本文選題：彌散張量核磁共振圖像 + 阿爾茲海默病　； 參考：《西安電子科技大學(xué)》2015年碩士論文

【摘要】：阿爾茲海默病(Alzheimer’s Disease,AD)被認(rèn)為是老年人中最常見的癡呆型疾病。AD的發(fā)病率很高,但該疾病的初期發(fā)展緩慢,往往不易察覺。這種疾病初期表現(xiàn)為記憶力衰退、交流障礙等,后期患者將逐漸喪失活動和記憶能力,直至死亡。AD給患者家屬和整個社會帶來非常沉重的經(jīng)濟(jì)負(fù)擔(dān)。輕度認(rèn)知障礙(mild cognitive impairment,MCI)表現(xiàn)為一定程度的認(rèn)知障礙、記憶功能明顯出現(xiàn)衰退,常常被認(rèn)為是介于正常人和癡呆狀態(tài)之間的一種病態(tài),雖然該病的危害程度不及AD,但隨著疾病的發(fā)展,會有一定比例的MCI患者轉(zhuǎn)為AD。近幾十來,研究人員對這類疾病進(jìn)行了深入的研究,取得了很多重要的研究成果,但該病的病理機(jī)制仍然不能被完全解釋,因此需要進(jìn)一步的研究。隨著核磁共振影像技術(shù)的發(fā)展,通過大腦功能和白質(zhì)纖維的變化來研究疾病,為研究AD的發(fā)生、發(fā)展過程提供了新的思路。近年來,有關(guān)白質(zhì)纖維結(jié)構(gòu)改變的研究大多基于感興趣區(qū)域和現(xiàn)有網(wǎng)絡(luò)模型(如小世界網(wǎng)絡(luò)和無標(biāo)度網(wǎng)絡(luò))的一些指標(biāo)的改變上。但由于AD病變的位置遍布全腦,因此基于感興趣區(qū)域的研究并不能很好地反映疾病發(fā)展的過程,同時已有的網(wǎng)絡(luò)結(jié)構(gòu)模型是在現(xiàn)實世界網(wǎng)絡(luò)中抽象出來的,雖然人腦網(wǎng)絡(luò)在一定程度上滿足這些網(wǎng)絡(luò)模型的特征,但是,在具體疾病的表征上卻不能得到一致性的結(jié)論。大量的研究資料表明,AD患者的大腦網(wǎng)絡(luò)中心節(jié)點(hubs)存在明顯的失連接現(xiàn)象。因此我們提出假設(shè),用基于hubs構(gòu)建成的rich-club模型,可以更好的描述AD、MCI病人與正常人腦結(jié)構(gòu)的差異。本文主要探討了作者在這方面的研究成果。其次,我們利用聚類分析方法對功能核磁數(shù)據(jù)進(jìn)一步處理,在提高功能網(wǎng)絡(luò)性噪比方面進(jìn)行了有益的探索研究。全文主要的研究結(jié)果和創(chuàng)新點可概括為以下三個部分:第一部分:研究AD和MCI在rich-club分級網(wǎng)絡(luò)中大腦結(jié)構(gòu)的改變。主要基于結(jié)構(gòu)核磁共振圖像和彌散張量圖像,通過標(biāo)準(zhǔn)模板將人大腦皮層分為90個腦區(qū)。經(jīng)過圖像分割、圖像配準(zhǔn)、纖維束追蹤的方法,構(gòu)建出由90個節(jié)點組成的人腦白質(zhì)纖維連接網(wǎng)絡(luò)。通過計算該連接網(wǎng)絡(luò)的rich-club系數(shù),證明三組被試(AD、MCI和正常對照組(NC))滿足rich-club網(wǎng)絡(luò)模型,接著在該等級模型上,統(tǒng)計分析每組被試在不同等級網(wǎng)絡(luò)連接強(qiáng)度的改變。結(jié)果發(fā)現(xiàn)AD病人的失連接主要發(fā)生在第二級和第三級網(wǎng)絡(luò),而MCI的失連接主要發(fā)生在第三級網(wǎng)絡(luò),而且這兩組被試失連接的程度與被試表現(xiàn)出的認(rèn)知障礙程度呈正相關(guān)。這樣的結(jié)果在某種程度上也說明了AD患者比MCI病情更為嚴(yán)重。第二部分:研究AD的生物學(xué)標(biāo)記。第一部分的研究成果表明,AD患者大腦一些重要節(jié)點網(wǎng)絡(luò)連接發(fā)生了明顯改變。我們推測通過計算構(gòu)成rich-club節(jié)點的一些特征量,可以找到一些AD的特征性生物標(biāo)記。我們發(fā)現(xiàn),用左側(cè)殼核的介數(shù)中心度、左側(cè)殼核與左側(cè)額下回的連接強(qiáng)度做為網(wǎng)絡(luò)特征,可以以89.09%的準(zhǔn)確度(基于SVM的平均敏感度)將AD病人和正常人區(qū)分開。第三部分:利用聚類分析方法在提高功能網(wǎng)絡(luò)性噪比方面進(jìn)行探索研究。主要包括了計算模型的基本假設(shè),數(shù)據(jù)處理以及對模型的驗證計算。雖然研究結(jié)果不盡如人意,但研究思路具有一定的合理性和啟發(fā)性,進(jìn)一步的工作有待進(jìn)行。
[Abstract]:Alzheimer's disease (Alzheimer 's Disease, AD) is considered to be the most common type of dementia onset.AD disease in the elderly rate is very high, but the early development of the disease is slow, often difficult to detect the disease. The early manifestations of memory loss, communication disorders, late patients will gradually lose activity and memory ability until death,.AD to patients' families and the society brought very heavy economic burden. Mild cognitive impairment (mild cognitive, impairment, MCI) showed certain cognitive impairment, memory function decline, is often considered a morbid state between normal people and dementia, although the disease is less than the degree of harm AD, but with the development of the disease, there will be a certain percentage of MCI were converted to AD. in recent decades, researchers have conducted in-depth research on this kind of disease, has made many important achievements, but The pathological mechanism of this disease still cannot be fully explained, so the need for further research. With the development of nuclear magnetic resonance imaging technology, the brain function and white matter fiber changes to study diseases, to study the occurrence of AD, provides a new way of development. In recent years, the research about model changes of white matter fiber structure most of the existing network and based on region of interest (such as small world networks and scale-free networks) some of the index changes. But due to the AD lesion location throughout the brain, so the study of region of interest can not well reflect the development of the disease process and based on the existing network structure model is in reality world network abstracted, although the human brain network to meet the characteristics of the network model to a certain extent, but cannot get consistent conclusions in the characterization of specific diseases. A large number of research funding The material shows that the brain network center node with AD (hubs) has obvious loss of connection phenomenon. So we propose the hypothesis, using the rich-club model constructed based on hubs, can better describe the differences in AD, MCI patients and normal brain structure. This paper mainly discusses the author in this area of research. Secondly, we using the cluster analysis method further processing of functional magnetic data, improve the function of network in noise ratio was explored. The main research results and innovations can be summarized as the following three parts: the first part: the research of AD and MCI in brain rich-club hierarchical network structure is mainly based on nuclear magnetic resonance structure change. Images and diffusion tensor images, the standard template will be divided into 90 human cortical regions of the brain. After image segmentation, image registration, fiber tracking method, constructed by the 90 node group The human brain white matter fiber connected to the network. By calculating the rich-club coefficient of the network connection, that three groups of subjects (AD, MCI and normal control group (NC)) to meet the rich-club network model, and then the grade model, statistical analysis of each group of subjects in different levels of network connection strength changes. The results show that the connection occurs mainly in the second level and third level network of AD patients, and MCI connectivity occurred mainly in the third level network, and the two groups of subjects lost connection degree and the participants showed the degree of cognitive impairment was positively correlated. This results in a certain extent also shows that AD patients were more severe than the condition of MCI. The second part: Study on biological markers of AD. The first part of the research results show that the AD brains of some important nodes of network connection has changed obviously. We speculate that through the calculation of some characteristics of rich-club node structure The amount, you can find the feature of some biomarkers of AD. We found that with the left putamen betweenness centrality, the connection strength of the left inferior frontal gyrus and left putamen as characteristic of the network, with 89.09% accuracy (based on the average sensitivity of SVM) separated from AD patients and normal people. The third part: by using cluster analysis method in the study of improving ratio function of network noise. Including the basic assumptions of the model, data processing and verification of the model calculations. Although the results are not satisfactory, but the research has certain rationality and enlightening, further work to be done.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R749.16

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