本文選題：抑郁癥 + 功能磁共振��； 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：抑郁癥是一種伴有持續(xù)的悲傷情感和其他精神病變的一種常見精神疾病,它的發(fā)病率高,卻難以治愈,是一種潛在的死亡因素,影響著一個個家庭。盡管近年來對于抑郁癥的研究從未停歇,它的診斷卻還是沒有明朗化。神經(jīng)影像學(xué)是診斷精神疾病的一種臨床手段。通過靜息態(tài)核磁共振對人腦的掃描,得到大腦的醫(yī)學(xué)影像,進(jìn)一步分析病變部位。在此基礎(chǔ)上,大腦可以劃分為多個功能相對獨立的區(qū)域,結(jié)合復(fù)雜網(wǎng)絡(luò)的知識以這些區(qū)域作為節(jié)點構(gòu)建得到一個尺度的腦網(wǎng)絡(luò),提取腦網(wǎng)絡(luò)中的數(shù)據(jù)進(jìn)行分類可以作為醫(yī)學(xué)影像輔助診斷精神疾病的一種工具。隨著腦網(wǎng)絡(luò)技術(shù)研究的發(fā)展,有很多學(xué)者將其應(yīng)用在抑郁癥的計算機輔助診斷中。目前,基于抑郁癥的腦網(wǎng)絡(luò)數(shù)據(jù)進(jìn)行的大量分類研究多基于單一空間尺度下的腦網(wǎng)絡(luò),且使用的特征多為臨床指標(biāo)或腦網(wǎng)絡(luò)的基礎(chǔ)構(gòu)建元素。一些研究將重點放在特征選擇方法的比較和特征的選取上,以期得出輔助診斷抑郁癥的最佳方案,對空間尺度影響的研究還遠(yuǎn)遠(yuǎn)不夠。本文根據(jù)先前的研究,在前人的基礎(chǔ)上進(jìn)一步針對腦網(wǎng)絡(luò)的空間尺度對抑郁癥的分類做深入探討。本文的主要工作如下:第一,不同尺度的腦網(wǎng)絡(luò)分類比較。采集抑郁癥患者和正常被試的靜息態(tài)磁共振腦影像數(shù)據(jù),劃分腦區(qū)后進(jìn)一步得到五個不同節(jié)點個數(shù)的尺度下的腦網(wǎng)絡(luò),尺度分別為90,256,497,1003,1501。對不同尺度的腦網(wǎng)絡(luò)提取局部屬性,使用雙樣本T檢驗對兩類被試的數(shù)據(jù)做統(tǒng)計分析,選取具有統(tǒng)計顯著性的屬性作為分類特征,使用支持向量機(Support Vector Machine,SVM)分類后進(jìn)行比較。第二,分析初始分類結(jié)果的影響因素。首先,為了判斷有效特征的影響,針對不同尺度的腦網(wǎng)絡(luò),將大尺度腦網(wǎng)絡(luò)下的有效特征逐個替換為小尺度的有效特征,與大尺度腦網(wǎng)絡(luò)下的有效特征結(jié)合并進(jìn)行分類比較。其次,為了判斷特征數(shù)目的影響,分別從小尺度腦網(wǎng)絡(luò)的有效特征中抽取與大尺度有效特征數(shù)相同的特征進(jìn)行分類比較。第三,尺度是否越小越好?為判斷不同腦網(wǎng)絡(luò)尺度對于分類結(jié)果的影響,使用最大相關(guān)最小冗余(minimal Redundancy Maximal Relevance,mRMR)方法對特征與不同尺度腦網(wǎng)絡(luò)類標(biāo)簽的相關(guān)性及不同尺度腦網(wǎng)絡(luò)下特征間的冗余度進(jìn)行分析,并判斷不同尺度腦區(qū)間的距離及腦區(qū)體積對冗余度的影響。第四,結(jié)合時間復(fù)雜度及以上分析,得出某一尺度腦網(wǎng)絡(luò)下的相對最優(yōu)分類器,并得出抑郁癥患者的異常腦區(qū)。綜合考量,分類效果是隨著尺度的減小而提升的,然而,經(jīng)本文分析,尺度并非越小越好,使用1003尺度的腦網(wǎng)絡(luò)構(gòu)建分類器對抑郁癥的分類效果較佳。
[Abstract]:Depression is a common mental disease with persistent sadness and other mental disorders. Its incidence is high, but it is difficult to cure. It is a potential death factor and affects families. Although research on depression has never stopped in recent years, its diagnosis remains unclear. Neuroimaging is a clinical method for the diagnosis of mental diseases. The medical images of brain were obtained by scanning the brain by resting MRI, and the lesion location was further analyzed. On the basis of this, the brain can be divided into several regions with relatively independent functions, which can be combined with the knowledge of complex networks to construct a scale brain network using these regions as nodes. Extracting data from brain network for classification can be used as a tool of medical image aided diagnosis of mental illness. With the development of brain network technology, many researchers have applied it to computer aided diagnosis of depression. At present, a large number of classification studies based on depression brain network data are based on a single spatial scale of the brain network, and the characteristics of the use of clinical indicators or the basic elements of the brain network. Some studies focus on the comparison of feature selection methods and feature selection in order to obtain the best scheme for the diagnosis of depression. On the basis of previous studies, this paper makes a further study on the classification of depression based on the spatial scale of brain network. The main work of this paper is as follows: first, the classification of different scales of brain network comparison. The resting magnetic resonance imaging data of depression patients and normal subjects were collected and the brain networks with five different nodal numbers were obtained after dividing the brain regions. The local attributes were extracted from different scales of brain network, and the data of two kinds of subjects were statistically analyzed by double sample T test. The attributes with statistical significance were selected as classification features, and the support vector machine support Vector Machine (SVM) was used to classify and compare. Secondly, the factors influencing the initial classification results are analyzed. Firstly, in order to judge the influence of effective features, the effective features of large-scale brain networks are replaced with those of small scale ones one by one, and the effective features of large-scale brain networks are combined with the effective features of large-scale brain networks to classify and compare. Secondly, in order to judge the influence of the number of features, the features which are the same as the large scale effective features are extracted from the effective features of the small scale brain network for classification and comparison. Third, is the smaller the better? In order to judge the influence of different scales of brain network on the classification results, the correlation between features and the labels of different scales of brain networks and the redundancy of features under different scales of brain networks were analyzed by using the method of maximum redundancy minimal redundancy MRs. The influence of the distance between different scales and the volume of brain area on redundancy was evaluated. Fourthly, combining the time complexity and the above analysis, the relative optimal classifier under a certain scale brain network is obtained, and the abnormal brain regions of depression patients are obtained. Overall, the classification effect is improved with the decrease of scale. However, the smaller the scale is, the better the scale is. The classifier based on the brain network with scale 1003 is better for the classification of depression.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R749.4;TP391.41

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本文編號：2033085