發(fā)布時(shí)間：2018-06-25 13:07

  本文選題：功能磁共振 + 復(fù)雜網(wǎng)絡(luò)　； 參考：《太原理工大學(xué)》2013年博士論文

【摘要】：人腦是現(xiàn)實(shí)世界中最為復(fù)雜的網(wǎng)絡(luò)系統(tǒng)之一。其復(fù)雜性不僅體現(xiàn)在數(shù)以?xún)|計(jì)的神經(jīng)元及連接的數(shù)量,更體現(xiàn)其在不同尺度下的構(gòu)成以及這些連接在認(rèn)知功能、思想、感覺(jué)及行為時(shí)所表現(xiàn)出來(lái)的不同模式。近年來(lái),將復(fù)雜網(wǎng)絡(luò)理論應(yīng)用在神經(jīng)認(rèn)知科學(xué)中,利用復(fù)雜網(wǎng)絡(luò)基本原理等方法進(jìn)行屬性分析,以期發(fā)現(xiàn)網(wǎng)絡(luò)基本屬性及節(jié)點(diǎn)間潛在的拓?fù)潢P(guān)系。復(fù)雜網(wǎng)絡(luò)理論使我們從一個(gè)不同的角度來(lái)看待人腦這一復(fù)雜系統(tǒng),也為人腦的研究提供了一個(gè)新的方向。 本文基于復(fù)雜網(wǎng)絡(luò)理論,探討靜息態(tài)功能腦網(wǎng)絡(luò)構(gòu)建、分析及比較方法,并在此基礎(chǔ)上完成靜息態(tài)腦網(wǎng)絡(luò)構(gòu)建分析軟件平臺(tái)的開(kāi)發(fā)；利用功能腦網(wǎng)絡(luò)進(jìn)行網(wǎng)絡(luò)指標(biāo)組間比較,從全局屬性、局部屬性、社團(tuán)化分析等多角度進(jìn)行組間差異分析；利用抑郁癥作為疾病模型驗(yàn)證上述方法的臨床可用性,尋找在腦疾病狀態(tài)及基因影響下的變化規(guī)律,探索發(fā)現(xiàn)抑郁癥早期診斷的影像學(xué)標(biāo)志,突破腦影像技術(shù)在精神疾病臨床診斷應(yīng)用所面臨的瓶頸問(wèn)題；針對(duì)網(wǎng)絡(luò)表征,利用機(jī)器學(xué)習(xí)算法,建立抑郁癥輔助模型,以輔助臨床診斷應(yīng)用。 本文主要?jiǎng)?chuàng)新工作包括有： (1)提出抑郁癥靜息態(tài)功能腦網(wǎng)絡(luò)指標(biāo)差異分析方法,并構(gòu)建分類(lèi)模型 本文分別對(duì)抑郁癥患者及健康人群的靜息態(tài)功能腦網(wǎng)絡(luò)拓?fù)鋵傩詮亩鄠�(gè)角度進(jìn)行刻畫(huà)及比較分析,尋找組間差異,揭示抑郁癥在網(wǎng)絡(luò)層面的指標(biāo)變化規(guī)律。利用多種機(jī)器學(xué)習(xí)方法,將所發(fā)現(xiàn)的差異指標(biāo)作為分類(lèi)特征,進(jìn)行分類(lèi)模型構(gòu)建及性能評(píng)價(jià)。并利用敏感性分析,判定其在分類(lèi)模型中的貢獻(xiàn)度,以驗(yàn)證研究方法的合理性。 (2)利用復(fù)雜網(wǎng)絡(luò)模塊劃分方法進(jìn)行靜息態(tài)功能腦模塊劃分,并提出抑郁癥模塊結(jié)構(gòu)差異分析技術(shù) 本文利用基于貪婪思想的CNM模塊劃分算法,完成抑郁組及對(duì)照組的靜息態(tài)功能腦網(wǎng)絡(luò)模塊劃分,并從模塊的組成、模塊角色、模塊間的連接等多個(gè)角度,挖掘抑郁癥在模塊結(jié)構(gòu)上的差異。最后,利用差異模塊指標(biāo)進(jìn)行分類(lèi)研究,以驗(yàn)證方法的可靠性,最高正確率可達(dá)到90%以上。 (3)提出基于基因的抑郁癥腦網(wǎng)絡(luò)拓?fù)鋵傩圆町惙治黾夹g(shù) 前人研究證明,基因?qū)τ谀X網(wǎng)絡(luò)的拓?fù)鋵傩詣t存在不同程度的影響。本文利用功能腦網(wǎng)絡(luò)方法,挖掘GSK3β基因?qū)τ谝钟舭Y患者及正常對(duì)照的網(wǎng)絡(luò)拓?fù)鋵傩圆町?以探討腦網(wǎng)絡(luò)的基因基礎(chǔ)。 (4)提出抑郁癥局部一致性指標(biāo)差異分類(lèi)技術(shù),構(gòu)建分類(lèi)模型并提出特征評(píng)價(jià)標(biāo)準(zhǔn) 局部一致性方法反映了腦區(qū)中某個(gè)局部的神經(jīng)元活動(dòng)在時(shí)間上的一致性和同步性。本文利用局部一致性指標(biāo),進(jìn)行抑郁癥組間差異分析。利用機(jī)器學(xué)習(xí)方法,驗(yàn)證局部一致性方法的可靠性,并提出通過(guò)敏感性分析方法對(duì)所選指標(biāo)進(jìn)行量化評(píng)價(jià)。 本文是國(guó)家自然科學(xué)基金項(xiàng)目《抑郁癥fMRI數(shù)據(jù)分析方法及輔助診斷治療模型研究》(No.61170136)的主要組成部分。研究工作還得到了山西省教育廳高�？萍柬�(xiàng)目《多模態(tài)腦網(wǎng)絡(luò)拓?fù)鋵傩苑治龇椒ㄑ芯俊?No.20121003)以及太原理工大學(xué)青年基金項(xiàng)目《抑郁癥靜息態(tài)功能腦網(wǎng)絡(luò)拓?fù)鋵傩圆町惙治鲅芯俊?No.2012L014)的支持。本文重點(diǎn)研究靜息態(tài)功能腦網(wǎng)絡(luò)的構(gòu)建、分析方法及其軟件平臺(tái)的開(kāi)發(fā),以及腦疾病狀態(tài)下腦網(wǎng)絡(luò)的變化規(guī)律,在此基礎(chǔ)上探索抑郁癥等重大腦疾病早期診斷的影像學(xué)標(biāo)志,并建立輔助診斷模型。這不僅是國(guó)際前沿基礎(chǔ)科學(xué)問(wèn)題,也是國(guó)家重大需求。
[Abstract]:The human brain is one of the most complex network systems in the real world. Its complexity is not only reflected in the number of hundreds of millions of neurons and connections, but also its composition at different scales and the different modes of these connections in cognitive function, thought, feeling and behavior. In recent years, the complex network theory has been applied to the complex network theory. In neurocognitive science, attribute analysis is carried out by means of the basic principles of complex networks, in order to find the basic properties of the network and the potential topological relations between nodes. Complex network theory makes us look at the complex system of human brain from a different angle, and also provides a new direction for the research of human brain.
Based on the complex network theory, this paper discusses the construction, analysis and comparison of resting state functional brain networks. On this basis, the rest state brain network construction analysis software platform is developed. By using depression as a disease model to verify the clinical availability of the above methods, look for the changes in the state of brain disease and the influence of genes, explore the imaging signs of early diagnosis of depression, break through the bottleneck of the application of brain imaging technology in the clinical diagnosis of mental disease, and use the machine for network characterization. The learning algorithm is used to establish the auxiliary model of depression to assist clinical diagnosis.
The main innovative work of this article includes:
(1) put forward the method of differential analysis of resting state functional brain network index, and build a classification model.
This paper depicts and compares the topological properties of the resting functional brain network of the depressive patients and the healthy people respectively, and finds the difference between the groups and reveals the changing rules of the index of the depression at the network level. The classification model is constructed by using a variety of machine learning methods to classify the identified differences as the classification characteristics. The sensitivity analysis is used to determine its contribution in the classification model, so as to verify the rationality of the research method.
(2) use the complex network module partition method to divide the resting state functional brain module, and put forward the analysis technology of depression module structure difference.
In this paper, the CNM module division algorithm based on greedy thought is used to divide the resting state functional brain network module of the depression group and the control group, and the differences in the module structure are excavated from the components of the module, the module role and the connection between the modules. Finally, the difference module index is used to classify and study the methods to verify the method. Reliability, the highest correct rate can reach more than 90%.
(3) put forward the technology of topological difference analysis of brain network based on gene depression.
Previous studies have shown that genes have different effects on the topological properties of brain networks. In this paper, the functional brain network method is used to explore the network topological properties of GSK3 beta gene for patients with depression and normal controls, so as to explore the genetic basis of brain networks.
(4) put forward the difference classification technology of regional coherence index of depression, build classification model and put forward characteristic evaluation standard.
The local conformance method reflects the time consistency and synchronism of a local neuron activity in the brain. In this paper, the local conformance index is used to carry out the difference analysis between the depression groups. The reliability of the local consistency method is verified by the machine learning method, and the quantity of the selected index is measured by the sensitivity analysis method. Evaluation.
This paper is the main component of the National Natural Science Foundation of National Natural Science Project (National Natural Science Foundation), the fMRI data analysis method of depression and the model of auxiliary diagnosis and treatment (No.61170136). The research work has also been obtained by the science and technology project of the University of education of Shanxi Province, the study of the multi-modal brain network topology analysis method (No.20121003) and the Taiyuan University of Technology youth fund. This article focuses on the construction of the resting state functional brain network (No.2012L014), the analysis method and the development of its software platform, as well as the changes of brain network in brain disease state, and explore the early diagnosis of depression and other serious brain diseases on this basis. Imaging markers and establishing auxiliary diagnostic models are not only the international frontier basic science issues, but also the major national needs.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：R749.4;O157.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 樊曉燕,郭春彥;從認(rèn)知神經(jīng)科學(xué)的角度看熟悉性和回想[J];心理科學(xué)進(jìn)展;2005年03期

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本文編號(hào)：2066037