疼痛刺激对帕金森病人脑网络影响的研究

发布时间：2018-11-10 12:08

【摘要】：帕金森病是一种常见的老年神经系统疾病,表现为运动或者非运动障碍,目前大量研究致力于运动功能障碍的研究,同时也有研究发现帕金森病人的认知功能障碍,比如对疼痛的感知。独立成分分析方法是一种数据驱动方法,能够在时间和空间上将大脑信号分为相对独立和稳定的成分,同时过滤掉噪声信号;而网络属性分析方法则从图论角度反映大脑信息交流和整合能力。本文主要以热痛刺激下的帕金森病人功能磁共振图像为研究对象,采用基于数据驱动的方法以及脑网络构建分析的方法,探究帕金森病人在疼痛刺激下,脑网络属性的变化。1、从感兴趣脑区的角度出发,通过疼痛刺激找到激活脑区作为疼痛相关脑区,以这些脑区作为种子点,提取其血氧依赖水平(blood oxygen level dependent,BOLD)信号,并计算这些信号之间的功能连接,并对两组人两种状态(静息态和51度热痛刺激)的相关Z值进行方差分析,发现基底节与脑岛等脑区与其他脑区的相关系数在双因素交互作用下,在两组人中存在显著差异,并且这些差异跟临床热痛觉和热敏觉评分显著相关。2、从局部网络的角度出发,通过独立成分分析(ICA)的方法,得到47个时间和空间相对独立和稳定的成分;根据文献挑选出7个疼痛相关成分,并计算这些疼痛网络之间的功能连接,最后采用方差分析方法对相关Z值做统计分析。主要发现帕金森病人的基底节与salience网络之间的功能连接存在显著降低,进一步说明了基底节网络在帕金森病人中的变化。3、从大脑全局网络出发,通过AAL模板,计算大脑90个脑区之间的功能连接构建全脑功能网络,通过分析网络属性,包括度中心性、中介中心性、聚类系数、特征向量中心性、局部效率以及hub节点等指标,发现帕金森病人全局网络特征变化,包括度中心性降低、hub节点分布变化、rich-club系数降低等。本研究从以上三个方面,主要从局部和全局网络出发,提供帕金森病人大脑网络变化情况,并为帕金森病的诊断和治疗提供影像学依据。
[Abstract]:Parkinson's disease (PD) is a common disease of the aged nervous system, which is characterized by motor or non-motor disorders. At present, a great deal of research has been devoted to the study of motor dysfunction. At the same time, some studies have found cognitive dysfunction in patients with Parkinson's disease. Like the perception of pain. Independent component analysis (ICA) is a data-driven method, which can divide brain signals into relatively independent and stable components in time and space, and filter out noise signals at the same time. The network attribute analysis method reflects the ability of brain information exchange and integration from the angle of graph theory. In this paper, the functional magnetic resonance images of patients with Parkinson's disease induced by heat pain were studied, and the data driven method and the analysis of brain network construction were used to explore the Parkinson's patients under pain stimulation. 1. From the point of view of the brain region of interest, the activated brain area was found as the pain related brain area through pain stimulation, and these brain regions were used as seed points to extract the (blood oxygen level dependent,BOLD) signals of oxygen-dependent level. The functional connections between these signals were calculated, and the correlation Z values of two groups of people (resting state and 51 degree heat pain stimulation) were analyzed by ANOVA. It was found that the correlation coefficients between basal ganglia and isobaric and other brain regions were significantly different between the two groups under the interaction of two factors, and these differences were significantly correlated with the clinical scores of thermal pain and thermosensitivity. From the point of view of local network, 47 independent and stable components in time and space are obtained by the method of independent component analysis (ICA). According to the literature, 7 pain related components were selected, and the functional connections between these pain networks were calculated. Finally, the correlation Z value was statistically analyzed by means of variance analysis. It was found that the functional connections between basal ganglia and salience network in Parkinson's patients were significantly decreased, which further explained the changes of basal ganglion network in Parkinson's patients. The functional connections between 90 brain regions were calculated to construct the whole brain functional network. By analyzing the properties of the network, including degree centrality, intermediary centrality, clustering coefficient, characteristic vector centrality, local efficiency and hub nodes, etc. It was found that the global network characteristics of Parkinson's patients were decreased, including the decrease of degree centrality, the change of hub node distribution, and the decrease of rich-club coefficient. From the above three aspects, mainly from the local and global networks, the changes of brain network in Parkinson's patients were provided, and the imaging data were provided for the diagnosis and treatment of Parkinson's disease.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R742.5

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