最小生成樹在精神分裂癥EEG腦網(wǎng)絡(luò)中的應(yīng)用研究
發(fā)布時(shí)間：2018-09-10 10:52
【摘要】：隨著社會(huì)壓力的不斷增加,精神類疾病已經(jīng)成為導(dǎo)致亞健康的主要因素之一,精神分裂癥就是最為常見的精神類疾病,對(duì)該疾病的早期診斷也顯得尤為重要。目前的診斷手段大多基于量表,醫(yī)生根據(jù)量表進(jìn)行主觀判斷,這種診斷方式存在較多主觀因素,為精神分裂癥的診斷帶來不可預(yù)估的影響。隨著復(fù)雜網(wǎng)絡(luò)理論和腦功能成像技術(shù)的發(fā)展,基于EEG腦網(wǎng)絡(luò)的精神分裂癥疾病研究取得了一定的進(jìn)展,為該疾病的病理認(rèn)知和早期診斷提供了新的思路。但是,近來也有研究指出傳統(tǒng)復(fù)雜網(wǎng)絡(luò)分析方法存在一定的弊端。傳統(tǒng)復(fù)雜網(wǎng)絡(luò)研究方法包括無權(quán)網(wǎng)絡(luò)、加權(quán)網(wǎng)絡(luò)。無權(quán)網(wǎng)絡(luò)涉及到閾值選擇的問題,閾值T的選擇從本質(zhì)上是隨機(jī)的。閾值的選擇可能會(huì)導(dǎo)致網(wǎng)絡(luò)中存在虛假或噪聲連接(T的選擇較小),也可能丟棄掉網(wǎng)絡(luò)中包含重要信息的一些弱連接(T的選擇較大)。邊的多少進(jìn)一步影響腦網(wǎng)絡(luò)屬性值的測(cè)量。而加權(quán)網(wǎng)絡(luò)的度量同樣會(huì)被噪聲連接和平均功能連接強(qiáng)度影響,所以,無權(quán)網(wǎng)絡(luò)和加權(quán)網(wǎng)絡(luò)的分析方法可能會(huì)由于方法學(xué)的問題導(dǎo)致無法令人確信的研究結(jié)果。因此,本研究將最小生成樹算法(Minimum Spanning Tree,MST)引入到復(fù)雜腦網(wǎng)絡(luò)的研究中,期望能解決傳統(tǒng)網(wǎng)絡(luò)分析方法中存在的問題,在EEG腦網(wǎng)絡(luò)分析的方法學(xué)問題上進(jìn)行有價(jià)值的探索。并在精神分裂癥與正常被試的EEG腦網(wǎng)絡(luò)中對(duì)無權(quán)網(wǎng)絡(luò)、加權(quán)網(wǎng)絡(luò)以及最小生成樹這三種研究方法進(jìn)行了對(duì)比分析與探討。本研究所用的EEG數(shù)據(jù)為來自合作單位北京回龍觀醫(yī)院的40例精神分裂癥患者和40例正常被試,所有數(shù)據(jù)經(jīng)過預(yù)處理后,通過相位滯后指數(shù)(PLI)分別構(gòu)建了無權(quán)、加權(quán)以及MST腦網(wǎng)絡(luò),然后將不同被試組間顯著差異的網(wǎng)絡(luò)屬性作為分類特征進(jìn)行了分類研究,最后從理論分析、相關(guān)性分析和分類效果上證明了MST引入EEG腦網(wǎng)絡(luò)的研究是有效可行的,是對(duì)復(fù)雜腦網(wǎng)絡(luò)傳統(tǒng)分析方法的有益補(bǔ)充。本研究主要完成的內(nèi)容有以下幾點(diǎn):(1)選取不同閾值構(gòu)建各個(gè)稀疏度下精神分裂癥與正常被試的無權(quán)腦網(wǎng)絡(luò),計(jì)算其屬性并進(jìn)行了統(tǒng)計(jì)分析,提取有顯著性差異的無權(quán)網(wǎng)屬性。(2)計(jì)算精神分裂癥與正常被試的加權(quán)腦網(wǎng)絡(luò)屬性并對(duì)其進(jìn)行了統(tǒng)計(jì)分析,提取有顯著性差異的加權(quán)網(wǎng)屬性。(3)采用Kruskal算法構(gòu)建精神分裂癥與正常被試的MST。計(jì)算精神分裂癥與正常被試MST網(wǎng)絡(luò)的屬性并對(duì)其進(jìn)行統(tǒng)計(jì)分析,提取有顯著性差異的MST屬性。(4)從理論分析、相關(guān)性分析和分類效果上證明了MST引入EEG腦網(wǎng)絡(luò)的研究是有效可行的,是對(duì)復(fù)雜腦網(wǎng)絡(luò)傳統(tǒng)分析方法的有益補(bǔ)充�？傊�,MST比傳統(tǒng)復(fù)雜網(wǎng)絡(luò)分析方法在EEG腦網(wǎng)絡(luò)研究中具有更好的表現(xiàn),將其作為傳統(tǒng)復(fù)雜網(wǎng)絡(luò)的有益補(bǔ)充,能為EEG腦網(wǎng)絡(luò)的研究提供更佳的思路與方法。
[Abstract]:With the increasing of social pressure, mental diseases have become one of the main causes of sub-health, schizophrenia is the most common mental diseases, the early diagnosis of this disease is particularly important. At present, most diagnostic methods are based on the scale, and doctors make subjective judgment according to the scale. There are many subjective factors in this diagnostic method, which has an unpredictable impact on the diagnosis of schizophrenia. With the development of complex network theory and brain function imaging technology, the research of schizophrenia disease based on EEG brain network has made some progress, which provides a new idea for the pathological cognition and early diagnosis of the disease. However, recent studies have pointed out that traditional complex network analysis methods have some drawbacks. Traditional complex network research methods include weighted network and weighted network. The selection of threshold T is random in nature. The selection of threshold may lead to the existence of false or noisy connections (the selection of T is small), or it may discard some weak connections containing important information in the network (the choice of T is large). The number of edges further affects the measurement of brain network attribute values. The measurement of weighted networks is also affected by the noise connection and the average functional connection strength. Therefore, the analysis methods of the weighted network and the weighted network may lead to the inconclusive research results due to the methodological problems. Therefore, the minimum spanning tree algorithm (Minimum Spanning Tree,MST) is introduced into the study of complex brain networks, which is expected to solve the problems existing in the traditional network analysis methods and to explore the methodology of EEG brain network analysis. In the EEG brain network of schizophrenia and normal subjects, the three research methods of weighted network, weighted network and minimal spanning tree were compared and discussed. The EEG data used in this study were 40 schizophrenic patients and 40 normal subjects from Huilongguan Hospital in Beijing. After pretreatment, the data were constructed with phase lag index (PLI). Weighted and MST brain networks were used to classify the significantly different network attributes of different subjects as classification features. Finally, the theoretical analysis was carried out. The results of correlation analysis and classification show that the research of introducing MST into EEG brain networks is effective and feasible, and it is a useful supplement to the traditional analysis methods of complex brain networks. The main contents of this study are as follows: (1) selecting different thresholds to construct the brain network of schizophrenia and normal subjects under different sparsity, calculating its attributes and making statistical analysis. (2) the weighted brain network attributes of schizophrenia and normal subjects were calculated and analyzed statistically. (3) Kruskal algorithm was used to construct MST. between schizophrenia and normal subjects. The attributes of MST network of schizophrenia and normal subjects were calculated and statistically analyzed, and the MST attributes with significant differences were extracted. (4) from theoretical analysis, The results of correlation analysis and classification show that the research of introducing MST into EEG brain networks is effective and feasible, and it is a useful supplement to the traditional analysis methods of complex brain networks. In a word, MST has better performance in the research of EEG brain network than the traditional complex network analysis method. As a useful supplement to the traditional complex network, it can provide a better way of thinking and method for the study of EEG brain network.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R749.3;O157.5
本文編號(hào)：2234233
[Abstract]:With the increasing of social pressure, mental diseases have become one of the main causes of sub-health, schizophrenia is the most common mental diseases, the early diagnosis of this disease is particularly important. At present, most diagnostic methods are based on the scale, and doctors make subjective judgment according to the scale. There are many subjective factors in this diagnostic method, which has an unpredictable impact on the diagnosis of schizophrenia. With the development of complex network theory and brain function imaging technology, the research of schizophrenia disease based on EEG brain network has made some progress, which provides a new idea for the pathological cognition and early diagnosis of the disease. However, recent studies have pointed out that traditional complex network analysis methods have some drawbacks. Traditional complex network research methods include weighted network and weighted network. The selection of threshold T is random in nature. The selection of threshold may lead to the existence of false or noisy connections (the selection of T is small), or it may discard some weak connections containing important information in the network (the choice of T is large). The number of edges further affects the measurement of brain network attribute values. The measurement of weighted networks is also affected by the noise connection and the average functional connection strength. Therefore, the analysis methods of the weighted network and the weighted network may lead to the inconclusive research results due to the methodological problems. Therefore, the minimum spanning tree algorithm (Minimum Spanning Tree,MST) is introduced into the study of complex brain networks, which is expected to solve the problems existing in the traditional network analysis methods and to explore the methodology of EEG brain network analysis. In the EEG brain network of schizophrenia and normal subjects, the three research methods of weighted network, weighted network and minimal spanning tree were compared and discussed. The EEG data used in this study were 40 schizophrenic patients and 40 normal subjects from Huilongguan Hospital in Beijing. After pretreatment, the data were constructed with phase lag index (PLI). Weighted and MST brain networks were used to classify the significantly different network attributes of different subjects as classification features. Finally, the theoretical analysis was carried out. The results of correlation analysis and classification show that the research of introducing MST into EEG brain networks is effective and feasible, and it is a useful supplement to the traditional analysis methods of complex brain networks. The main contents of this study are as follows: (1) selecting different thresholds to construct the brain network of schizophrenia and normal subjects under different sparsity, calculating its attributes and making statistical analysis. (2) the weighted brain network attributes of schizophrenia and normal subjects were calculated and analyzed statistically. (3) Kruskal algorithm was used to construct MST. between schizophrenia and normal subjects. The attributes of MST network of schizophrenia and normal subjects were calculated and statistically analyzed, and the MST attributes with significant differences were extracted. (4) from theoretical analysis, The results of correlation analysis and classification show that the research of introducing MST into EEG brain networks is effective and feasible, and it is a useful supplement to the traditional analysis methods of complex brain networks. In a word, MST has better performance in the research of EEG brain network than the traditional complex network analysis method. As a useful supplement to the traditional complex network, it can provide a better way of thinking and method for the study of EEG brain network.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R749.3;O157.5
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