基于靜息態(tài)磁共振的突顯網(wǎng)絡(luò)頻率特異性研究
發(fā)布時間：2018-12-16 16:53
【摘要】：神經(jīng)活動的基本特征表現(xiàn)為神經(jīng)活動的節(jié)律性,是指神經(jīng)元在某個相同頻率的活動特征。大腦中不同腦區(qū)的神經(jīng)振蕩有不同的優(yōu)勢頻率,腦區(qū)功能越高級,其所體現(xiàn)的優(yōu)勢頻率越低,表現(xiàn)為趨于功能整合的低頻神經(jīng)振蕩,而部分信息加工的功能分離發(fā)生在更高級的腦區(qū)活動。然而因為技術(shù)方面的缺陷,無法揭示低頻神經(jīng)振蕩的生理學(xué)機(jī)制,需要更先進(jìn)的技術(shù)。功能磁共振成像技術(shù)(functional magnetic resonance imaging,fMRI)是目前研究低頻神經(jīng)振蕩的主要工具,主要原因是其采集的低頻信號是穩(wěn)定的,圖像的空間分辨率很高,存在的神經(jīng)活動具有相關(guān)性。在傳統(tǒng)的低頻振蕩研究中,突顯網(wǎng)絡(luò)(salience network,SN)可被分成背側(cè)認(rèn)知網(wǎng)絡(luò)和腹側(cè)情緒網(wǎng)絡(luò)。而突顯網(wǎng)絡(luò)的主要區(qū)域是腦島,右側(cè)前腦島在協(xié)調(diào)其他腦網(wǎng)絡(luò)的自適應(yīng)行為方面起到重要作用。然而,網(wǎng)絡(luò)間的協(xié)調(diào)是否受時間-空間限制的,這是一個值得深思的問題。換句話說,腦島的不同亞區(qū)是否存在功能分離和功能整合?在不同頻段是否存在頻譜指紋現(xiàn)象?窄的頻段間隔難以闡述這些網(wǎng)絡(luò)的復(fù)雜功能作用。通過網(wǎng)上的人類腦連接項目(human connectcome project,HCP)大數(shù)據(jù),我們計算不同頻段的功能連接的頻率特異性,比如全頻段(full frequency,0.001-0.694Hz)、亞慢頻段(infra slow frequency,0.001-0.1Hz)、慢頻段(slow frequency,0.1-0.694Hz)。研究結(jié)果發(fā)現(xiàn)背側(cè)前腦島構(gòu)成的背側(cè)突顯網(wǎng)絡(luò)跟外在朝向網(wǎng)絡(luò)之間有聯(lián)系,而腹側(cè)突顯網(wǎng)絡(luò)與內(nèi)在朝向網(wǎng)絡(luò)有連接,比如,可以發(fā)現(xiàn)背側(cè)突顯網(wǎng)絡(luò)跟中央執(zhí)行網(wǎng)絡(luò)存在連接,而腹側(cè)突顯網(wǎng)絡(luò)跟默認(rèn)網(wǎng)絡(luò)存在連接;甚至背側(cè)突顯網(wǎng)絡(luò)和腹側(cè)突顯網(wǎng)絡(luò)有各自的頻率效應(yīng)區(qū)域;慢頻段時,突顯網(wǎng)絡(luò)的功能連接分布存在右側(cè)偏側(cè)化現(xiàn)象;而且兩個突顯網(wǎng)絡(luò)的功能連接區(qū)域有重疊,部分區(qū)域是存在生理意義的。這些發(fā)現(xiàn)為我們理解突顯網(wǎng)絡(luò)功能連接分布的頻率特征提供依據(jù),同時揭示了網(wǎng)絡(luò)頻率特異性的生理機(jī)制,將對生理機(jī)制的理解從低頻分析延伸到高頻范圍,例如枕葉α波的躲避作用。這些發(fā)現(xiàn)對于功能分離和功能整合、網(wǎng)絡(luò)間的協(xié)調(diào)有重要的啟示。
[Abstract]:The basic characteristic of neural activity is the rhythm of neural activity, which refers to the characteristics of neuron activity at the same frequency. The higher the brain function, the lower the dominant frequency. Part of the functional separation of information processing occurs in higher-level brain activity. However, due to technical shortcomings, it is impossible to reveal the physiological mechanism of low frequency neural oscillation, and more advanced techniques are needed. Functional Magnetic Resonance Imaging (functional magnetic resonance imaging,fMRI) is the main tool for studying low frequency neural oscillations. The main reason is that the low frequency signals collected by (functional magnetic resonance imaging,fMRI are stable, the spatial resolution of images is very high, and the neural activity is correlated. In the traditional low-frequency oscillation research, the salient network (salience network,SN) can be divided into dorsal cognitive network and ventral emotional network. The main area that highlights the network is the brain island, and the right forebrain island plays an important role in coordinating the adaptive behavior of other brain networks. However, whether the coordination between networks is limited by time-space is a question worth pondering. In other words, is there functional separation and functional integration in different subareas of the brain island? Is there a spectrum fingerprint phenomenon in different frequency bands? It is difficult to describe the complex function of these networks at narrow frequency intervals. Using the online human brain connection project (human connectcome project,HCP) big data, we calculated the frequency specificity of functional connections at different frequencies, such as full frequency band (full frequency,0.001-0.694Hz), subslow band (infra slow frequency, 0.001-0.1Hz, slow band (slow frequency,0.1-0.694Hz). The results show that there is a connection between the dorsal salience network and the outward orientation network, while the ventral salience network is connected to the inward orientation network. For example, it can be found that there is a connection between the dorsal salience network and the central executive network. But the ventral side highlights the network to have the connection with the default network; Even the dorsal and ventral salience networks have their own frequency effect regions, and the distribution of the functional connections of the salient networks has the phenomenon of right-flanking in the slow band. Moreover, there is overlap between the two salient networks, some of which have physiological significance. These findings provide a basis for us to understand the frequency characteristics that highlight the distribution of network functional connections, and at the same time reveal the network frequency specific physiological mechanism, which extends the understanding of physiological mechanism from low frequency analysis to high frequency range. For example, occipital 偽-wave avoidance. These findings have important implications for functional separation and functional integration and coordination between networks.
【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】：2017
【分類號】：R445.2;R338
[Abstract]:The basic characteristic of neural activity is the rhythm of neural activity, which refers to the characteristics of neuron activity at the same frequency. The higher the brain function, the lower the dominant frequency. Part of the functional separation of information processing occurs in higher-level brain activity. However, due to technical shortcomings, it is impossible to reveal the physiological mechanism of low frequency neural oscillation, and more advanced techniques are needed. Functional Magnetic Resonance Imaging (functional magnetic resonance imaging,fMRI) is the main tool for studying low frequency neural oscillations. The main reason is that the low frequency signals collected by (functional magnetic resonance imaging,fMRI are stable, the spatial resolution of images is very high, and the neural activity is correlated. In the traditional low-frequency oscillation research, the salient network (salience network,SN) can be divided into dorsal cognitive network and ventral emotional network. The main area that highlights the network is the brain island, and the right forebrain island plays an important role in coordinating the adaptive behavior of other brain networks. However, whether the coordination between networks is limited by time-space is a question worth pondering. In other words, is there functional separation and functional integration in different subareas of the brain island? Is there a spectrum fingerprint phenomenon in different frequency bands? It is difficult to describe the complex function of these networks at narrow frequency intervals. Using the online human brain connection project (human connectcome project,HCP) big data, we calculated the frequency specificity of functional connections at different frequencies, such as full frequency band (full frequency,0.001-0.694Hz), subslow band (infra slow frequency, 0.001-0.1Hz, slow band (slow frequency,0.1-0.694Hz). The results show that there is a connection between the dorsal salience network and the outward orientation network, while the ventral salience network is connected to the inward orientation network. For example, it can be found that there is a connection between the dorsal salience network and the central executive network. But the ventral side highlights the network to have the connection with the default network; Even the dorsal and ventral salience networks have their own frequency effect regions, and the distribution of the functional connections of the salient networks has the phenomenon of right-flanking in the slow band. Moreover, there is overlap between the two salient networks, some of which have physiological significance. These findings provide a basis for us to understand the frequency characteristics that highlight the distribution of network functional connections, and at the same time reveal the network frequency specific physiological mechanism, which extends the understanding of physiological mechanism from low frequency analysis to high frequency range. For example, occipital 偽-wave avoidance. These findings have important implications for functional separation and functional integration and coordination between networks.
【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】：2017
【分類號】：R445.2;R338
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