本文選題：默認(rèn)模式網(wǎng)絡(luò) + 腦電�。� 參考：《電子科技大學(xué)》2017年博士論文

【摘要】：靜息態(tài)(resting state)下的大腦并非是“空載”的,而是表現(xiàn)出自發(fā)的、有組織的、持續(xù)的神經(jīng)活動(dòng),這些神經(jīng)活動(dòng)形成了多種靜息態(tài)網(wǎng)絡(luò)(resting-state network,RSN)。默認(rèn)模式網(wǎng)絡(luò)(default mode network,DMN)是RSN的一種,因其在靜息態(tài)下表現(xiàn)出獨(dú)特的活動(dòng)模式,并在眾多神經(jīng)和精神疾病中發(fā)生紊亂,受到越來越多的關(guān)注。新近的研究發(fā)現(xiàn),DMN并非人類獨(dú)有,在嚙齒類動(dòng)物中同樣存在。這一發(fā)現(xiàn)為探索DMN的生理和病理機(jī)制提供了理想的臨床前模型。然而,已有的研究大多基于代謝信號(hào),并且絕大部分信號(hào)是在動(dòng)物處于“非自然”狀態(tài)下獲得的,這些不足極大地限制了對(duì)嚙齒類動(dòng)物DMN的研究。在本論文中,我們采集了自由行為狀態(tài)下大鼠DMN的電生理信號(hào),并選取了覺醒靜息(wakeful rest,WR)、慢波睡眠(slow wave sleep,SWS)和快速眼動(dòng)睡眠(rapid eye movement sleep,REMS)三種不同警覺狀態(tài)的數(shù)據(jù),運(yùn)用多種網(wǎng)絡(luò)分析方法,從局部腦電(electroencephalogram,EEG)的振蕩頻段特征、功能網(wǎng)絡(luò)特征(包括動(dòng)態(tài)網(wǎng)絡(luò)特征)和效應(yīng)網(wǎng)絡(luò)特征三個(gè)方面,對(duì)大鼠電生理的DMN進(jìn)行了比較深入的研究。主要內(nèi)容如下:第一,不同警覺狀態(tài)下大鼠DMN的EEG頻段的統(tǒng)計(jì)學(xué)劃分。針對(duì)不同狀態(tài)的振蕩頻段特征,我們采用因子分析(factor analysis)對(duì)EEG振蕩的功率譜密度(power spectral density,PSD)的共變特性進(jìn)行聚類,由此劃分各DMN腦區(qū)的頻段。我們發(fā)現(xiàn)這些劃分出的頻段在狀態(tài)之間以及腦區(qū)之間都存在差異。特別是,REMS狀態(tài)下的θ(theta)振蕩可以被進(jìn)一步細(xì)分為兩個(gè)頻段,分別對(duì)應(yīng)于緊張型REMS(tonic REMS,tREMS)和相位型REMS(phasic REMS,pREMS)兩個(gè)狀態(tài)。同時(shí),我們還在SWS的不同階段提取到對(duì)應(yīng)于不同頻段的兩種紡錘波,包括高電壓紡錘波(high-voltage spindle,HVS)和低電壓紡錘波(low-voltage spindle,LVS)。這些結(jié)果為大鼠DMN在不同警覺狀態(tài)下的神經(jīng)振蕩活動(dòng)提供了新的認(rèn)識(shí),并為后續(xù)研究的數(shù)據(jù)選取以及頻段選擇提供了依據(jù)。第二,探討大鼠電生理DMN功能網(wǎng)絡(luò)的連接特性。在此,我們選取WR、SWS(不含HVS)和tREMS狀態(tài)的數(shù)據(jù),使用PSD、相位鎖時(shí)值(phase locking value,PLV)和模塊化分析(modularity analysis),對(duì)大鼠DMN的局部活動(dòng)和功能網(wǎng)絡(luò)特征進(jìn)行了研究。結(jié)果表明,在不同警覺狀態(tài)下,大鼠DMN中的局部γ(gamma)頻段能量的變化與人類同源腦區(qū)的代謝波動(dòng)具有一致性;并且,基于大鼠電生理DMN的網(wǎng)絡(luò)分析結(jié)果與功能磁共振(functional magnetic resonance imaging,fMRI)研究得到的結(jié)果具有廣泛的相似性。這些結(jié)果為嚙齒類動(dòng)物大腦中DMN的存在提供了電生理的證據(jù)。第三,進(jìn)一步探討大鼠電生理DMN功能網(wǎng)絡(luò)的動(dòng)態(tài)連接特性。在此研究中,我們利用滑動(dòng)窗分析(sliding window analysis)和因子分析,對(duì)不同警覺狀態(tài)下DMN的網(wǎng)絡(luò)動(dòng)態(tài)特征進(jìn)行了研究。我們發(fā)現(xiàn)大鼠電生理DMN是高度動(dòng)態(tài)變化的,并且,動(dòng)態(tài)的DMN可以被進(jìn)一步提取為不同的空間模式(spatial pattern)。其中,部分空間模式僅存在于特定警覺狀態(tài),而其余的空間模式獨(dú)立于警覺狀態(tài)存在。進(jìn)一步,我們發(fā)現(xiàn)空間模式的貢獻(xiàn)隨時(shí)間波動(dòng),并受到警覺狀態(tài)的影響。這些波動(dòng)的空間模式可能為神經(jīng)信息的高效整合提供了一個(gè)構(gòu)架,以維持靈活的認(rèn)知和行為。這些結(jié)果為理解大鼠DMN的動(dòng)態(tài)功能組織提供了新的視角。第四,探究DMN中子區(qū)域之間的效應(yīng)連接(effective connectivity)。本研究中,我們采用定向相位轉(zhuǎn)移熵(directed phase transfer entropy,dPTE),對(duì)不同警覺狀態(tài)下DMN的效應(yīng)連接進(jìn)行了分析。我們發(fā)現(xiàn),在δ(delta)頻段,大鼠DMN內(nèi)存在由前至后的信息流模式;而在θ頻段,存在著相反的信息流模式。絕大多數(shù)在δ頻段信息流出的腦區(qū),在θ頻段存在信息流入,反之亦然,形成了具有頻段特異性的信息流環(huán)路。這一現(xiàn)象僅存在于WR和tREMS狀態(tài)。上述發(fā)現(xiàn)可能揭示了再進(jìn)入的(reentrant)神經(jīng)信息整合機(jī)制,以及潛在的意識(shí)維持機(jī)理。綜上所述,本文基于自由行為狀態(tài)下的大鼠電生理數(shù)據(jù),通過多種網(wǎng)絡(luò)分析方法,從多個(gè)角度探討了大鼠DMN的信息整合功能。我們的發(fā)現(xiàn)一方面佐證了嚙齒類動(dòng)物中DMN的存在,另一方面也為理解其信息整合及意識(shí)維持的生理機(jī)制提供了獨(dú)特的視角。
[Abstract]:The brain under the resting state (resting state) is not "empty", but shows spontaneous, organized, persistent neural activity, which forms a variety of resting-state network (RSN). The default mode network (default mode network, DMN) is a kind of RSN, because it shows unique activity in the resting state. Recent studies have found that DMN is not unique to humans and exists in rodents. This discovery provides an ideal preclinical model for exploring the physiological and pathological mechanisms of DMN. However, most of the previous studies have been based on metabolic signals. And most of the signals are obtained in the "unnatural" state of animals, which greatly limit the study of rodent DMN. In this paper, we collected electrophysiological signals of DMN in rats under free behavior, and selected the awakening quiescent (wakeful rest, WR), slow wave sleep (slow wave sleep, SWS) and rapid development. Three different alerting states of rapid eye movement sleep (REMS), using a variety of network analysis methods, from the characteristics of the oscillation frequency of local electroencephalogram (electroencephalogram, EEG), functional network features (including dynamic network features) and effect network characteristics, three aspects, the electrophysiological DMN of rats is more in-depth. The main contents are as follows: first, the statistical division of the EEG frequency band of the rat DMN under different alert states. According to the characteristics of the oscillatory bands in different states, we use factor analysis (factor analysis) to cluster the co variation of the power spectral density of the EEG oscillation (power spectral density, PSD), and then divide the frequency bands of each DMN brain region. We found that these bands are different between States and between the brain regions. In particular, the theta oscillation in the REMS state can be further subdivided into two bands, corresponding to the two states of the tense REMS (tonic REMS, tREMS) and the phase REMS (phasic REMS, pREMS). At the same time, we also carry out the different stages of SWS. Two kinds of spindle waves corresponding to different frequency bands, including high voltage spindle wave (high-voltage spindle, HVS) and low voltage spindle wave (low-voltage spindle, LVS), are taken. These results provide a new understanding for the nervous oscillation activity of rat DMN in different alert states, and provide the basis for data selection and frequency selection for subsequent research. Second, discuss the connection characteristics of the rat electrophysiological DMN function network. Here, we select WR, SWS (not HVS) and tREMS state data, use PSD, phase lock time value (phase locking value, PLV) and modularized analysis (modularity analysis), and study the local activity and functional network characteristics of the rat. Under the alert state, the local gamma (gamma) band energy changes in the rat DMN are consistent with the metabolic fluctuations in the human homologous brain region; moreover, the results of the network analysis based on the electrophysiological DMN of the rat and the functional magnetic resonance (functional magnetic resonance imaging, fMRI) have extensive similarity. These results are the meshing results. The existence of DMN in the cerebrum of the teeth provides evidence of electrophysiology. Third, we further explore the dynamic connection characteristics of the DMN functional network of rats. In this study, we used sliding window analysis (sliding window analysis) and factor analysis to study the dynamic characteristics of DMN network under different alertness. The electrophysiological DMN of rats is highly dynamic, and dynamic DMN can be further extracted as a different spatial pattern (spatial pattern). In which some spatial patterns exist only in a specific alert state, while the rest of the spatial patterns exist independently of the alert state. The spatial patterns of these fluctuations may provide a framework for the efficient integration of neural information to maintain flexible cognition and behavior. These results provide a new perspective for understanding the dynamic functional organization of the rat DMN. Fourth, explore the effect connection between the DMN neutron region (effective connectivity). In the study, we use directed phase transfer entropy (dPTE) to analyze the effect connection of DMN under different alarm states. We find that in the Delta (delta) band, the rat DMN is in the information flow pattern from the front to the back, while there is the opposite information flow pattern in the theta band. The vast majority of the information flow in the delta band is in the delta band. In the brain region, there is information flow in the theta band and vice versa, forming a loop of frequency specific information flow. This phenomenon exists only in the state of WR and tREMS. The above discovery may reveal the mechanism of the re entry of (reentrant) neural information and the underlying mechanism of the maintenance of consciousness. In summary, this article is based on the state of free behavior. The electrophysiological data of the rats, through a variety of network analysis methods, explored the information integration function of the rat DMN from a variety of angles. Our discovery, on the one hand, supported the existence of DMN in rodents, and on the other hand, it provided a unique perspective for understanding the physiological mechanism of its information integration and the maintenance of consciousness.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：R338

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 鄧澤懷;劉波波;李彥良;;常見的功率譜估計(jì)方法及其Matlab仿真[J];電子科技;2014年02期

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本文編號(hào)：2072887