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重度抑郁症及其无抽搐电休克治疗的大脑结构和功能研究
汪胜佩
Subtype博士
Thesis Advisor何晖光
2020-08-29
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword磁共振成像,重度抑郁症,无抽搐电休克治疗,大脑结构和功能可塑性, 多层图论关键词
Abstract

重度抑郁症(Major depressive disorderMDD)是一种常见的精神障碍类疾病,该疾病伴有高致残率,会对患者身心造成严重危害。无抽搐电休克疗法(Modified Electroconvulsive therapymECT)对重度抑郁症具有较好的疗效,但其治疗机制仍不明确,这在一定程度上限制了mECT的发展与应用。因此,明确mECT对抑郁症的神经作用机制不仅能够有助于了解抑郁症的发病机制,同时也为改进优化mECT乃至于新技术的开发提供科学依据。在本论文中,我们与安徽精神卫生中心首先联合招募了58名重度抑郁症患者并接受mECT的治疗,同时招募了34名年龄性别相匹配的健康对照,然后采集mECT治疗前后的重度抑郁症患者及其健康对照的多模态磁共振成像。最后,使用多模态核磁共振图像对重度抑郁症及其mECT治疗的大脑结构和功能的可塑性进行研究。论文的主要的研究工作及创新点包括以下几个方面:

首先,本文提出了不同尺度的大脑形态学和结构连接分析框架,分别从体素、感兴趣区和大脑结构网络三种尺度对重度抑郁症及其治疗的大脑结构的可塑性进行研究。研究结果发现重度抑郁症患者多个脑区受到损伤而导致灰质体积减少,而电休克治疗能够改善和修复受损的脑区。该研究表明重度抑郁症患者的边缘叶-皮层环路脑区受损,而电休克治疗可作用于该环路,改善整个环路的受损而起到抗抑郁效果。

其次,本文基于图论分析了功能子系统连接模式,探究重度抑郁症及mECT治疗的大脑功能子系统之间的功能连接的改变。研究结果发现重度抑郁症在感觉运动网络和额顶控制网络内的功能连接显著低于正常对照,高级认知网络(默认网络和扣带-鳃盖网络)与低级感觉网络(注意网络、躯体运动网络、视觉网络)的连接也显著降低。经过mECT治疗后默认网络和额顶控制网络内的连接显著增加,默认网络和低级感觉网络间连接也显著增加。上述研究结果说明重度抑郁症患者高级认知网络和低级感觉认知网络之间存在失连接,而mECT治疗后可加强高级认知网络对低级感觉认知网络的调节。

接着,本文提出了基于多层网络的模块动态特征分析的框架,探究重度抑郁症及其mECT治疗的大脑动态特性。研究发现重度抑郁症的患者的基底节网络的分离度显著增加,执行控制网络的分离度和灵活度显著降低;mECT治疗后的重度抑郁症患者的躯体运动网络的内聚度显著降低,同时语言网络的灵活度显著增加,且额中回、扣带、舌回的动态模块特征与临床参数存在显著的相关性。上述研究说明重度抑郁症患者执行控制网络灵活度的受损可能与患者对负性感官加工行为的调节作用有关,mECT治疗可改善重度抑郁症患者语言网络的损伤。

最后,本文提出了基于隐马尔科夫模型的大脑动态特性分析框架,探究了重度抑郁患者大脑微状态时间上的重配置及特异的状态转移模式。研究发现重度抑郁症患者时间上存在大脑微状态的重配置,其中,重度抑郁症患者以默认网络激活降低为表征的大脑微状态的相对占比(Fractional Occupancy)和存活时间(Lifetime)显著增加。重度抑郁症患者存在特异的状态转移模块,以及重度抑郁症相关状态转移模块与正常对照相关状态转移模块之间存在多条状态转移路径。上述研究为抑郁症大脑网络的动态环路提供了新的证据,有助于理解重度抑郁的发病机制。

Other Abstract

Major depressive disorder (MDD) is a common kind of psychogenic with a high disability rate and can cause serious harm to patients’ body and mind. Modified Electroconvulsive therapy (mECT) is a highly effective acute treatment for major depression. However, little is known about the underlying therapeutic mechanisms of mECT. Exploring the therapeutic mechanisms of mECT for major depression can further illuminate the pathogenesis of depression, improve the mECT technique, and contribute to developing a new treatment method for depression. In this paper, we recruited 58 patients with MDD, who received mECT treatment, and recruited 34 age- and gender-matched helathy controls in the Anhui Mental Health Center. Then, we acquired the multi-modal magnetic resonance imaging (MRI) of before-mECT and after-mECT MDD patients and helathy controls. Finally we used multi-modal magnetic resonance imaging (MRI) to investigate the structural and functional brain plasticity in MDD and the influence of mECT on them. The contributions of this dissertation are presented as follows:

First, we proposed a research framework with different scales of brain morphology and structural connectivity analysis. In the framework, we investigated the structural brain plasticity in MDD and the influence of mECT on them respmECTively from three scales of the voxel, the region of interest, and the brain structural network. We found that multiple brain regions of MDD were damaged and resulted in a decrease in gray matter volume and mECT can repair the damaged brain regions and resulted in an increase in gray matter volume. Our results indicated that brain regions of limbic-cortex circuit were damaged in MDD. MECT can act on the limbic-cortex circuit and improve the damaged brain regions, resulting in the antidepressant response for the MDD patients.

Second, based on the graph theory, we analyzed functional segregation of the whole-brain functional network to investigate the alteration of with- and between-system functional connectivity in MDD and the response of mECT. In this paper, we found that compared to the healthy controls, the within-system FCs of the sensorimotor network (SMN) and frontoparietal network were significantly decreased. The between-system FCs between high-order cognition network (including default mode network (DMN) and cingulo-opercular task control network (CON)) and low-order sensory and motor network (attention network (AN), visual network (VIS) and sensorimotor network (SMN)) were also significantly decreased. Compared to the before-mECT MDD, the within-system FCs of DMN and FPN were significantly increased and between-system FCs between DMN and low-order sensory and motor network were also significantly increased. Our results indicated the dis-connectivity between high-order cognition network and sensory and motor network might be potential causes of depression and mECT might enhance the regulatory function of the high-level cognitive network to the low-level sensory and motor network.

Third, we proposed a framework for the analysis of dynamic community characteristics based multi-layer network. We aimed to investigate the alteration of dynamic characteristics in MDD and the influence of mECT on them. Our found that compared to healthy controls, the disjointedness of the basal ganglia network (BG) was significantly increased in MDD, and the disjointedness and flexibility of the executive control network (ECN) were significantly decreased. Compared to the before-mECT MDD, the cohesiveness of SMN in after-mECT MDD was significantly reduced, and flexibly of language network was significantly increased. In addition, the dynamic characteristics of the middle frontal gyrus, cingulate cortex, and lingual gyrus were closely correlated to clinical parameters. Our study indicated that the impaired flexibly of ECN in MDD might be closely related the regulation of negative sensory processing and mECT could improve the impaired function of the language network in MDD.

Finally, we proposed a Hidden Markov Model (HMM) framework to investigate the dynamic characteristics of the brain and explored the temporal reconfiguration and specific state transition patterns of brain states for MDD. We found that the temporal reconfiguration of states in MDD was associated with the high-order cognition network (DMN), subcortical network (SUB), and sensory and motor networks (SMN). Further, we found the specific module of transitions was closely related to MDD, which were characterized by two opposite activation in DMN, SMN, and subcortical areas. Notably, our results provided novel insights into the dynamical circuit configuration of whole-brain networks for MDD and suggested that brain dynamics should remain a prime target for further MDD research.

Pages147
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40451
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
汪胜佩. 重度抑郁症及其无抽搐电休克治疗的大脑结构和功能研究[D]. 中国科学院自动化研究所. 中国科学院大学,2020.
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