基于腦網(wǎng)絡(luò)社團結(jié)構(gòu)和深度學習的自閉癥診斷研究
發(fā)布時間：2018-11-15 15:32
【摘要】：雖然科學家們已經(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議。因為這個原因,醫(yī)生無法從一個客觀的視角來對自閉癥進行診斷。事實上,自閉癥的診斷在醫(yī)學界是一個十分主觀的過程,由于診斷是以行為為導向,以診斷表為準繩,因此目前并不能把醫(yī)學評估數(shù)據(jù)作為診斷的依據(jù)。隨著科學的進步,一些新方法為診斷自閉癥指出了新的方向。近年來,科學家們廣泛應用靜息態(tài)功能核磁共振技術(shù)(rs-fMRI,Resting-state Functional Magnetic Resonance Imaging)探索腦部疾病。這是因為fMRI能夠在無創(chuàng)的條件下,通過檢測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),通過進一步分析而得到被試大腦的特征無疑成為了一種十分有效的方法。腦功能網(wǎng)絡(luò)是一個復雜網(wǎng)絡(luò),因此具備了復雜網(wǎng)絡(luò)的屬性。在這些屬性中,社團結(jié)構(gòu)占據(jù)著極其重要的地位,當然也存在于腦網(wǎng)絡(luò)中。由于相似的節(jié)點處在同樣的社團中,而相異節(jié)點處在不同的社團中,因此社團劃分也可以看作是在復雜網(wǎng)絡(luò)中尋找相似結(jié)構(gòu)的一種方法。然而,社團劃分方法繁多,評價標準各有不同,加之社團劃分問題本身是一個NP-hard問題,因此找到一種適用于腦功能網(wǎng)絡(luò)的社團劃分方法是很困難的。深度學習是機器學習方法的一種。它通過模仿人類的思考方式對數(shù)據(jù)進行重新整理,以此得到事物的更高維更抽象的特征。當前使用深度學習最引人注意和最成功的例子是語音識別和圖像識別。深度學習還尤其擅長分類問題,這也使得眾多科學家把深度學習方法用于疾病診斷問題上。然而,獲取有效的特征數(shù)據(jù)和選擇深度分類器是兩個重要的問題。針對以上這些問題,本文通過設(shè)計和使用新的社團劃分算法GAcut(Genetic Algorithm Cut)提取和分析了自閉癥和對照組的腦網(wǎng)絡(luò)社團結(jié)構(gòu)特征。然后以此作為依據(jù),使用深度降噪自動編碼器對自閉癥和對照組進行區(qū)分,最終得到了較高的診斷準確率。本文的主要工作如下:(1)運用目前流行的方法對rs-fMRI數(shù)據(jù)進行了預處理,并在此基礎(chǔ)上分別設(shè)計個體相關(guān)性矩陣和組相關(guān)性矩陣對一個被試和一組被試構(gòu)建了腦功能網(wǎng)絡(luò)。(2)為了準確的劃分腦網(wǎng)絡(luò)的社團結(jié)構(gòu),本文在遺傳算法和模塊度Q的基礎(chǔ)上設(shè)計和實現(xiàn)了算法GAcut。在真實網(wǎng)絡(luò)和fMRI數(shù)據(jù)上的實驗表明,GAcut算法有效。(3)使用GAcut算法分別對正常被試和自閉癥被試的腦功能網(wǎng)絡(luò)進行社團劃分并證明了腦網(wǎng)絡(luò)具備社團屬性,然后結(jié)合自閉癥的病理對患者的腦網(wǎng)絡(luò)社團結(jié)構(gòu)進行了醫(yī)學上的相關(guān)性分析,詳細描述了自閉癥患者和正常人腦網(wǎng)絡(luò)社團結(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)自閉癥和對照組之間的社團結(jié)構(gòu)的具體差異可以通過標準化互信息(Normalized Mutual Information,NMI)定量的展現(xiàn)出來。(4)通過構(gòu)建NMI統(tǒng)計矩陣將所有被試的腦網(wǎng)絡(luò)社團結(jié)構(gòu)特征濃縮到一個低維度的矩陣中,然后將其作為深度降噪自動編碼器的輸入,從而對自閉癥和對照組進行區(qū)分。大量對比實驗表明,使用NMI統(tǒng)計矩陣作為深度降噪自動編碼器的輸入,不但能夠得到更加準確的診斷結(jié)果,而且時間成本也更低。
[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.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R749;O157.5
本文編號：2333694
[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.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R749;O157.5
【參考文獻】
相關(guān)期刊論文 前3條
1 鄒小兵;;兒童孤獨癥診斷與治療新動向[J];中國兒童保健雜志;2012年04期
2 鄧賜平,劉明;自閉癥診斷理論的進展與困頓[J];中國特殊教育;2005年03期
3 鄧賜平,劉明;自閉癥的認知神經(jīng)發(fā)展研究:回顧與展望[J];華東師范大學學報(教育科學版);2004年03期
，本文編號：2333694
本文鏈接：http://www.sikaile.net/kejilunwen/yysx/2333694.html
最近更新
教材專著
