【摘要】：人類的神經(jīng)系統(tǒng)是目前已知的最復(fù)雜的功能系統(tǒng),這一系統(tǒng)是基于數(shù)以百億計(jì)的基本結(jié)構(gòu)單元——神經(jīng)元相互連接組成神經(jīng)纖維網(wǎng)絡(luò)。目前,由于技術(shù)限制,對(duì)阿爾茨海默病(Alzheimer’s disease,AD)發(fā)展進(jìn)程中,神經(jīng)元形態(tài)的精細(xì)變化仍缺乏了解。大腦形態(tài)各異的神經(jīng)元相互連接形成網(wǎng)絡(luò),使全腦成為一個(gè)復(fù)雜的系統(tǒng),各部分協(xié)同工作。為了研究神經(jīng)元在病變時(shí)精細(xì)的形態(tài)變化,需要利用分辨率更高的全腦切片成像技術(shù)。通過這樣的技術(shù)我們可以研究正常和AD鼠之間神經(jīng)元形態(tài)的差異。本文借助MOST技術(shù)的高分辨率全腦成像能力,對(duì)三轉(zhuǎn)基因AD模型小鼠(3×TgAD)的全腦進(jìn)行Golgi染色,實(shí)施全腦切片和掃描,首次獲得在細(xì)胞精度層面AD小鼠的全腦圖譜,對(duì)所獲得的每只鼠高達(dá)1TB的數(shù)據(jù)圖片進(jìn)行三維重建。在三維空間中構(gòu)建并顯示神經(jīng)元的幾何分布,追蹤并提取數(shù)以百計(jì)的神經(jīng)元重建信息。通過聚類分析、樹突分析和Sholl分析,來分析和檢測(cè)AD小鼠腦部神經(jīng)元的發(fā)育及病理變化。為了獲得神經(jīng)元數(shù)據(jù),需要將神經(jīng)元形態(tài)信息從圖片像素中分離出來,目前主要采用Amira軟件實(shí)現(xiàn)半自動(dòng)的追蹤方法。但這一方法需要大量人工參與,追蹤一個(gè)神經(jīng)元至少需要半個(gè)小時(shí),而每只小鼠有超過千萬的神經(jīng)元。在目前的文獻(xiàn)報(bào)道中,已有的算法都不能有效實(shí)現(xiàn)對(duì)復(fù)雜圖像數(shù)據(jù)的神經(jīng)元追蹤。為了能夠?qū)崿F(xiàn)對(duì)復(fù)雜數(shù)據(jù)更為有效的追蹤方法,本文在OpenSnake追蹤算法的基礎(chǔ)上提出了改進(jìn)方案,并用C++語言初步實(shí)現(xiàn)了這一算法及其三維成像。從我們的結(jié)果可以看出,阿爾茨海默病對(duì)小鼠的不同腦區(qū)產(chǎn)生不同的影響,其中影響最大的是海馬CA1區(qū)。WT和AD小鼠隨年齡增長(zhǎng)神經(jīng)元的纖維豐度增加,但病變鼠的增加幅度低于正常鼠,表明AD會(huì)減緩神經(jīng)元的生長(zhǎng),同時(shí)AD會(huì)促使有活性的神經(jīng)元比例減小。我們的研究結(jié)果和方法為AD的研究提供了重要的理論和方法基礎(chǔ)。
[Abstract]:The human nervous system is the most complex functional system known at present. This system is based on tens of billions of basic structural units-neurons connected to each other to form a neural fiber network. At present, due to technical constraints, the fine changes of neuron morphology in the development of Alzheimer's disease (AD) are still lack of understanding. The neurons of different forms of the brain are connected to each other to form a network, which makes the whole brain a complex system and all parts work together. In order to study the fine morphological changes of neurons in pathological changes, a higher resolution whole brain slice imaging technique is needed. This technique allows us to study the differences in neuronal morphology between normal and AD mice. In this paper, the whole brain of three transgenic AD mice (3 脳 TgAD) was stained with Golgi staining, and the whole brain was sliced and scanned with the help of the high resolution global brain imaging ability of the most-technique. The whole brain map of AD mice at the cell precision level was obtained for the first time. Three-dimensional reconstruction was carried out on each mouse up to 1 TB of data. The geometric distribution of neurons is constructed and displayed in 3D space, and hundreds of neuronal reconstruction information are traced and extracted. Cluster analysis, dendritic analysis and Sholl analysis were used to analyze and detect the development and pathological changes of brain neurons in AD mice. In order to obtain neuron data, it is necessary to separate the morphological information from the image pixels. At present, Amira software is mainly used to realize semi-automatic tracking method. But this method requires a lot of artificial involvement, tracking a neuron for at least half an hour, and each mouse has more than 10 million neurons. In the current literature reports, the existing algorithms can not effectively realize the neuronal tracking of complex image data. In order to realize a more effective tracking method for complex data, this paper proposes an improved algorithm based on OpenSnake tracing algorithm, and realizes the algorithm and its 3D imaging with C language. From our results, we can see that Alzheimer's disease has different effects on different brain regions in mice. The most significant effect is the increase of fiber abundance of neurons in hippocampal CA1 region, WT and AD mice with age. However, the increase of AD was lower than that of normal mice, indicating that AD could slow down the growth of neurons, and AD could induce the proportion of active neurons to decrease. Our results and methods provide an important theoretical and methodological basis for AD research.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R749.16

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本文編號(hào)：2131474