【摘要】：雖然科學(xué)家們已經(jīng)探明了自閉癥患者的大腦與健康人的大腦有著明顯的不同,但是兩者之間的具體差異卻一直存在爭(zhēng)議。因?yàn)檫@個(gè)原因,醫(yī)生無(wú)法從一個(gè)客觀的視角來(lái)對(duì)自閉癥進(jìn)行診斷。事實(shí)上,自閉癥的診斷在醫(yī)學(xué)界是一個(gè)十分主觀的過(guò)程,由于診斷是以行為為導(dǎo)向,以診斷表為準(zhǔn)繩,因此目前并不能把醫(yī)學(xué)評(píng)估數(shù)據(jù)作為診斷的依據(jù)。隨著科學(xué)的進(jìn)步,一些新方法為診斷自閉癥指出了新的方向。近年來(lái),科學(xué)家們廣泛應(yīng)用靜息態(tài)功能核磁共振技術(shù)(rs-fMRI,Resting-state Functional Magnetic Resonance Imaging)探索腦部疾病。這是因?yàn)閒MRI能夠在無(wú)創(chuàng)的條件下,通過(guò)檢測(cè)血氧水平獲得更高分辨率的圖像。正因如此,使用fMRI數(shù)據(jù)構(gòu)建大腦功能網(wǎng)絡(luò),通過(guò)進(jìn)一步分析而得到被試大腦的特征無(wú)疑成為了一種十分有效的方法。腦功能網(wǎng)絡(luò)是一個(gè)復(fù)雜網(wǎng)絡(luò),因此具備了復(fù)雜網(wǎng)絡(luò)的屬性。在這些屬性中,社團(tuán)結(jié)構(gòu)占據(jù)著極其重要的地位,當(dāng)然也存在于腦網(wǎng)絡(luò)中。由于相似的節(jié)點(diǎn)處在同樣的社團(tuán)中,而相異節(jié)點(diǎn)處在不同的社團(tuán)中,因此社團(tuán)劃分也可以看作是在復(fù)雜網(wǎng)絡(luò)中尋找相似結(jié)構(gòu)的一種方法。然而,社團(tuán)劃分方法繁多,評(píng)價(jià)標(biāo)準(zhǔn)各有不同,加之社團(tuán)劃分問(wèn)題本身是一個(gè)NP-hard問(wèn)題,因此找到一種適用于腦功能網(wǎng)絡(luò)的社團(tuán)劃分方法是很困難的。深度學(xué)習(xí)是機(jī)器學(xué)習(xí)方法的一種。它通過(guò)模仿人類(lèi)的思考方式對(duì)數(shù)據(jù)進(jìn)行重新整理,以此得到事物的更高維更抽象的特征。當(dāng)前使用深度學(xué)習(xí)最引人注意和最成功的例子是語(yǔ)音識(shí)別和圖像識(shí)別。深度學(xué)習(xí)還尤其擅長(zhǎng)分類(lèi)問(wèn)題,這也使得眾多科學(xué)家把深度學(xué)習(xí)方法用于疾病診斷問(wèn)題上。然而,獲取有效的特征數(shù)據(jù)和選擇深度分類(lèi)器是兩個(gè)重要的問(wèn)題。針對(duì)以上這些問(wèn)題,本文通過(guò)設(shè)計(jì)和使用新的社團(tuán)劃分算法GAcut(Genetic Algorithm Cut)提取和分析了自閉癥和對(duì)照組的腦網(wǎng)絡(luò)社團(tuán)結(jié)構(gòu)特征。然后以此作為依據(jù),使用深度降噪自動(dòng)編碼器對(duì)自閉癥和對(duì)照組進(jìn)行區(qū)分,最終得到了較高的診斷準(zhǔn)確率。本文的主要工作如下:(1)運(yùn)用目前流行的方法對(duì)rs-fMRI數(shù)據(jù)進(jìn)行了預(yù)處理,并在此基礎(chǔ)上分別設(shè)計(jì)個(gè)體相關(guān)性矩陣和組相關(guān)性矩陣對(duì)一個(gè)被試和一組被試構(gòu)建了腦功能網(wǎng)絡(luò)。(2)為了準(zhǔn)確的劃分腦網(wǎng)絡(luò)的社團(tuán)結(jié)構(gòu),本文在遺傳算法和模塊度Q的基礎(chǔ)上設(shè)計(jì)和實(shí)現(xiàn)了算法GAcut。在真實(shí)網(wǎng)絡(luò)和fMRI數(shù)據(jù)上的實(shí)驗(yàn)表明,GAcut算法有效。(3)使用GAcut算法分別對(duì)正常被試和自閉癥被試的腦功能網(wǎng)絡(luò)進(jìn)行社團(tuán)劃分并證明了腦網(wǎng)絡(luò)具備社團(tuán)屬性,然后結(jié)合自閉癥的病理對(duì)患者的腦網(wǎng)絡(luò)社團(tuán)結(jié)構(gòu)進(jìn)行了醫(yī)學(xué)上的相關(guān)性分析,詳細(xì)描述了自閉癥患者和正常人腦網(wǎng)絡(luò)社團(tuán)結(jié)構(gòu)的不同之處和可能造成這些差異的病理原因。最后發(fā)現(xiàn)自閉癥和對(duì)照組之間的社團(tuán)結(jié)構(gòu)的具體差異可以通過(guò)標(biāo)準(zhǔn)化互信息(Normalized Mutual Information,NMI)定量的展現(xiàn)出來(lái)。(4)通過(guò)構(gòu)建NMI統(tǒng)計(jì)矩陣將所有被試的腦網(wǎng)絡(luò)社團(tuán)結(jié)構(gòu)特征濃縮到一個(gè)低維度的矩陣中,然后將其作為深度降噪自動(dòng)編碼器的輸入,從而對(duì)自閉癥和對(duì)照組進(jìn)行區(qū)分。大量對(duì)比實(shí)驗(yàn)表明,使用NMI統(tǒng)計(jì)矩陣作為深度降噪自動(dòng)編碼器的輸入,不但能夠得到更加準(zhǔn)確的診斷結(jié)果,而且時(shí)間成本也更低。
[Abstract]:Although scientists have found that the brain of a patient with autism is distinct from the brain of a healthy person, the specific difference between the two has been controversial. For this reason, doctors can't diagnose autism from an objective perspective. In fact, the diagnosis of autism is a very subjective process in the medical field, because the diagnosis is guided by behavior, and the diagnosis table is the quasi-rope, so the medical evaluation data cannot be used as the basis for diagnosis. With the progress of science, some new approaches have identified new directions for the diagnosis of autism. In recent years, scientists have been widely used in the exploration of brain diseases by resting-state magnetic resonance (rs-fMRI). This is because fMRI is able to obtain higher resolution images by detecting blood oxygen levels without invasive conditions. For this reason, the use of fMRI data to construct the brain function network is a very effective way to get the characteristics of the brain to be tested by further analysis. The brain function network is a complex network, so it has the properties of complex networks. In these attributes, the community structure plays a very important role, of course in the brain network. Because similar nodes are in the same community, and different nodes are in different communities, the division of associations can also be seen as a method of finding similar structures in a complex network. However, it is difficult to find a kind of community division method which is suitable for the brain function network, because the division method of the community is different, the evaluation standard is different, and the problem of the division of the community itself is an NP-hard problem. Depth learning is one of the methods of machine learning. It makes the data refresh by simulating the human thinking mode, so as to obtain the higher-dimensional and more abstract features of the object. The most interesting and successful examples of current use of depth learning are speech recognition and image recognition. Deep learning is also particularly good at the classification problem, which also makes it possible for many scientists to use depth-learning methods in the diagnosis of disease. However, the acquisition of valid feature data and the selection of a depth classifier is two important issues. In view of the above problems, this paper extracts and analyzes the structural features of the brain network community in the autistic and control group by the design and use of the new community-based algorithm GAcut (Genetic Algorithm Cut). and then using the depth noise reduction automatic coder to distinguish the autism and the control group as the basis, and finally the high diagnosis accuracy rate is obtained. The main work of this paper is as follows: (1) Using the current method to pre-process the rs-fMRI data, and on this basis, the individual correlation matrix and the group correlation matrix are designed to construct the brain function network. (2) In order to accurately classify the community structure of the brain network, this paper designs and implements the algorithm GAut based on the genetic algorithm and the module degree Q. The experiments on real network and fMRI data show that the GAut algorithm is effective. (3) The brain function network of the normal and autistic people is divided by the GAcut algorithm, and the association property of the brain network is proved, and then the association analysis of the brain network community structure of the patient is carried out in combination with the pathology of the autism. The differences in the structure of the autistic and normal human brain networks and the possible causes of these differences are described in detail. Finally, it is found that the specific difference of the community structure between the autism and the control group can be quantified by the standardized mutual information (NMI). and (4) concentrating all the tested brain network community structure features into a low-dimension matrix by constructing the NMI statistical matrix, and then using the NMI statistical matrix as the input of the depth noise reduction automatic encoder, thereby distinguishing the autism and the control group. A large number of comparison experiments show that the NMI statistical matrix is used as the input of the depth noise reduction automatic encoder, so that the accurate diagnosis result can be obtained, and the time cost is lower.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：R749;O157.5

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本文編號(hào)：2333694