抑郁癥腦網(wǎng)絡(luò)異常拓?fù)鋵傩苑诸愌芯?/H1>
發(fā)布時間：2018-03-28 10:26
本文選題：腦網(wǎng)絡(luò) 切入點：功能磁共振成像 出處：《太原理工大學(xué)》2013年碩士論文
【摘要】：抑郁癥是在生理、心理以及社會環(huán)境等因素影響下,人的大腦功能失調(diào),導(dǎo)致認(rèn)知、情感、意志等精神活動出現(xiàn)不同程度障礙的疾病。 目前抑郁癥的診斷,多是根據(jù)病人所表現(xiàn)的外在癥狀來診斷病人是否患病。腦網(wǎng)絡(luò)作為一種廣泛應(yīng)用于識別異常拓?fù)鋵傩缘墓ぞ?為抑郁癥的診斷提供了一個新的視角。然而,靜息狀態(tài)腦功能網(wǎng)絡(luò)的拓?fù)鋵傩阅芊駪?yīng)用于抑郁癥的分類仍然未知。進(jìn)一步的研究需要找到最合適的特征選擇方法,建立分類模型,為抑郁癥患者的計算機輔助診斷提供支持。主要工作如下： 本次研究中,我們采集了38例未嘗試任何藥物的首發(fā)抑郁癥患者和28例正常對照組的靜息狀態(tài)功能磁共振成像數(shù)據(jù),構(gòu)建靜息狀態(tài)腦功能網(wǎng)絡(luò)。 利用圖論的方法計算腦網(wǎng)絡(luò)的聚合系數(shù),最短路徑長度,節(jié)點的度,節(jié)點中間中心度和節(jié)點效率,把這些典型的腦網(wǎng)絡(luò)拓?fù)鋵傩宰鳛榉诸愄卣?使用特征選擇的方法對其進(jìn)行篩選。 用五種不同的分類算法構(gòu)建分類器,分類器包括支持向量機,神經(jīng)網(wǎng)絡(luò)和決策樹等。用分類特征在統(tǒng)計意義上的重要性作為閾值來劃分特征,并對包含不等特征數(shù)量的分類器的性能進(jìn)行評估。 實驗得到在28個特征(P0.05)下,基于徑向基函數(shù)的支持向量機算法和神經(jīng)網(wǎng)絡(luò)算法得到最高平均預(yù)測率(分別為84.36%和80.70%)。結(jié)果表明抑郁癥和異常腦功能網(wǎng)絡(luò)拓?fù)鋵傩杂嘘P(guān),并且其統(tǒng)計意義成功的用于分類算法中的特征選擇中。建立的分類模型對抑郁癥的診斷具有一定的參考價值。
[Abstract]:Depression is a kind of disease which is affected by physiological, psychological and social environment, which leads to different degrees of disorder in mental activities such as cognition, emotion, will and so on. At present, the diagnosis of depression is based on the external symptoms of the patients. The brain network is widely used as a tool to identify abnormal topological properties. This provides a new perspective for the diagnosis of depression. However, whether the topological attributes of resting brain function network can be applied to the classification of depression is still unknown. Establish classification model to provide support for computer-aided diagnosis of depression patients. The main work is as follows:. In this study, we collected resting functional magnetic resonance imaging data from 38 first-episode depression patients and 28 normal controls to construct a resting brain functional network. In this paper, the aggregation coefficient, the shortest path length, the node degree, the center degree and the node efficiency of the brain network are calculated by using the graph theory method. These typical topological attributes of the brain network are taken as the classification features. The method of feature selection is used to screen it. Five different classification algorithms are used to construct the classifier, which includes support vector machine, neural network and decision tree. The performance of classifier with unequal number of features is evaluated. The maximum average predictive rate (84.36% and 80.70%) of the support vector machine (SVM) algorithm based on radial basis function (RBF) and neural network algorithm was obtained under 28 features (P0.05). The results show that depression is related to the topological properties of abnormal brain functional networks. And its statistical significance is successfully used in feature selection in classification algorithm. The established classification model has a certain reference value for the diagnosis 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】：2013
【分類號】：R749.4;TP311.13
【參考文獻(xiàn)】
相關(guān)期刊論文 前7條
1 易東;黃玉清;;基于SVM的移動機器人路標(biāo)識別算法[J];兵工自動化;2009年08期
2 鮑玉斌,王琢,孫煥良,于戈;一種基于分形維的快速屬性選擇算法[J];東北大學(xué)學(xué)報;2003年06期
3 田婭,饒妮妮,蒲立新;國內(nèi)醫(yī)學(xué)圖像處理技術(shù)的最新動態(tài)[J];電子科技大學(xué)學(xué)報;2002年05期
4 楊紫微;王儒敬;檀敬東;應(yīng)磊;蘇雅茹;;基于幾何判據(jù)的SVM參數(shù)快速選擇方法[J];計算機工程;2010年17期
5 吳琳琳;徐碩;;基于SVM的蛋白質(zhì)二級結(jié)構(gòu)預(yù)測[J];生物信息學(xué);2010年03期
6 趙小虎,王培軍,唐孝威;靜息狀態(tài)腦活動及其腦功能成像[J];自然科學(xué)進(jìn)展;2005年10期
7 王順銓,陳正平,陳曉華;紹興市精神疾病調(diào)查結(jié)果分析[J];中國康復(fù)理論與實踐;2005年06期
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本文編號：1675926
本文鏈接：http://sikaile.net/yixuelunwen/jsb/1675926.html
本文選題：腦網(wǎng)絡(luò) 切入點：功能磁共振成像 出處：《太原理工大學(xué)》2013年碩士論文
【摘要】：抑郁癥是在生理、心理以及社會環(huán)境等因素影響下,人的大腦功能失調(diào),導(dǎo)致認(rèn)知、情感、意志等精神活動出現(xiàn)不同程度障礙的疾病。 目前抑郁癥的診斷,多是根據(jù)病人所表現(xiàn)的外在癥狀來診斷病人是否患病。腦網(wǎng)絡(luò)作為一種廣泛應(yīng)用于識別異常拓?fù)鋵傩缘墓ぞ?為抑郁癥的診斷提供了一個新的視角。然而,靜息狀態(tài)腦功能網(wǎng)絡(luò)的拓?fù)鋵傩阅芊駪?yīng)用于抑郁癥的分類仍然未知。進(jìn)一步的研究需要找到最合適的特征選擇方法,建立分類模型,為抑郁癥患者的計算機輔助診斷提供支持。主要工作如下： 本次研究中,我們采集了38例未嘗試任何藥物的首發(fā)抑郁癥患者和28例正常對照組的靜息狀態(tài)功能磁共振成像數(shù)據(jù),構(gòu)建靜息狀態(tài)腦功能網(wǎng)絡(luò)。 利用圖論的方法計算腦網(wǎng)絡(luò)的聚合系數(shù),最短路徑長度,節(jié)點的度,節(jié)點中間中心度和節(jié)點效率,把這些典型的腦網(wǎng)絡(luò)拓?fù)鋵傩宰鳛榉诸愄卣?使用特征選擇的方法對其進(jìn)行篩選。 用五種不同的分類算法構(gòu)建分類器,分類器包括支持向量機,神經(jīng)網(wǎng)絡(luò)和決策樹等。用分類特征在統(tǒng)計意義上的重要性作為閾值來劃分特征,并對包含不等特征數(shù)量的分類器的性能進(jìn)行評估。 實驗得到在28個特征(P0.05)下,基于徑向基函數(shù)的支持向量機算法和神經(jīng)網(wǎng)絡(luò)算法得到最高平均預(yù)測率(分別為84.36%和80.70%)。結(jié)果表明抑郁癥和異常腦功能網(wǎng)絡(luò)拓?fù)鋵傩杂嘘P(guān),并且其統(tǒng)計意義成功的用于分類算法中的特征選擇中。建立的分類模型對抑郁癥的診斷具有一定的參考價值。
[Abstract]:Depression is a kind of disease which is affected by physiological, psychological and social environment, which leads to different degrees of disorder in mental activities such as cognition, emotion, will and so on. At present, the diagnosis of depression is based on the external symptoms of the patients. The brain network is widely used as a tool to identify abnormal topological properties. This provides a new perspective for the diagnosis of depression. However, whether the topological attributes of resting brain function network can be applied to the classification of depression is still unknown. Establish classification model to provide support for computer-aided diagnosis of depression patients. The main work is as follows:. In this study, we collected resting functional magnetic resonance imaging data from 38 first-episode depression patients and 28 normal controls to construct a resting brain functional network. In this paper, the aggregation coefficient, the shortest path length, the node degree, the center degree and the node efficiency of the brain network are calculated by using the graph theory method. These typical topological attributes of the brain network are taken as the classification features. The method of feature selection is used to screen it. Five different classification algorithms are used to construct the classifier, which includes support vector machine, neural network and decision tree. The performance of classifier with unequal number of features is evaluated. The maximum average predictive rate (84.36% and 80.70%) of the support vector machine (SVM) algorithm based on radial basis function (RBF) and neural network algorithm was obtained under 28 features (P0.05). The results show that depression is related to the topological properties of abnormal brain functional networks. And its statistical significance is successfully used in feature selection in classification algorithm. The established classification model has a certain reference value for the diagnosis 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】：2013
【分類號】：R749.4;TP311.13
【參考文獻(xiàn)】
相關(guān)期刊論文 前7條
1 易東;黃玉清;;基于SVM的移動機器人路標(biāo)識別算法[J];兵工自動化;2009年08期
2 鮑玉斌,王琢,孫煥良,于戈;一種基于分形維的快速屬性選擇算法[J];東北大學(xué)學(xué)報;2003年06期
3 田婭,饒妮妮,蒲立新;國內(nèi)醫(yī)學(xué)圖像處理技術(shù)的最新動態(tài)[J];電子科技大學(xué)學(xué)報;2002年05期
4 楊紫微;王儒敬;檀敬東;應(yīng)磊;蘇雅茹;;基于幾何判據(jù)的SVM參數(shù)快速選擇方法[J];計算機工程;2010年17期
5 吳琳琳;徐碩;;基于SVM的蛋白質(zhì)二級結(jié)構(gòu)預(yù)測[J];生物信息學(xué);2010年03期
6 趙小虎,王培軍,唐孝威;靜息狀態(tài)腦活動及其腦功能成像[J];自然科學(xué)進(jìn)展;2005年10期
7 王順銓,陳正平,陳曉華;紹興市精神疾病調(diào)查結(jié)果分析[J];中國康復(fù)理論與實踐;2005年06期
，本文編號：1675926
本文鏈接：http://sikaile.net/yixuelunwen/jsb/1675926.html
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