【作者】 郭浩；

【导师】 陈俊杰；

【作者基本信息】 太原理工大学 ， 计算机应用技术， 2013， 博士

【摘要】 人脑是现实世界中最为复杂的网络系统之一。其复杂性不仅体现在数以亿计的神经元及连接的数量,更体现其在不同尺度下的构成以及这些连接在认知功能、思想、感觉及行为时所表现出来的不同模式。近年来,将复杂网络理论应用在神经认知科学中,利用复杂网络基本原理等方法进行属性分析,以期发现网络基本属性及节点间潜在的拓扑关系。复杂网络理论使我们从一个不同的角度来看待人脑这一复杂系统,也为人脑的研究提供了一个新的方向。本文基于复杂网络理论,探讨静息态功能脑网络构建、分析及比较方法,并在此基础上完成静息态脑网络构建分析软件平台的开发；利用功能脑网络进行网络指标组间比较,从全局属性、局部属性、社团化分析等多角度进行组间差异分析；利用抑郁症作为疾病模型验证上述方法的临床可用性,寻找在脑疾病状态及基因影响下的变化规律,探索发现抑郁症早期诊断的影像学标志,突破脑影像技术在精神疾病临床诊断应用所面临的瓶颈问题；针对网络表征,利用机器学习算法,建立抑郁症辅助模型,以辅助临床诊断应用。本文主要创新工作包括有：(1)提出抑郁症静息态功能脑网络指标差异分析方法,并构建分类模型本文分别对抑郁症患者及健康人群的静息态功能脑网络拓扑属性从多个角度进行刻画及比较分析,寻找组间差异,揭示抑郁症在网络层面的指标变化规律。利用多种机器学习方法,将所发现的差异指标作为分类特征,进行分类模型构建及性能评价。并利用敏感性分析,判定其在分类模型中的贡献度,以验证研究方法的合理性。(2)利用复杂网络模块划分方法进行静息态功能脑模块划分,并提出抑郁症模块结构差异分析技术本文利用基于贪婪思想的CNM模块划分算法,完成抑郁组及对照组的静息态功能脑网络模块划分,并从模块的组成、模块角色、模块间的连接等多个角度,挖掘抑郁症在模块结构上的差异。最后,利用差异模块指标进行分类研究,以验证方法的可靠性,最高正确率可达到90%以上。(3)提出基于基因的抑郁症脑网络拓扑属性差异分析技术前人研究证明,基因对于脑网络的拓扑属性则存在不同程度的影响。本文利用功能脑网络方法,挖掘GSK3β基因对于抑郁症患者及正常对照的网络拓扑属性差异,以探讨脑网络的基因基础。(4)提出抑郁症局部一致性指标差异分类技术,构建分类模型并提出特征评价标准局部一致性方法反映了脑区中某个局部的神经元活动在时间上的一致性和同步性。本文利用局部一致性指标,进行抑郁症组间差异分析。利用机器学习方法,验证局部一致性方法的可靠性,并提出通过敏感性分析方法对所选指标进行量化评价。本文是国家自然科学基金项目《抑郁症fMRI数据分析方法及辅助诊断治疗模型研究》(No.61170136)的主要组成部分。研究工作还得到了山西省教育厅高校科技项目《多模态脑网络拓扑属性分析方法研究》(No.20121003)以及太原理工大学青年基金项目《抑郁症静息态功能脑网络拓扑属性差异分析研究》(No.2012L014)的支持。本文重点研究静息态功能脑网络的构建、分析方法及其软件平台的开发,以及脑疾病状态下脑网络的变化规律,在此基础上探索抑郁症等重大脑疾病早期诊断的影像学标志,并建立辅助诊断模型。这不仅是国际前沿基础科学问题,也是国家重大需求。更多还原

【Abstract】 The human brain is one of the most complex system in the world. Its complexity is not only reflected in the number of neurons and connections, but also in how the brain is wired on different scales and how such patterns of their connections produce cognitive functions, thoughts, feelings, and behaviors. Recently, a number of researchers have applied complex network methods in cognitive neuroscience, especially for the study of psychiatric disorders. Using complex network principles and statistical physics methods, researchers can analyze and discover basic network properties and the potential topological relationships between nodes. Complex network theory provides a new perspective and method for brain research.The current study explores the resting state functional brain network, compares methods based on complex network theory, and develops a software platform. On the basis of these observations, different network metrics, including global, local, and modular properties, are compared to explore the differences between groups. We then confirm the clinical availability of major depressive disorder (MDD) as a disease model. The aim is to determine the imageology symbol for the clinical early diagnosis and treatment of MDD at the network level. Finally, referring to the network metrics, the current study constructs an aided diagnosis model of MDD using a machine learning algorithm that could be helpful in clinical applications.The current study includes the following research outcomes:(1) Construction, analysis and classification of a resting state functional brain network in MDDGlobal and local metrics are calculated using graph theory-based approaches. Non-parametric permutation tests are then used for group comparisons of topological metrics, which are used as classified features in different algorithms. A sensitivity analysis is used to calculate the change in variance of each feature in the target category.(2) Research of the different community structures of resting state functional brain networks in MDDA greedy algorithm is used to divide the community structures. In terms of the modularity of the brain network, differences in the modularity metrics of normal control subjects and MDD patients are found, including the modular components, modular roles, and connections between modules. These differences are used as classification features in the machine learning method, the results of which exhibit a highest accuracy of90.50%.(3) Gene effects for the resting state functional brain network in MDDConvergent evidence from multimodal imaging studies has demonstrated that brain networks are both structurally and functionally heritable. A general linear2×2analysis of variance test is performed to investigate the main effects of genotype and prevalence, as well as their interaction. The metrics with significant differences are selected as features in the classification research.(4) Feature selection and classifier using regional homogeneity method in MDDThe current study explores the regional homogeneity of brain regions in the resting state to test the abnormality hypothesis in MDD patients. Classification and sensitivity analysis are also used to determine changes in the variance of each feature in the target category.The current study is the main component of the National Natural Science Foundation Project "The study of fMRI data analysis methods and diagnosis treatment models in major depressive disorder (No.61170136)", and is also supported by the University Science Research and Development Project of Shanxi Province (No.20121003) and the Special/Youth Foundation of Taiyuan University of Technology (No.2012L014).In brief, the current study focuses on resting state funcaional brain network construction and analysis techniques, the development of a software platform, and topological network changes in the condition of brain disease. On this basis, the current study explores the imageology symbols of early diagnosis and prognostic evaluation of major brain diseases, and constructs a diagnostic model. This is not only at the frontier of international scientific issues, but is also a major national requirement.更多还原