù)據(jù)誤差最小的輸出參數(shù),然后基于該參數(shù)來對相應(yīng)SSVEP的機(jī)制進(jìn)行解釋。結(jié)果顯示,對于不同的頻率刺激,在枕葉和額葉內(nèi)部的連接沒有顯著變化,并且額葉向枕葉的信息反饋也沒有明顯的變化,最主要的變化來自于枕葉向額葉的信息傳遞,也就是說,在有SSVEP響應(yīng)的8Hz刺激條件下,顯示出枕葉較強(qiáng)地向額葉信息的傳遞。這一發(fā)現(xiàn)揭示枕葉向額葉的信息傳遞強(qiáng)度對SSVEP的產(chǎn)生有較大的影響,而且這與利用PDC構(gòu)建有向網(wǎng)絡(luò)得到的結(jié)果是一致的。
[Abstract]:Steady state visual evoked potential. SSVEP has the advantages of high signal-to-noise ratio (SNR) and stable spectral characteristics, and has been widely used, although some studies have revealed that the production of SSVEP involves multiple brain regions. However, the neural mechanism is not very clear, which restricts the research in related fields. In this study, we use multi-channel, and have high time, high spatial resolution intracranial recording. The electroencephalographic data of anesthetized rats were recorded. The main work of this thesis is as follows: 1. The brain network analysis method based on graph theory is used to study the neural mechanism of SSVEP. We studied the relationship between the amplitude of SSVEP and the topological properties of the network. The relationship between the amplitude of SSVEP and its corresponding network topology attributes is analyzed in detail. The relationship between its idle baseline network attribute and the network attribute of SSVEP response is analyzed in detail. Our results show for the first time that the generation of SSVEP is closely related to the network recombination of nodes distributed throughout the brain. The network recombination of control state plays an important role, it can provide an important predictor for the generation of SSVEP, and it is found in the comparison with other states of the network. It can induce a stronger SSVEP 8Hz stimulation of the network topology connections more closely, and the occipital lobe and the frontal lobe between the long distance connections significantly increased, so we speculate. The information interaction between occipital lobe and frontal lobe plays an important role in the generation of SSVEP. 2. A two-column model based on neural parameter lumped model is used for the first time. The information exchange between frontal lobe and occipital lobe in SSVEP response was studied, and the mechanism of SSVEP response under different frequency was revealed. And the partial directed coherence analysis method is used to analyze the real EEG data in directed network. The results of the two-column model are verified. In this study, we use particle Swarm Optimization. PSOs are used to estimate the model parameters under different stimulus states, and the output parameters with the smallest error from the real EEG data are used to explain the mechanism of the corresponding SSVEP based on these parameters. The results show that. For different frequency stimuli, the connection between the occipital lobe and the frontal lobe did not change significantly, and the information feedback from the frontal lobe to the occipital lobe did not change significantly, and the most important change came from the information transmission from the occipital lobe to the frontal lobe. That is to say, under the condition of 8Hz stimulation with SSVEP response. The findings show that the occipital lobe strongly transmits information to the frontal lobe. This finding reveals that the intensity of information transmission from occipital lobe to frontal lobe has a great influence on the production of SSVEP. And this is consistent with the result of using PDC to build a directed network.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R338

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