本文選題：阿爾茨海默病　切入點：輕度認知障礙　出處：《太原理工大學》2017年碩士論文

【摘要】：阿爾茨海默病(Alzheimer’s Disease,AD)又稱原發(fā)性老年癡呆病,以嚴重的高級認知功能和記憶功能障礙為臨床表現(xiàn)。輕度認知障礙(Mild Cognitive Impairment,MCI)是AD與正常人間的一種過渡狀態(tài)。該類疾病潛伏期長不易被發(fā)現(xiàn),且病變嚴重,損害不可逆。我國已逐漸進入老齡化社會,防治AD成為一項嚴峻的工作。如能在更早的時間發(fā)現(xiàn)疾病的征兆,并及時進行醫(yī)療干預,可以降低病人的痛苦,節(jié)省治療的開支,因此發(fā)現(xiàn)AD早期異常特征是AD輔助診斷系統(tǒng)的研究熱點之一。目前,基于磁共振影像的AD輔助診斷雖已取得了一些成果,但使用的主要是結構特征,而一旦結構發(fā)生變化,患者已進入AD晚期,進入了不可逆階段。如能利用認知功能異常作為輔助診斷的依據(jù),就可以做到早發(fā)現(xiàn)、早干預。因此AD早期診斷的關鍵是找到AD認知功能的異常指標。大腦偏側化現(xiàn)象是指大腦的左右半球存在結構和功能上的不對稱性,本研究借助腦功能網(wǎng)絡研究方法,以圖論理論為基礎,研究AD的偏側化現(xiàn)象,并將其用于AD的輔助診斷中,提高AD早期診斷的分類準確度。本文主要研究工作如下:(1)不同于傳統(tǒng)的腦功能網(wǎng)絡研究過程,本研究首先制作可用于偏側化研究的腦膜板,接著構建半球功能腦網(wǎng)絡,計算網(wǎng)絡連接強度與拓撲屬性,并計算偏側化指數(shù)。(2)利用統(tǒng)計分析的方法,篩選可用于AD輔助診斷的特征,并對篩選的特征進行生理意義的解釋。(3)根據(jù)篩選的特征,構造特征空間,使用SVM(support vector machine)分類器訓練分類模型,采用留一驗證法測試分類模型。(4)采用ADNI數(shù)據(jù)集,驗證本文提出的方法,結果表明,AD患者確實存在偏側化異常現(xiàn)象,篩選出的偏側化特征與其他研究方法得到較為一致的結論。加入偏側化特征后的分類準確率為85.71%,敏感度為87.06%,特異度為84.34%。本文對比了使用功能連接、功能連接及其偏側化指數(shù),網(wǎng)絡屬性、網(wǎng)絡屬性及其偏側化指數(shù)的分類結果,結合已有文獻,得出了偏側化指數(shù)的加入對于AD的分類準確率有提高作用,尤其對于輕度認識障礙與正常對照組的準確率提升作用明顯。說明偏側化指數(shù)確實能提高AD輔助診斷準確率,對AD早期輔助診斷的研究提供了一定的依據(jù)。
[Abstract]:Alzheimer's disease (Alzheimer's disease), also known as primary Alzheimer's disease (AD), is characterized by severe advanced cognitive and memory impairment.Mild cognitive impairment (mild Cognitive Impairment MCI) is a transitional state between AD and normal subjects.The long incubation period of the disease is not easy to be found, and the lesion is serious and irreversible.China has gradually entered an aging society, prevention and treatment of AD has become a serious task.If we can find the symptoms of the disease earlier and carry out medical intervention in time, we can reduce the suffering of patients and save the cost of treatment. Therefore, the discovery of early abnormal features of AD is one of the hot topics in AD auxiliary diagnosis system.At present, although some achievements have been made in AD aided diagnosis based on magnetic resonance imaging, the main structural features are used, and once the structure changes, the patients have entered the late stage of AD and have entered the irreversible stage.If cognitive dysfunction can be used as the basis of auxiliary diagnosis, early detection and early intervention can be achieved.Therefore, the key to early diagnosis of AD is to find abnormal indicators of AD cognitive function.The phenomenon of cerebral lateralization refers to the asymmetry in the structure and function of the left and right hemispheres of the brain. This study studied the lateralization of AD on the basis of graph theory and applied it to the auxiliary diagnosis of AD.To improve the classification accuracy of early diagnosis of AD.The main work of this paper is as follows: (1) different from the traditional research process of brain functional network, the meningeal plate which can be used for lateralization is first made, and then the hemispherical functional brain network is constructed to calculate the network connection strength and topological properties.Using statistical analysis method to screen the features that can be used for AD diagnosis, and to interpret the physiological meaning of the selected features. The feature space is constructed according to the characteristics of the screening.The classification model was trained by SVM(support vector machine, and the classification model was tested by a residual verification method. The ADNI data set was used to verify the method proposed in this paper. The results show that the abnormal phenomenon of partial lateralization does exist in AD patients.The selected characteristics of lateralization are in good agreement with other research methods.The classification accuracy, sensitivity and specificity were 85.71, 87.06 and 84.34, respectively.This paper compares the classification results of using functional connection, functional connection and its lateralization index, network attribute, network attribute and partial lateralization index.It is concluded that the addition of the hemilateralization index can improve the classification accuracy of AD, especially for the mild cognitive impairment and the normal control group.The results show that the hemipartialization index can improve the accuracy of AD auxiliary diagnosis and provide some basis for the study of AD early auxiliary diagnosis.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R749.16;O157.5

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本文編號：1721746