【摘要】：人腦是自然界最復(fù)雜的系統(tǒng)之一,科研工作者一直致力于使用各種新技術(shù)來研究和探索人腦的工作原理和運(yùn)行機(jī)制。近年來基于核磁共振成像的人腦結(jié)構(gòu)網(wǎng)絡(luò)重構(gòu)技術(shù)日益成熟,利用圖論和復(fù)雜網(wǎng)絡(luò)理論去分析人腦網(wǎng)絡(luò)也正在成為腦科學(xué)研究的重點(diǎn)。人腦結(jié)構(gòu)網(wǎng)絡(luò)是一個(gè)復(fù)雜網(wǎng)絡(luò),存在著模塊結(jié)構(gòu),對(duì)大腦整體運(yùn)行起著至關(guān)重要的作用,目前多數(shù)研究集中在二值人腦結(jié)構(gòu)網(wǎng)絡(luò)的模塊劃分方法上。二值人腦結(jié)構(gòu)網(wǎng)絡(luò)往往只體現(xiàn)腦區(qū)間有無連接的關(guān)系,而基于人腦生理信息所構(gòu)建的加權(quán)人腦結(jié)構(gòu)網(wǎng)絡(luò)則可以表達(dá)腦區(qū)之間更具體的關(guān)系,在此基礎(chǔ)上進(jìn)行的模塊結(jié)構(gòu)劃分也更有意義。本文主要以加權(quán)人腦結(jié)構(gòu)網(wǎng)絡(luò)為對(duì)象,對(duì)加權(quán)網(wǎng)絡(luò)的模塊化算法展開研究。首先完成了基于核磁共振成像數(shù)據(jù)的二值人腦結(jié)構(gòu)網(wǎng)絡(luò)和加權(quán)人腦結(jié)構(gòu)網(wǎng)絡(luò)的構(gòu)建,并采用Fast Newman算法對(duì)二值人腦結(jié)構(gòu)網(wǎng)絡(luò)進(jìn)行了模塊劃分和結(jié)果分析;然后,在此基礎(chǔ)上對(duì)加權(quán)人腦結(jié)構(gòu)網(wǎng)絡(luò)的模塊結(jié)構(gòu)劃分算法展開了研究,提出了一種基于凝聚節(jié)點(diǎn)思想的加權(quán)Fast Newman模塊化算法,該算法以單個(gè)腦區(qū)權(quán)重和網(wǎng)絡(luò)總權(quán)重為依據(jù)構(gòu)建加權(quán)模塊度評(píng)價(jià)指標(biāo),并將其增量作為度量值來決定腦區(qū)是否合并從而實(shí)現(xiàn)模塊劃分。該算法分別與二值人腦結(jié)構(gòu)網(wǎng)絡(luò)的模塊化算法和現(xiàn)有加權(quán)網(wǎng)絡(luò)的模塊化算法進(jìn)行了實(shí)驗(yàn)對(duì)比,結(jié)果顯示,本文算法得到的模塊度更高,其模塊結(jié)構(gòu)也更貼近已知的人腦生理特征。最后將本文算法應(yīng)用于精神分裂癥患者與健康人的實(shí)驗(yàn)數(shù)據(jù)中,對(duì)比實(shí)驗(yàn)顯示了兩組被試的人腦結(jié)構(gòu)網(wǎng)絡(luò)在模塊結(jié)構(gòu)上存在著差異性。
[Abstract]:Human brain is one of the most complex systems in nature. Researchers have been using various new technologies to study and explore the working principle and mechanism of human brain. In recent years, the technology of network reconstruction of human brain structure based on nuclear magnetic resonance imaging (NMR) is becoming more and more mature, and it is becoming the focus of brain science to analyze human brain network by using graph theory and complex network theory. The human brain structural network is a complex network with a modular structure, which plays an important role in the whole operation of the brain. At present, most of the researches focus on the modular partition method of the binary human brain structure network. The binary human brain structure network usually only reflects the relationship between brain regions, and the weighted human brain structure network based on the human brain physiological information can express the more specific relationship between the brain regions. On this basis, the module structure partition is more meaningful. In this paper, the modularization algorithm of weighted human brain network is studied. Firstly, the binary human brain structure network and the weighted human brain structure network are constructed based on the MRI data, and the binary human brain structure network is partitioned by Fast Newman algorithm and the results are analyzed. On this basis, the modular structure partition algorithm of weighted human brain network is studied, and a weighted Fast Newman modularization algorithm based on the idea of condensed nodes is proposed. Based on the weight of a single brain region and the total weight of the network, the algorithm constructs a weighted modular degree evaluation index, and takes its increment as a measure to determine whether the brain region is merged or not, so as to realize the module division. The algorithm is compared with the modular algorithm of the binary human brain network and the existing modular algorithm of the weighted network. The results show that the modular degree of this algorithm is higher than that of the traditional algorithm. Its module structure is also closer to the known physiological characteristics of the human brain. Finally, the algorithm is applied to the experimental data of schizophrenic patients and healthy people. The comparative experiments show that there are differences in the structure of the human brain network between the two groups.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R318;O157.5

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