精神分裂症和抑郁症患者核心脑网络异常连接模式研究

发布时间：2019-06-19 14:23

【摘要】：精神分裂症是患病率最高的一种重度精神疾病,主要表现为特征性情感、行为、思维、感知等多方面的障碍,并伴有多种认知和情感功能损伤。抑郁症作为最常见的心理情感障碍之一,临床表现为与处境不相称的、持久的情绪低落。其发病率,复发率,自杀率很高。作为最常见的两种精神疾病,精神分裂症和抑郁症享有一些相同和不同的临床特征。近年来,功能磁共振技术发现,大脑不同脑区的异常连接可能作为精神疾病的病理生理机制。探究不同脑区之间的信息流动和整合机制,对理解精神疾病的病理生理机制存在重要意义。特别是,认知核心网络模型的提出,更加重视探究疾病间的共性和特异性,也暗示着多种精神疾病可能是由于内源和外源信息交互异常。该模型认为默认网络、突显网络和中央执行网络之间的异常连接模式是精神疾病病理机制的核心所在。然而,至今少有文献关注在这三个核心网络之间,不同的精神疾病是否存在一些相同或者不同的异常连接。本文以此作为出发点和落脚点,探究默认网络、突显网络和中央执行网络之间的连接模式,分别采用功能连接分析和格兰杰因果分析评测网络间的信息整合和信息交流,重点关注两种疾病间同向和反向的异常改变连接。在本研究中,采集精神分裂症患者、抑郁症患者和健康对照组每组各20名被试的静息态功能磁共振数据。首先利用先验信息,确定默认网络和中央执行网络核心节点,即后扣带和背外侧前额皮层脑区。采用基于全脑功能连接图谱的K均值聚类方法,将脑岛分为三个亚区,从而定位出突显网络的核心节点——背侧前脑岛。然后,利用基于种子点的功能连接分析定位出三个核心网络的空间分布。然后计算种子点和核心网络内每个体素的功能连接和有效连接,分别刻画不同脑区间功能整合和信息传递。着重关注两个种疾病间同向和反向改变的连接模式。最后,采用偏相关分析探究改变的连接和临床资料的关系。本文发现三核心网络间的连接存在同向和反向的改变模式。在功能连接水平:默认模式网络和中央执行网络之间存在反向改变的功能连接。相较于正常对照组,背外侧前额皮层和内侧前额皮层的功能连接以及后扣带和额下回三角部区域的功能连接在精神分裂症组呈现显著上升,而在抑郁症组显著下降。这种反向改变的功能连接可能源于“自参考”功能异常,因而表现出不同的表象:抑郁症中过多的自我归因和精神分裂症中过少甚至缺失的自我认识。在有效连接网络水平:发现精神分裂症和抑郁症患者的默认网络和突显网络之间存在同向改变模式的有效连接,即相较于正常对照组,两患者组从后扣带到背侧前脑岛和颞顶交界区的有效连接均增强;而从背侧前脑岛脑岛到后扣带的有效连接减弱。这一同向改变的有效连接可能反映了默认网络和突显网络之间动态信息交互在两种疾病间出现平行的缺陷,可能和两种疾病间共同的认知和情感损伤有关。
[Abstract]:Schizophrenia is the highest prevalence of a kind of severe mental illness, mainly characterized by the characteristic emotion, behavior, thinking, perception, etc., with a variety of cognitive and emotional function damage. Depression is one of the most common psychological affective disorders, and the clinical manifestations are out of proportion with the situation, and the persistent mood is low. Its incidence, recurrence rate and suicide rate are high. Schizophrenia and depression have some of the same and different clinical features as the most common two psychiatric disorders. In recent years, functional magnetic resonance (MRI) has found that abnormal connections in different brain regions of the brain may be a pathophysiological mechanism of mental illness. To explore the mechanism of information flow and integration among different brain regions, it is of great significance to understand the pathophysiological mechanism of mental illness. In particular, the concept of cognitive core network model has given more attention to exploring the commonness and specificity among diseases, and also suggests that various mental disorders may be due to the abnormal interaction of endogenous and foreign information. The model considers that the default network, highlighting the abnormal connection pattern between the network and the central executive network, is the core of the psychopathology. However, there has so far been little literature on the existence of some of the same or different abnormal connections between these three core networks. In this paper, as the starting point and the landing point, the default network is explored, the connection mode between the network and the central executive network is highlighted, the functional connection analysis and the Granger causality analysis are used to evaluate the information integration and information exchange among the networks, respectively. The focus is on the connection of the two diseases with the abnormal change in the direction and the reverse direction. In this study,20 of the 20 tested resting-state magnetic resonance data in each group of the patients with schizophrenia, the patients with depression and the healthy control group were collected. First, using the prior information, it is determined that the default network and the central execution network core node, i.e., the back-and back-lateral prefrontal cortex areas. The K-means clustering method based on the whole-brain functional linkage map is used to divide the brain island into three subareas, so as to locate the core node _ dorsal forebrain island of the highlight network. Then, the spatial distribution of the three core networks is located using the function connection analysis based on the seed point. Then the functional connection and the effective connection of each voxel in the seed point and the core network are calculated, and the functional integration and the information transfer of different brain intervals are respectively described. Focus on the connection pattern between the two diseases in the same direction and in the reverse direction. Finally, the relationship between the change of the connection and the clinical data was explored by the analysis of the partial correlation. In this paper, it is found that the connection between the three core networks is in the same direction and in the reverse mode. At the functional connection level: there is a reverse-change function connection between the default mode network and the central execution network. The functional connection between the dorsolateral prefrontal cortex and the medial prefrontal cortex, as well as the functional connection of the back and lower back-back delta regions, showed a significant increase in the schizophrenia group compared to the normal control group, while a significant decrease in the depression group. The functional connection of this reverse change may stem from the abnormal "self-reference" function, thus showing different symptoms: too many self-attribution in the depression and the excessive or even missing self-awareness in the schizophrenia. In the effective connection network level: the effective connection of the default network and the highlight network of the patients with schizophrenia and depression is found, that is, the effective connection between the two patient groups from the back button to the back-side forebrain island and the top-top interface area is enhanced compared with the normal control group; And the effective connection of the back-back belt from the back-side forebrain island to the back-back belt is reduced. This effective connection to the change may reflect the parallel defect of the dynamic information interaction between the default network and the highlight network between the two diseases, possibly related to a common cognitive and emotional damage between the two diseases.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R749

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