本文關(guān)鍵詞：基于磁共振成像的大腦功能網(wǎng)絡(luò)動態(tài)特性研究　出處：《西北工業(yè)大學(xué)》2015年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 動態(tài)功能連接 腦網(wǎng)絡(luò) 靜息態(tài)網(wǎng)絡(luò)(RSN) 字典學(xué)習(xí) 多視圖譜聚類 大腦亞穩(wěn)態(tài)

【摘要】：腦科學(xué)研究是當(dāng)今世界重點研究領(lǐng)域之一。在對大腦的復(fù)雜結(jié)構(gòu)和功能進(jìn)行研究的過程中,磁共振成像(magnetic resonance imaging,MRI)技術(shù)發(fā)揮了重要的作用,比如:通過彌散張量成像(diffusion tensor imaging,DTI)技術(shù)可以在活體無創(chuàng)研究大腦的白質(zhì)神經(jīng)纖維結(jié)構(gòu),利用功能磁共振成像(functional MRI,fMRI)技術(shù)可以對腦區(qū)功能進(jìn)行分析。基于磁共振成像對大腦進(jìn)行腦網(wǎng)絡(luò)研究及功能連接分析是腦科學(xué)研究的一個重要方面。大腦在感知世界的過程中經(jīng)歷著信息感知、傳遞、協(xié)調(diào)、變換、存儲以及新信息生成等一系列信息處理過程,大腦的功能活動狀態(tài)隨之發(fā)生相應(yīng)的變化,然而,在這一動態(tài)變化過程中,大腦是否存在暫時穩(wěn)定的亞穩(wěn)態(tài)狀態(tài)?如何準(zhǔn)確地描述與表達(dá)大腦的亞穩(wěn)態(tài)狀態(tài)?在大腦完成認(rèn)知任務(wù)的過程中,腦區(qū)之間的功能信息交互是如何動態(tài)變化的?這些問題的研究對于理解大腦的功能認(rèn)知具有重要的意義。針對這些熱點問題,本文利用大腦的DTI和fMRI圖像,基于大腦的全腦功能連接網(wǎng)絡(luò),從以下三個方面對大腦功能狀態(tài)動態(tài)性進(jìn)行了研究:1)動態(tài)大腦通用狀態(tài)模式表達(dá);2)大腦靜息態(tài)網(wǎng)絡(luò)(resting state networks,RSN)動態(tài)性研究;3)大腦動態(tài)信息傳遞路徑追蹤。主要研究內(nèi)容和創(chuàng)新點包括:(1)引入一套高精確度、個體一致對應(yīng)的大規(guī)模全腦網(wǎng)絡(luò)參考系統(tǒng),即DICCCOL(Dense Individualized and Common Connectivity-based Cortical Landmarks)網(wǎng)絡(luò)。利用大腦DTI圖像得到白質(zhì)神經(jīng)纖維結(jié)構(gòu)信息,通過數(shù)學(xué)模型表達(dá)和量化比較,在全局范圍內(nèi)尋求群體結(jié)構(gòu)差異最小的神經(jīng)纖維束所對應(yīng)的一組節(jié)點作為不同個體上同一DICCCOL腦區(qū)標(biāo)記,最后共得到358個群體一致的全腦腦區(qū)標(biāo)記節(jié)點。這些節(jié)點在個體和群體上都具有很高的結(jié)構(gòu)和功能的對應(yīng)性、可重復(fù)性和可預(yù)測性,能有效地表示大腦的共有結(jié)構(gòu)連接模式和主要的腦功能區(qū),有助于我們對不同個體或群體進(jìn)行大腦結(jié)構(gòu)和功能的分析與比較。(2)基于DICCCOL全腦動態(tài)功能連接,發(fā)現(xiàn)并提出了一種動態(tài)大腦亞穩(wěn)態(tài)狀態(tài)表達(dá)形式。通過將fMRI圖像映射到相應(yīng)DTI空間,得到DICCCOL各節(jié)點的fMRI信號。采用滑動時間窗口分析方法,計算各節(jié)點間隨時間變化的三維動態(tài)功能連接強(qiáng)度,進(jìn)一步統(tǒng)計節(jié)點的連接強(qiáng)度得到二維節(jié)點動態(tài)功能連接強(qiáng)度矩陣。觀察發(fā)現(xiàn),該矩陣在一些短的時間段內(nèi)呈現(xiàn)出一定的穩(wěn)定性,即大腦的亞穩(wěn)態(tài)狀態(tài)。則大腦的動態(tài)狀態(tài)可以用一系列基于DICCCOL網(wǎng)絡(luò)的亞穩(wěn)態(tài)全腦功能連接矩陣來表示,為后續(xù)研究大腦的動態(tài)信息處理過程及狀態(tài)變化等打下基礎(chǔ)。(3)基于大腦亞穩(wěn)態(tài)狀態(tài)樣本的稀疏表達(dá)與分類,提出了一種動態(tài)大腦通用狀態(tài)模式空間描述方法。將個體大腦的亞穩(wěn)態(tài)狀態(tài),用全腦近似穩(wěn)定連接模式(whole-brain quasi-stable connectome pattern,WQCP)樣本表示后,將群體內(nèi)所有WQCP樣本組合在一起,采用一種基于Fisher判別準(zhǔn)則和稀疏表達(dá)的字典學(xué)習(xí)算法,即FDDL(Fisher discriminative dictionary learning)方法對樣本進(jìn)行學(xué)習(xí)和分類,最后將大腦的動態(tài)狀態(tài)變化用若干通用狀態(tài)模式來表示,即動態(tài)大腦通用狀態(tài)模式空間。通過對靜息態(tài)和任務(wù)態(tài)大腦WQCP樣本進(jìn)行統(tǒng)一表達(dá),發(fā)現(xiàn)二者具有不同的動態(tài)通用狀態(tài)變化模式。任務(wù)態(tài)fMRI數(shù)據(jù)群組激活檢測實驗結(jié)果表明,任務(wù)掃描過程中出現(xiàn)靜息態(tài)模式的個體沒有很好的遵從實驗設(shè)計的任務(wù)范式,這為大腦f MRI圖像質(zhì)量控制提供了一種有效的輔助手段。(4)基于靜息態(tài)大腦的動態(tài)通用狀態(tài)模式,對RSN網(wǎng)絡(luò)的動態(tài)性進(jìn)行了研究。將靜息態(tài)大腦的每個動態(tài)通用狀態(tài)模式看作是全腦功能連接狀態(tài)的一種單獨的視圖,采用多視圖譜聚類方法對DICCCOL節(jié)點進(jìn)行聚類,得到一系列包含了豐富的動態(tài)信息的DICCCOL子集,即動態(tài)RSN網(wǎng)絡(luò),與同樣由DICCCOL節(jié)點表達(dá)的靜態(tài)RSN網(wǎng)絡(luò)進(jìn)行比較。結(jié)果發(fā)現(xiàn),一些RSN網(wǎng)絡(luò)在靜息態(tài)下呈現(xiàn)出很好的穩(wěn)定性,比如缺省模式網(wǎng)絡(luò)(default mode network,DMN),視覺RSN網(wǎng)絡(luò)等,另外一些RSN網(wǎng)絡(luò)包括運動相關(guān)網(wǎng)絡(luò)則呈現(xiàn)出強(qiáng)動態(tài)性和變化性,說明這些動態(tài)性強(qiáng)的網(wǎng)絡(luò)對靜息態(tài)大腦功能區(qū)域的動態(tài)交互起關(guān)鍵作用。該研究為RSN網(wǎng)絡(luò)的研究以及研究靜息態(tài)大腦功能信息處理機(jī)制提供了一種新的視角。(5)基于大腦亞穩(wěn)態(tài)狀態(tài)時間序列及動態(tài)通用狀態(tài)模式表達(dá)形式,對各亞穩(wěn)態(tài)下所隱含的大腦動態(tài)信息傳遞路徑進(jìn)行了追蹤研究,即認(rèn)為大腦亞穩(wěn)態(tài)狀態(tài)的形成是由于不同腦區(qū)之間的動態(tài)信息交互造成的。首先,根據(jù)亞穩(wěn)態(tài)時間序列信號,將DICCCOL節(jié)點聚類到不同的空間子網(wǎng)絡(luò),對各子網(wǎng)絡(luò)的平均信號進(jìn)行擬合并檢測其峰值激活時刻,根據(jù)激活時刻的先后對子網(wǎng)絡(luò)進(jìn)行排序;然后,建立信息傳遞概率模型,采用動態(tài)規(guī)劃的方法求解最大概率路徑,即最優(yōu)信息傳遞路徑,由每個子網(wǎng)絡(luò)中的關(guān)鍵“路由”節(jié)點來表達(dá)。通過對視覺任務(wù)下正常青少年組和患有創(chuàng)傷后應(yīng)激障礙疾病(post-traumatic stress disorder,PTSD)青少年組比較,發(fā)現(xiàn)兩組群體大腦在該視覺任務(wù)中的高頻率“路由”節(jié)點分布具有明顯差異,正常組在視覺皮層區(qū)域呈大面積高頻率分布,而PTSD組則相對弱許多,此外,PTSD組的功能活動涉及更多的大腦皮層區(qū)域。該研究對于了解PTSD疾病大腦的功能信息處理機(jī)制具有參考價值。
[Abstract]:The research of brain science is one of the most important research fields in the world. In the process of complex structure and function of the brain, magnetic resonance imaging (magnetic resonance imaging MRI) technology has played an important role, for example: by using diffusion tensor imaging (diffusion tensor, imaging, DTI) technology can be no white matter nerve fiber structure and study of the brain in vivo, using functional magnetic resonance imaging imaging (functional MRI fMRI) technology on brain function analysis. The brain network research and functional connection analysis based on magnetic resonance imaging (MRI) are an important aspect of brain science. The brain through perception, transmission, coordination, transformation, storage and generation of new information and a series of information processing in the process of perceiving the world, functional state of the brain change accordingly, however, in the process of dynamic change in the brain, the existence of metastable state temporarily stable metastable state? How to accurately describe and express the brain? The process of cognitive tasks in the brain, brain function and information interaction zone between how dynamic changes? The study of these problems for understanding the brain's cognitive function has important significance. Aiming at these issues, this paper use the brain DTI and fMRI images, the whole brain functional brain network connection based on studied the dynamic state of brain function from the following three aspects: 1) the expression of general dynamic brain state model; 2) brain resting state network (resting state networks, RSN) dynamic research; 3) tracking brain dynamic information transmission path. The main research contents and innovations include: (1) introduce a set of high-precision, individual correspondence corresponding large-scale large-scale brain network reference system, namely DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) network. Get the structure information of white matter nerve fibers by brain DTI images, through the mathematical model expression and quantitative comparison, in the global scope for different population structures a minimal set of nodes corresponding to the nerve fiber bundle as a DICCCOL marker on different individual brain regions, finally to mark the whole brain area of 358 nodes in group consensus the. These nodes have the structure and function of high in individuals and groups on the correspondence, repeatability and predictability, can effectively represent the brain's total structure connection mode and main functional areas, help us to different individuals or groups of brain structure and function and comparison. (2) based on the dynamic functional connection of DICCCOL whole brain, a dynamic state of brain metastable state was found and proposed. By mapping the fMRI image to the corresponding DTI space, the fMRI signal of each node of the DICCCOL is obtained. The sliding time window analysis method is used to calculate the three dimensional dynamic functional connection strength between nodes, and further calculate the connection strength of nodes to get two-dimensional node dynamic functional connectivity strength matrix. It is found that the matrix shows a certain stability in some short periods of time, that is, the metastable state of the brain. The dynamic state of the brain can be represented by a series of metastable whole brain functional connectivity matrices based on DICCCOL network, which lays the foundation for subsequent research on the dynamic information processing process and state changes of the brain. (3) based on the sparse representation and classification of brain metastable state samples, a dynamic general state pattern spatial description method is proposed. The individual brain metastable state, connection mode in whole brain (whole-brain quasi-stable connectome pattern approximate stability, WQCP) said after the group within the sample, all WQCP samples together, using a Fisher discriminant criterion and the expression of sparse dictionary learning algorithm based on FDDL (Fisher discriminative dictionary learning) learning and classification method of the sample, the dynamic state changes of the brain with a number of general state model to represent the dynamic state of brain, general pattern space. Through the unified expression of the resting state and the task state brain WQCP samples, it is found that the two models have different dynamic general state change patterns. Experimental results of task group fMRI data group activation test show that individuals with resting state patterns in task scanning process do not follow the task paradigm of experimental design well, which provides an effective auxiliary method for F MRI image quality control. (4) based on the dynamic general state pattern of the resting state of the brain, the dynamics of the RSN network is studied. The general state of each dynamic mode of resting state brain as a single view of the whole brain functional connectivity state, is used to cluster the DICCCOL node multi spectral clustering method, get a series of dynamic information contains rich DICCCOL subset, namely dynamic RSN network, compared with the static RSN network also expressed by DICCCOL node. The results showed that some RSN network shows a good stability in the resting state, such as the default mode network (default mode network, DMN, RSN) visual network, some RSN network including network sports related showed a strong dynamic and variable, the dynamic network strong state interaction plays a key role the dynamic of resting state brain function area. The research provides a new perspective for the research of RSN network and the study of the resting state brain function information processing mechanism. (5) the brain metastable state of time series and general dynamic state model expression based on brain dynamic information transmission path implied on the metastable studied, that the formation of metastable state of the brain is due to dynamic information between different regions of the brain caused by interaction. First, according to the metastable time series signal, the DICCCOL nodes are clustered into different space subnetworks, and the average signals of each subnetwork are combined to detect its peak.
【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：O482.532;R338
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本文編號：1345095