IJPN International Journal of Psychiatry and Neurology 2166-5788 Scientific Research Publishing 10.12677/IJPN.2023.122002 IJPN-65403 IJPN20230200000_95210146.pdf 医药卫生 双相抑郁认知功能损害磁共振成像研究进展 Research Progress in Magnetic Resonance Imaging of Cognitive Impairment in Bipolar Depression 孟璞 2 1 海云 2 3 1 温州医科大学精神医学学院,浙江 温州 温州医科大学精神医学学院,浙江 温州;温州医科大学附属康宁医院,浙江省精神心理疾病临床医学研究中心,浙江 温州 null 17 05 2023 12 02 17 25 © Copyright 2014 by authors and Scientific Research Publishing Inc. 2014 This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

双相抑郁障碍(BDD)是一类重型精神障碍。患者以情绪不稳定为临床特点,主要表现为情绪低落与情感高涨交替出现或同时出现。此外,部分患者也同时存在认知功能损害,而且药物治疗对恢复认知功能见效甚微。物理治疗,包括重复经颅磁刺激,在短时间内可以部分改善双相障碍患者的认知功能,但难以长期维持。存在这些挑战是因为我们尚未充分了解该病的发病机制。磁共振成像是一种无创性的脑影像学技术,已经被广泛地应用于包括双相障碍在内的精神疾病的临床和实验研究。本文较系统地复习了近年来利用磁共振成像术研究双相抑郁障碍的临床研究,并扼要概括了研究进展。 Bipolar Depressive Disorder (BDD) is a severe mental disorder. Patients with emotional instability as clinical characteristics, the main manifestations of low mood and high mood occur alternately or simultaneously. In addition, some patients also have cognitive impairment, and drug therapy has little effect on restoring cognitive function. Physical therapy, including repetitive trans-cranial magnetic stimulation, can partially improve cognitive function in patients with BDD in the short term, but is difficult to maintain in the long term. These challenges exist because we do not yet fully understand the pathogenesis of the disease. Magnetic Resonance Imaging (MRI) is a noninvasive brain imaging technique that has been widely used in clinical and experimental studies of mental disorders, including BDD. This review systematically reviews the recent clinical studies with BDD patients using various kinds of MRI and briefly summarizes the research progress.

双相抑郁,多模态磁共振,默认模式网络,皮层厚度,各向异性分数,Bipolar Depression Multimode Magnetic Resonance Default Mode Network Cortical Thickness An-isotropic Fraction
摘要

双相抑郁障碍(BDD)是一类重型精神障碍。患者以情绪不稳定为临床特点,主要表现为情绪低落与情感高涨交替出现或同时出现。此外,部分患者也同时存在认知功能损害,而且药物治疗对恢复认知功能见效甚微。物理治疗,包括重复经颅磁刺激,在短时间内可以部分改善双相障碍患者的认知功能,但难以长期维持。存在这些挑战是因为我们尚未充分了解该病的发病机制。磁共振成像是一种无创性的脑影像学技术,已经被广泛地应用于包括双相障碍在内的精神疾病的临床和实验研究。本文较系统地复习了近年来利用磁共振成像术研究双相抑郁障碍的临床研究,并扼要概括了研究进展。

关键词

双相抑郁,多模态磁共振,默认模式网络,皮层厚度,各向异性分数

Research Progress in Magnetic Resonance Imaging of Cognitive Impairment in Bipolar Depression<sup> </sup>

Mengpu Wang1, Haiyun Xu1,2*

1School of Mental Health, Wenzhou Medical University, Wenzhou Zhejiang

2Zhejiang Provincial Clinical Research Center for Mental Disorder, The Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou Zhejiang

Received: Apr. 8th, 2023; accepted: May 8th, 2023; published: May 17th, 2023

ABSTRACT

Bipolar Depressive Disorder (BDD) is a severe mental disorder. Patients with emotional instability as clinical characteristics, the main manifestations of low mood and high mood occur alternately or simultaneously. In addition, some patients also have cognitive impairment, and drug therapy has little effect on restoring cognitive function. Physical therapy, including repetitive trans-cranial magnetic stimulation, can partially improve cognitive function in patients with BDD in the short term, but is difficult to maintain in the long term. These challenges exist because we do not yet fully understand the pathogenesis of the disease. Magnetic Resonance Imaging (MRI) is a noninvasive brain imaging technique that has been widely used in clinical and experimental studies of mental disorders, including BDD. This review systematically reviews the recent clinical studies with BDD patients using various kinds of MRI and briefly summarizes the research progress.

Keywords:Bipolar Depression, Multimode Magnetic Resonance, Default Mode Network, Cortical Thickness, Anisotropic Fraction

Copyright © 2023 by author(s) and beplay安卓登录

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

http://creativecommons.org/licenses/by/4.0/

1. 引言

双相抑郁障碍(BDD)是一类严重的精神障碍。患者以情绪不稳定为特点,具体表现为抑郁发作与躁狂或轻躁狂发作交替出现或同时出现。与此同时,患者往往伴随记忆力减退、执行功能下降、注意力受损和抽象思维能力下降等认知功能损害的表现,对患者的正常生活造成非常不利的影响。更不幸的是,BDD患者是一个庞大的人群,2011年的一项流行病学调查发现,全球人群BDD患病率高达2.4% ‎[ 1 ] 。药物治疗虽然能够显著改善BDD患者的临床症状,但是长期疗效尚不理想。患者出院或停药后往往病情复发,且复发次数与预后相关,特别是认知功能障碍 ‎[ 2 ] 。BDD患者认知功能的多个维度均受损害,而且在躁狂发作期和抑郁发作期,甚至缓解期都存在认知障碍 ‎[ 3 ] 。一项荟萃分析发现,BDD一级亲属相对健康人群也存在不同程度的认知功能下降 ‎[ 4 ] ‎[ 5 ] 。

BDD的难治性不仅是由于该疾病的复杂性,更是因为其特殊性。其他精神疾病的表型都可以利用动物模型进行模拟或复制,但是动物模型无法复制BDD病人的抑郁发作与躁狂或轻躁狂发作交替出现的特征。幸运的是,随着医学影像技术的发展和进步,我们已经能够应用无创性神经影像学技术(如磁共振成像术,MRI)于精神疾病患者,开展临床工作和实验研究,探究该疾病的发生、发展、治疗、和转归。

MRI是基于质子的物理特性。当给人体施加一个外在磁场时,机体组织中的质子会发生绕轴旋转,并且绕轴旋转的方向与磁场方向相同,这个过程称为进动。此时,如果施加外在射频脉冲,质子吸收能量后从低能级转变为高能级,当撤掉脉冲信号时,高能级的水分子回到初始低能级状态,这个过程叫做弛豫。在弛豫的过程中,于受检器官周围放置的线圈会采集到感应电流,后者经过复杂的后期处理,生成用灰度表示的磁共振图像 ‎[ 6 ] 。MRI图像能够清晰地展示机体组织的结构变化,具有无创、便捷和高效的特点。经过后期图像处理工具处理,可以生成很多量化的指标,如皮层厚度、体积、曲率和表面积。也可以分析皮层下核团,并研究不同脑区、皮层结构和皮层下核团之间的联系,以及它们与临床症状之间的关系。现阶段广泛应用的MRI技术包括结构磁共振成像(Structural Magnetic Resonance Imaging, sMRI)、静息态功能磁共振成像(Resting-State Functional Magnetic Resonance Imaging, rs-fMRI)、任务态功能磁共振成像(Task Functional Magnetic Resonance Imaging, task-fMRI)、弥散张量成像(Diffusion Tensor Imaging, DTI),以及磁共振波谱成像(Magnetic Resonance Spectroscopy Imaging, MRS)等。本文较系统地复习了近年来利用磁共振成像术研究双相抑郁障碍的临床研究,并扼要概括了研究进展,表1汇总了各个部分的主要结果。

Abnormal changes of BDD patients in different modalities of MR
双相抑郁患者默认模式网络异常 DMN网络内部及其与其他脑网络之间的功能连接异常是双相障碍的一种特异性表现
双相抑郁障碍患者大脑皮层厚度的改变 双相障碍患者前额叶皮层存在广泛性变薄
双相抑郁障碍患者脑白质完整性下降 胼胝体白质完整性受损与双相障碍患者认知受损密切相关
双相抑郁障碍患者腹侧– 情感和背侧–认知环路功能异常 双相障碍患者认知环路的异常改变
双相抑郁障碍患者脑代谢变化 谷氨酸能神经元在双相障碍认知损害的过程中发挥重要作用

表1. BDD患者在不同模态MRI中的异常改变

2. 双相抑郁患者默认模式网络异常

fMRI能够以体素为单位捕捉到脑组织中血氧含量随着时间的变化,这个信号被称为BOLD时间序列,直接反映了脑功能的变化情况。rs-fMRI不需要患者执行任何任务,也不需要额外的刺激来激活脑神经元,旨在观察安静时脑功能网络的异常变化与疾病的关系,目前被广泛用于许多脑疾病的基础和临床研究 ‎[ 7 ] 。

在诸多的脑功能网络当中,默认模式网络(Default Mode Network, DMN)越来越吸引人们的兴趣。DMN是一个人在安静状态或自我思考状态时处于活跃状态的脑网络,因此很适合采用静息态的方法进行研究 ‎[ 8 ] 。DMN大致分为三个主要部分:腹内侧前额叶皮层(Ventro Medial Prefrontal Cortex, vmPFC)、背内侧前额叶皮层(Dorsal Medial Prefrontal Cortex, dmPFC)、后扣带皮层(Posterior Cingulate Cortex, PCC)及邻近的楔前叶和外侧顶叶皮层(Brodmann 39区)。PCC和内侧楔前叶是DMN的突出特征,也被认为是DMN的核心所在。DMN参与多种认知和情感功能,如情绪处理、自我参照心理活动、对先前经历的回忆,并可能在注意力要求高的任务中发挥调节作用 ‎[ 9 ] 。vmPFC构成DMN的前部,主要参与调节社会行为、情绪调节、执行功能和控制过程 ‎[ 10 ] 。PCC、内侧楔前叶和外侧顶叶皮层构成后DMN,主要参与注意调节、意识、心理意象和情景记忆过程 ‎[ 11 ] 。

BDD患者存在DMN功能障碍。图论分析发现,DMN存在全局属性的异常 ‎[ 12 ] 。一项研究采用11个种子点,通过与全脑的功能连接分析DMN的变化,发现PCC与眶额叶的功能连接增强,同时与楔前叶之间的功能连接减弱,并且DMN的三个子系统之间出现了功能连接减弱 ‎[ 13 ] 。也有报道BDD患者的DMN与前额叶皮质、纹状体、和小脑等脑区的功能连接减弱 ‎[ 14 ] ‎[ 15 ] 。Magioncalda及其同事的研究发现,在BDD患者中,扣带皮层内部信息传递紊乱,导致DMN与凸显网络之间的平衡被打破。DMN的前部与凸显网络之间的连接异常使平衡点移动至DMN,导致过度关注自身内在想法,缺乏转变为行动的动力;DMN自身前后部的连接异常使平衡点移动至凸显网络,导致过度关注外部信息并且从想法到行为的转变能力增强,这可能与患者过度悲伤或兴奋的极端情绪密切相关 ‎[ 16 ] 。

一项研究在BDD患者和单相抑郁患者之间进行了基于DMN功能连接的机器学习辨别分析,结果表明,发生在DMN中的改变可能是BDD的一种特异性表现 ‎[ 17 ] 。一项类似的研究,通过检测BDD患者和他们的近亲属DMN的功能连接,发现患者与近亲属的DMN均存在前额叶区域节点的异常属性改变 ‎[ 18 ] 。然而,也有一些研究未发现BDD患者的DMN异常 ‎[ 19 ] ‎[ 20 ] 。这些不一致的结果可能归咎于技术原因,因为rs-fMRI只能截取一段较短的时间进行分析。但是,人脑在一天的活动中存在自主性的节律,我们几乎不能采集到任何受试者一整天的BOLD信号,这也是静息态研究方法的缺点之一 ‎[ 21 ] 。脑电研究方法可能是一个很好的补充。其他原因还可能包括患者的病程不同、药物或物理治疗的影响、数据采集参数和后期处理工具的不同等方面。

3. 双相抑郁障碍患者大脑皮层厚度的改变

皮层厚度指软脑膜表面到白质外表面之间的距离,是反应皮层完整性的一个重要指标 ‎[ 22 ] ,在探索脑形态学与疾病症状之间的关系中被大量地应用 ‎[ 23 ] 。皮层厚度取决于神经元和胶质细胞的数量和空间分布 ‎[ 24 ] 。一项荟萃分析报道了BDD患者存在多个脑区的皮层厚度变薄,主要包括岛叶、眶额叶和内侧前额叶 ‎[ 25 ] ,在另一研究中,BDD患者的颞叶和内侧枕叶皮层变薄 ‎[ 26 ] 。也有一些阴性结果的报道,未发现BDD患者脑区皮层厚度异常 ‎[ 27 ] ‎[ 28 ] ,甚至有一些相反的研究结果,即BDD患者脑皮层厚度增加,这些脑区主要分布在外侧面 ‎[ 29 ] ,包括右侧颞中回 ‎[ 30 ] 、颞叶和顶叶 ‎[ 31 ] 。这些不一致的研究结果可能与不同研究中患者的病程、样本量、药物治疗、磁共振主磁场强度、后期分析工具等诸多因素的不同有关。

相对于动物而言,人类拥有独一无二的前额叶区域,无论是体积,还是质量占比,都远超动物 ‎[ 32 ] 。据报道,BDD患者前额叶皮层厚度减小 ‎[ 26 ] ‎[ 33 ] 。一项荟萃分析报道BDD患者眶额叶皮层变薄,该改变与患者决策能力损害存在相关性 ‎[ 34 ] 。类似地,也有荟萃分析报道BDD患者眶额叶皮质体积减小 ‎[ 35 ] ,该结果在一项尸检研究中得到了进一步的证实 ‎[ 36 ] 。早期的一项荟萃分析报道了BDD患者存在第三脑室和两侧侧脑室体积增大,同时伴随脑总体积和白质体积下降,但灰质体积未显著改变 ‎[ 37 ] 。

与上述BDD患者皮层厚度变薄的发现相关,重复经颅磁刺激通过刺激左右两侧的背外侧前额叶皮质改善BDD患者的抑郁表现。治疗过程中发现重复经颅磁刺激不仅能够显著改善抑郁症状,认知功能也会部分恢复。与背外侧前额叶皮层(Dorsolateral Prefrontal Cortex, DLPFC)类似,vmPFC也可以作为重复经颅磁刺激刺激的靶区所在,BDD患者在这个脑区皮层厚度变薄 ‎[ 38 ] 和体积下降 ‎[ 39 ] 。一项基于BDD患者一级亲属的研究发现,被试的额下回厚度和体积降低 ‎[ 40 ] 。另外一项纵向观察发现,BDD患者的躁狂发作次数与前额叶皮层变薄相关 ‎[ 41 ] 。

4. 双相抑郁障碍患者脑白质完整性下降

众所周知,人脑存在错综复杂的白质纤维,遍布脑的每个区域。DTI可以在活体脑组织中直接检测白质纤维分布,在精神疾病研究中有着非常广泛的应用 ‎[ 42 ] 。基于白质束的空间统计方法(Tract-Based Spatial Statistics, TBSS)是目前常用的研究方法之一,旨在通过全部的受试者DTI扫描数据构建全脑白质束架构并计算各白质束的分数各向异性值(FA) ‎[ 43 ] 。FA值是反应水分子在脑组织中扩散情况的一个综合指标,取决于水分子所在组织的结构特征。一般而言,神经纤维存在明显的方向性,所以拥有较高的FA值。在脑脊液中水分子在各个方向上都能自由扩散,所以FA值趋近于0。灰质结构比较复杂,FA值介于白质和脑脊液之间 ‎[ 44 ] 。白质纤维的FA值降低提示纤维的完整性受损。相对于传统的基于全脑体素分析或感兴趣区的方法,TBSS的精准性更好,因而在精神障碍的研究中应用十分广泛。

DTI研究发现,BDD患者的部分脑区白质纤维完整性受损,主要分布在胼胝体 ‎[ 45 ] 、内囊、前扣带回和后扣带回 ‎[ 45 ] 等区域。与情绪加工有关的脑区也存在显著的FA值降低,主要包括钩状束 ‎[ 46 ] ‎[ 47 ] 、丘脑前放射束 ‎[ 48 ] 、扣带回 ‎[ 49 ] 等。考虑到BDD患者主要以情绪不稳定为主要临床表现,伴随认知能力下降,可以认为上述区域在情绪调控和认知加工的过程中发挥重要作用。当这些白质结构的完整性受损时脑功能便会受到影响,因而出现一系列复杂的临床综合征。然而,也有一些早期的研究结果发现,BDD患者某些脑白质FA值增加 ‎[ 50 ] ‎[ 51 ] 。目前尚不清楚FA值增加的原因,可能是与后期处理工具包可能存在算法上的缺点,或患者的选择偏差有关。

胼胝体是连接左右大脑半球的重要结构,是情绪、认知、运动和感觉等信息整合的关键节点所在。胼胝体前部的区域整合左右两侧前额叶、前扣带回以及岛叶等脑区的信息,这些区域的功能与舒缓情绪存在关联,因此可能与BDD患者的临床表现密切相关。BDD患者的认知功能损害是多方面的,执行功能似乎是其中比较突出的一个表现,其他如言语功能、短时和长时记忆、注意力、抽象思维等也存在不同程度的下降。这些认知的改变可能与胼胝体和其他脑白质的完整性受损有关 ‎[ 52 ] 。既往的一项临床研究发现,胼胝体FA值可以预测认知功能的衰退 ‎[ 53 ] 。基础研究也同样证实了这个观点 ‎[ 54 ] 。

5. 双相抑郁障碍患者腹侧–情感和背侧–认知环路功能异常

大脑特定的脑区均有其独特的功能,任务态磁共振成像能更好地发现不同脑区与其特定功能之间的相关性。受试者在执行不同的任务时,其特定脑区的神经元兴奋或活动水平升高,因而消耗更多的氧,同时释放更多的二氧化碳,后者引发该脑区的小血管扩张伴随血流量增加,同时血氧饱和度升高、二氧化碳水平降低。这些动态变化均可以被功能磁共振成像术捕捉到,经后处理呈现为BOLD信号。一项在抑郁障碍患者中进行的研究发现,右侧扣带回在负性表情的刺激下神经元被激活 ‎[ 55 ] 。类似的研究发现,海马、杏仁核与颞下回皮层等区域在掩盖悲伤和快乐的表情时表现出更强的激活,内侧额叶皮层、眶额叶皮层与前颞叶皮层在内的脑区在患者面对悲伤和快乐的表情时出现明显的激活 ‎[ 56 ] 。一项在BDD患者中的研究发现,与健康组相比,BDD患者海马旁回在受到负性和中性表情的刺激时显示激活降低;与抑郁组对比,BDD患者左侧楔前叶对中性表情的刺激显示出激活降低 ‎[ 57 ] 。上述发现表明,BDD患者对于外在表情的识别和加工能力减退,这可能与认知功能受损有着内在的关联。

王工书等人通过计算任务度分区的方法来评估BDD患者不同脑区对不同任务的反应情况,结果表明,带状盖网络、DMN和额顶网络存在很高的任务区分度 ‎[ 58 ] 。结合上述静息态磁共振关于DMN的分析,可以进一步推断DMN功能受损对认知功能产生的不利影响。一项在儿童BDD中的任务态磁共振研究发现,腹侧–情感和背侧–认知环路的异常,这可能是BDD患者认知受损的关键通路所在 ‎[ 59 ] 。综合目前的研究结果,BDD患者脑网络功能异常导致的认知功能受损可能会影响对外在刺激做出正确的判断,进而产生自身情绪不稳定的临床表现。

6. 双相抑郁障碍患者脑代谢变化

BDD患者情感和认知功能的异常与脑内的代谢改变存在密切关联。磁共振波谱成像(MRS)是一种无创性检测活体组织内化学成分的方法,其本质是不同的化合物在强磁场作用下存在不同的化学位移。我们可以借此来明确各个脑区内的代谢产物的组成,并计算其浓度。MRS在人脑中主要用于检测生化代谢产物,包括:N-乙酰天冬氨酸(NAA)、谷氨酸(Glu)、谷氨酰胺(Gln)、谷氨酸和谷氨酰胺复合物(Glx)、胆碱复合物(Cho)、肌醇(ml)、肌酸(Cr)和γ-氨基丁酸(GABA)等。目前,对于BDD患者脑内代谢异常的研究主要集中在前额叶和边缘系统等区域内。

既往的研究报道了BDD患者各个脑区内存在广泛的NAA浓度降低,伴随Glu、Gln和Glx的浓度升高。一项在缓解期BDD患者的MRS研究确认了缓解期认知功能损害,同时报道了BDD患者在DLPFC和海马体中存在NAA浓度显著降低并且与认知功能损害相关;另外,Glu、ml和Gln浓度在海马体中显著升高 ‎[ 60 ] 。NAA直接反映了神经元的活性,其浓度的降低也可能表示这个区域内神经元数量的下降,这与BDD的神经退行性假说相符 ‎[ 61 ] 。考虑到前额叶在认知功能中的重要作用,神经元数量减少将会直接对正常认知功能的维持产生不利影响 ‎[ 62 ] 。然而,也有一些研究发现,缓解期BDD患者在前额叶皮质不存在NAA的显著改变 ‎[ 63 ] ,这可能与缓解的时间长短、药物或神经营养剂的使用有关。同时,也说明了人脑NAA的代谢随着BDD患者病情的改善而逐步恢复到正常水平,从而发挥生物标记物的功能。

扣带回是边缘系统的重要组成部分,并且在维持认知功能方面有着复杂的作用,一项聚焦前扣带回MRS研究的荟萃分析报道了BDD患者Glu、Gln、Cho和Cr的浓度都升高。Cho的升高被认作是BDD的一个特异性改变,Gln浓度的升高与心境的恢复有关 ‎[ 64 ] 。既往的一些研究报道了Glx在成人和儿童BDD患者的多个脑区内均出现显著升高 ‎[ 65 ] ‎[ 66 ] 。然而,也有BDD的研究结果发现,Glx浓度未发生变化,甚至是显著降低 ‎[ 67 ] 。Jett等人发现,增加大鼠的Glu和GABA的浓度能够显著改善认知功能 ‎[ 68 ] ,这与之前一项临床研究 ‎[ 69 ] 的结果一致。Huber的研究发现,前扣带皮层中GABA浓度越高在很大程度上提示认知表现更好 ‎[ 70 ] 。这个过程可能是由于GABA能够维持相对稳定的情绪状态,从而保证了认知功能的各个维度的完整性。

7. 结论

综上所述,脑影像学技术已经成为研究BDD的主要方法,而且取得了较大的进展。前额叶皮层、内侧颞叶皮层和边缘系统是研究的热点,与健康对照相比,BDD患者的皮层厚度、生化代谢和BOLD均存在异常。这些脑区的结构和功能异常改变是BDD情感症状和认知受损可能的机制所在,特别是边缘系统中的白质异常改变,可能直接影响人脑各个区域之间的信息传递。DMN在情感的调控和认知功能维持中起关键作用,BDD患者存在明确的DMN功能受损和信息加工处理能力下降,对人脑的情感和认知活动造成不利影响。因此,改善DMN的功能是治疗BDD患者的一个新视角。

文章引用

王孟璞,许海云. 双相抑郁认知功能损害磁共振成像研究进展Research Progress in Magnetic Resonance Imaging of Cognitive Impairment in Bipolar Depression[J]. 国际神经精神科学杂志, 2023, 12(02): 17-25. https://doi.org/10.12677/IJPN.2023.122002

参考文献 References Merikangas, K.R., Jin, R., He, J.-P., et al. (2011) Prevalence and Correlates of Bipolar Spectrum Disorder in the World Mental Health Survey Initiative. Archives of General Psychiatry, 682, 41-51.
https://doi.org/10.1001/archgenpsychiatry.2011.12
Martinez-Aran, A., Vieta, E., Torrent, C., et al. (2007) Functional Out-come in Bipolar Disorder: The Role of Clinical and Cognitive Factors. Bipolar Disorders, 9, 103-113.
https://doi.org/10.1111/j.1399-5618.2007.00327.x
Elias, L.R., Miskowiak, K.W., Vale, A.M.O., et al. (2017) Cog-nitive Impairment in Euthymic Pediatric Bipolar Disorder: A Systematic Review and Meta-Analysis. Journal of the American Academy of Child and Adolescent Psychiatry, 56, 286-296.
https://doi.org/10.1016/j.jaac.2017.01.008
Miskowiak, K.W., Kjærstad, H.L., Meluken, I., et al. (2017) The Search for Neuroimaging and Cognitive Endophenotypes: A Critical Systematic Review of Studies Involving Unaffected First-Degree Relatives of Individuals with Bipolar Disorder. Neuroscience and Biobehavioral Reviews, 73, 1-22.
https://doi.org/10.1016/j.neubiorev.2016.12.011
Bora, E., Yucel, M. and Pantelis, C. (2009) Cognitive Endophenotypes of Bipolar Disorder: A Meta-Analysis of Neuropsychological Deficits in Euthymic Patients and Their First-Degree Relatives. Journal of Affective Disorders, 113, 1-20.
https://doi.org/10.1016/j.jad.2008.06.009
Ray H. Hashemi, William G. Bradley Jr., Christo-pher J. Lisanti. MRI基础[M]. 第2版. 尹建忠, 译. 天津: 天津科技翻译出版公司, 2004. 孙尧, 王颖, 黄力. 双相情感障碍的静息态功能MRI研究进展[J]. 磁共振成像, 2016(1): 77-80. Tryon, W.W. (2014) Chapter 3: Core Network Principles: The Explanatory Nucleus. In: Tryon, W.W., Ed., Cognitive Neuroscience and Psychotherapy, Academic Press, Cambridge, 125-222.
https://doi.org/10.1016/B978-0-12-420071-5.00003-X
Raichle, M.E. (2015) The Brain’s Default Mode Network. Annual Review of Neuroscience, 38, 433-447.
https://doi.org/10.1146/annurev-neuro-071013-014030
Damasio, H., Grabowski, T., Frank, R., Galaburda, A.M. and Damasio, A.R. (1994) The Return of Phineas Gage: Clues about the Brain from the Skull of a Famous Patient. Science, 264, 1102-1105.
https://doi.org/10.1126/science.8178168
Leech, R. and Sharp, D.J. (2013) The Role of the Posterior Cingulate Cortex in Cognition and Disease. Brain, 137, 12-32.
https://doi.org/10.1093/brain/awt162
Wang, Y., Wang, J., Jia, Y., et al. (2017) Topologically Convergent and Divergent Functional Connectivity Patterns in Unmedicated Unipolar Depression and Bipolar Disorder. Translational Psychiatry, 7, e1165.
https://doi.org/10.1038/tp.2017.117
Yang, Y., Cui, Q., Lu, F., et al. (2021) Default Mode Network Subsystem Alterations in Bipolar Disorder during Major Depressive Episode. Journal of Affective Disorders, 281, 856-264.
https://doi.org/10.1016/j.jad.2020.11.049
Wang, Y., Zhong, S., Jia, Y., Sun, Y., Wang, B., Liu, T., Pan, J. and Huang, L. (2016) Disrupted Resting-State Functional Connectivity in Nonmedicated Bipolar Disorder. Radiology, 280, 529-536.
https://doi.org/10.1148/radiol.2016151641
Wang, Y., Zhong, S., Chen, G., et al. (2018) Altered Cerebellar Functional Con-nectivity in Remitted Bipolar Disorder: A Resting-State Functional Magnetic Resonance Imaging Study. Australian & New Zealand Journal of Psychiatry, 52, 962-971.
https://doi.org/10.1177/0004867417745996
Magioncalda, P., Martino, M., Conio, B., et al. (2015) Functional Connectivity and Neuronal Variability of Resting State Activity in Bipolar Disorder—Reduction and Decoupling in Anterior Cortical Midline Structures. Human Brain Mapping, 36, 666-682.
https://doi.org/10.1002/hbm.22655
Rive, M.M., Redlich, R., Schmaal, L., Marquand, A.F., Dannlowski, U., Grotegerd, D., Veltman, D.J., Schene, A.H. and Ruhé, H.G. (2016) Distinguishing Medication—Free Subjects with Unipolar Disorder from Subjects with Bipolar Disorder: State Matters. Bipolar Disor-ders, 18, 612-623.
https://doi.org/10.1111/bdi.12446
Khadka, S., Meda, S.A., Stevens, M.C., et al. (2013) Is Aberrant Functional Connectivity a Psychosis Endophenotype? A Resting State Functional Magnetic Resonance Imaging Study. Biological Psy-chiatry, 74, 458-466.
https://doi.org/10.1016/j.biopsych.2013.04.024
Yip, S.W., Mackay, C.E. and Goodwin, G.M. (2014) Increased Tempo-ro-Insular Engagement in Unmedicated Bipolar II Disorder: An Exploratory Resting State Study Using Independent Component Anal-ysis. Bipolar Disorders, 16, 748-755.
https://doi.org/10.1111/bdi.12206
Nguyen, T.T., Kovacevic, S., Dev, S.I., et al. (2017) Dynamic Functional Connectivity in Bipolar Disorder Is Associated with Executive Function and Processing Speed: A Preliminary Study. Neuropsychology, 31, 73-83.
https://doi.org/10.1037/neu0000317
Logan, R.W. and McClung, C.A. (2019) Rhythms of Life: Circadian Disruption and Brain Disorders across the Lifespan. Nature Reviews Neuroscience, 20, 49-65.
https://doi.org/10.1038/s41583-018-0088-y
Dickerson, B.C. and Wolk, D.A. (2012) MRI Cortical Thickness Biomarker Predicts AD-Like CSF and Cognitive Decline in Normal Adults. Neurology, 78, 84-90.
https://doi.org/10.1212/WNL.0b013e31823efc6c
Dickerson, B.C., Fenstermacher, E., Salat, D.H., et al. (2008) Detection of Cortical Thickness Correlates of Cognitive Performance: Reliability across MRI Scan Sessions, Scanners, and Field Strengths. Neu-roImage, 39, 10-18.
https://doi.org/10.1016/j.neuroimage.2007.08.042
Bennett, M.R. (2011) The Prefrontal-Limbic Network in Depression: A Core Pathology of Synapse Regression. Progress in Neurobiology, 93, 457-467.
https://doi.org/10.1016/j.pneurobio.2011.01.001
Zhu, Z., Zhao, Y., Wen, K., et al. (2022) Cortical Thickness Abnormalities in Patients with Bipolar Disorder: A Systematic Review and Meta-Analysis. Journal of Affective Disorders, 300, 209-218.
https://doi.org/10.1016/j.jad.2021.12.080
Abé, C., Ekman, C.-J., Sellgren, C., et al. (2016) Cortical Thickness, Volume and Surface Area in Patients with Bipolar Disorder types I and II. Journal of Psychiatry & Neuroscience, 41, 240-250.
https://doi.org/10.1503/jpn.150093
Shahab, S., Mulsant, B.H., Levesque, M.L., et al. (2019) Brain Structure, Cognition and Brain Age in Schizophrenia, Bipolar Disorder, and Healthy Controls. Neuropsychopharmacology, 44, 898-906.
https://doi.org/10.1038/s41386-018-0298-z
Rimol, L.M., Hartberg, C.B., Nesvåg, R., et al. (2010) Cortical Thickness and Subcortical Volumes in Schizophrenia and Bipolar disorder. Biological Psychiatry, 68, 41-50.
https://doi.org/10.1016/j.biopsych.2010.03.036
Bansal, R., Hao, X., Liu, F., et al. (2013) The Effects of Changing Water Content, Relaxation Times, and Tissue Contrast on Tissue Segmentation and Measures of Cortical Anatomy in MR Images. Magnetic Resonance Imaging, 31, 1709-1730.
https://doi.org/10.1016/j.mri.2013.07.017
Qiu, A., Gan, S.C., Wang, Y., et al. (2013) Amygdala-Hippocampal Shape and Cortical Thickness Abnormalities in First-Episode Schizophrenia and Mania. Psychological Medicine, 43, 1353-1363.
https://doi.org/10.1017/S0033291712002218
Hulshoff Pol, H.E., van Baal, G.C.M., Schnack, H.G., et al. (2012) Overlap-ping and Segregating Structural Brain Abnormalities in Twins with Schizophrenia or Bipolar Disorder. Archives of General Psychiatry, 69, 349-359.
https://doi.org/10.1001/archgenpsychiatry.2011.1615
Carlén, M. (2017) What Constitutes the Prefrontal Cortex? Science, 358, 478-482.
https://doi.org/10.1126/science.aan8868
Niu, M., Wang, Y., Jia, Y., et al. (2017) Common and Specific Abnormalities in Cortical Thickness in Patients with Major Depressive and Bipolar Disorders. EBioMedicine, 16, 162-171.
https://doi.org/10.1016/j.ebiom.2017.01.010
Happaney, K., Zelazo, P.D. and Stuss D.T. (2004) Development of Orbitofron-tal Function: Current Themes and Future Directions. Brain and Cognition, 55, 1-10.
https://doi.org/10.1016/j.bandc.2004.01.001
Yu, H., Meng, Y.-J., Li, X.-J., et al. (2019) Common and Distinct Patterns of Grey Matter Alterations in Borderline Personality Disorder and Bipolar Disorder: Voxel-Based Meta-Analysis. British Journal of Psy-chiatry, 215, 395-403.
https://doi.org/10.1192/bjp.2019.44
Rajkowska, G. (2000) Postmortem Studies in Mood Disorders Indicate Altered Num-bers of Neurons and Glial Cells. Biological Psychiatry, 48, 766-777.
https://doi.org/10.1016/S0006-3223(00)00950-1
Vita, A., De Peri, L. and Sacchetti, E. (2009) Gray Matter, White Matter, Brain and Intracranial Volumes in First-Episode Bipolar Disorder: A Meta-Analysis of Magnetic Resonance Imaging Studies. Bipolar Disorders, 11, 807-814.
https://doi.org/10.1111/j.1399-5618.2009.00759.x
Nugent, A.C., Milham, M.P., Bain, E.E., et al. (2006) Cortical Abnormal-ities in Bipolar Disorder Investigated with MRI and Voxel-Based Morphometry. NeuroImage, 30, 485-497.
https://doi.org/10.1016/j.neuroimage.2005.09.029
Anticevic, A., Brumbaugh, M.S., Winkler, A.M., et al. (2013) Global Prefrontal and Fronto-Amygdala dysconnectivity in bipolar I disorder with Psychosis History. Biological Psychiatry, 73, 565-573.
https://doi.org/10.1016/j.biopsych.2012.07.031
Roberts, G., Lenroot, R., Overs, B., et al. (2022) Accelerated Cortical Thin-ning and Volume Reduction over Time in Young People at High Genetic Risk for Bipolar Disorder. Psychological Medicine, 52, 1344-1355.
https://doi.org/10.1017/S0033291720003153
Abé, C., Ching, C.R.K., Liberg, B., et al. (2022) Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group. Biological Psychiatry, 91, 582-592.
https://doi.org/10.1016/j.biopsych.2021.09.008
Soares, J.M., Marques, P., Alves, V. and Sousa, N. (2013) A Hitchhiker’s Guide to Diffusion Tensor Imaging. Frontiers in Neuroscience, 7, Article 31.
https://doi.org/10.3389/fnins.2013.00031
Smith, S.M., Jenkinson, M., Johansen-Berg, H., et al. (2006) Tract-Based Spatial Statistics: Voxelwise Analysis of Multi-Subject Diffusion Data. NeuroImage, 31, 1487-505.
https://doi.org/10.1016/j.neuroimage.2006.02.024
Sbardella, E., Tona, F., Petsas, N., et al. (2013) DTI Measurements in Multiple Sclerosis: Evaluation of Brain Damage and Clinical Implications. Multiple Sclerosis International, 2013, Article ID: 671730.
https://doi.org/10.1155/2013/671730
Ishida, T., Donishi, T., Iwatani, J., et al.(2017) Interhemispheric Disconnectivity in the Sensorimotor Network in Bipolar Disorder Revealed by Functional Connectivity and Diffusion Tensor Imaging Analysis. Heliyon, 3, e00335.
https://doi.org/10.1016/j.heliyon.2017.e00335
Benedetti, F., Absinta, M., Rocca, M.A., et al. (2011) Tract-Specific White Matter structural disruption in patients with bipolar disorder. Bipolar Disorders, 13, 414-424.
https://doi.org/10.1111/j.1399-5618.2011.00938.x
Versace, A., Andreazza, A.C., Young, L.T., et al. (2014) Elevated Serum Measures of Lipid Peroxidation and Abnormal Prefrontal White Matter in Euthymic Bipolar Adults: Toward Peripheral Biomarkers of Bipolar Disorder. Molecular Psychiatry, 19, 200-208.
https://doi.org/10.1038/mp.2012.188
Lin, F., Weng, S., Xie, B., Wu, G. and Lei, H. (2011) Abnormal Frontal Cortex White Matter Connections in Bipolar Disorder: A DTI Tractography Study. Journal of Affective Disorders, 131, 299-306.
https://doi.org/10.1016/j.jad.2010.12.018
Emsell, L., Leemans, A., Langan, C., et al. (2013) Limbic and Callosal White Mat-ter Changes in Euthymic Bipolar I Disorder: An Advanced Diffusion Magnetic Resonance Imaging Tractography Study. Biological Psychiatry, 73, 194-201.
https://doi.org/10.1016/j.biopsych.2012.09.023
Versace, A., Almeida, J.R.C., Hassel, S., et al. (2008) Elevated Left and Reduced Right Orbitomedial Prefrontal Fractional Anisotropy in Adults with Bipolar Disorder Revealed by Tract-Based Spatial Statis-tics. Archives of General Psychiatry, 65, 1041-1052.
https://doi.org/10.1001/archpsyc.65.9.1041
Wessa, M., Houenou, J., Leboyer, M., et al. (2009) Microstructural White Matter Changes in Euthymic Bipolar Patients: A Whole-Brain Diffusion Tensor Im-aging Study. Bipolar Disorders, 11, 504-514.
https://doi.org/10.1111/j.1399-5618.2009.00718.x
Debette, S. and Markus, H.S. (2010) The Clinical Importance of White Matter Hyperintensities on Brain Magnetic Resonance Imaging: Systematic Review and Meta-Analysis. BMJ, 341, c3666.
https://doi.org/10.1136/bmj.c3666
Vemuri, P., Lesnick, T.G., Przybelski, S.A., et al. (2018) Development of a Cerebrovas-cular Magnetic Resonance Imaging Biomarker For cognitiveAging. Annals of Neurology, 84, 705-716.
https://doi.org/10.1002/ana.25346
Liu, Q., Bhuiyan, M.I.H., Liu, R., et al. (2021) Attenuating Vascular Stenosis-Induced Astrogliosis Preserves White Matter Integrity and Cognitive Function. Journal of Neuroinflammation, 18, Article No. 187.
https://doi.org/10.1186/s12974-021-02234-8
宋筱蕾, 穆新暖, 于美霞, 等. 首发轻中度抑郁症患者治疗前后脑任务态功能磁共振研究[J]. 临床放射学杂志. 2019, 38(7): 1174-1179. Victor, T.A., Furey, M.L., Fromm, S.J., et al. (2012) The Ex-tended Functional Neuroanatomy of Emotional Processing Biases for Masked Faces in Major Depressive Disorder. PLOS ONE, 7, e46439.
https://doi.org/10.1371/journal.pone.0046439
蔡溢, 李卫晖, 李则宣, 等. 抑郁症和双相抑郁患者情绪图片任务下脑功能磁共振成像研究[J]. 中华精神科杂志, 2016, 49(4): 202-209. 王工书, 任尊晓, 李丹丹, 等. 脑激活任务区分度的分析及应用研究[J]. 计算机工程与应用, 2020, 56(21): 272-278. 肖茜. 青少年双相障碍的脑结构和功能磁共振研究[D]: [博士学位论文]. 长沙: 中南大学, 2013. Gupta, R., Sood, M., Sharma, U., R.,Bhargava, Jagannathan, N.R. and Chadda, R.K. (2022) Neurochemical Correlates of Cognitive Functions in Euthymic Patients with Bipolar Disorder: 1H-MRS Study. Asian Journal of Psy-chiatry, 78, Article ID: 103273.
https://doi.org/10.1016/j.ajp.2022.103273
Zovetti, N., Rossetti, M.G., Perlini, C., Brambilla, P. and Bellani, M. (2023) Brain Ageing and Neurodegeneration in Bipolar Disorder. Journal of Affective Disorders, 323, 171-175.
https://doi.org/10.1016/j.jad.2022.11.066
Fredericks, C., Kalmar, J. and Blumberg, H. (2006) The Role of the Ventral Pre-frontal Cortex in Mood Disorders. In: Zald, D. and Rauch, S., Eds., The Orbitofrontal Cortex, Oxford Academic, Oxford, 544-577.
https://doi.org/10.1093/acprof:oso/9780198565741.003.0021
Liu, T., Wang, Y., Zhong, S., et al. (2017) A Comparison of Neurometabolitesbetween Remitted Bipolar Disorder and Depressed Bipolar Disorder: A Proton Magnetic Resonance Spectroscopy Study. Journal of Affective Disorders, 211, 153-161.
https://doi.org/10.1016/j.jad.2017.01.009
Scotti-Muzzi, E., Um-la-Runge, K. and Soeiro-de-Souza, M.G. (2021) Anterior Cingulate Cortex Neurometabolitesin Bipolar Disorder are Influenced by Mood State and Medication: A Meta-Analysis of 1H-MRS Studies. European Neuropsychopharmacology, 47, 62-73.
https://doi.org/10.1016/j.euroneuro.2021.01.096
Öngür, D., Jensen, J.E., Prescot, A.P., et al. (2008) Abnormal Glutama-tergic Neurotransmission and Neuronal-Glial Interactions in Acute Mania. Biological Psychiatry, 64, 718-726.
https://doi.org/10.1016/j.biopsych.2008.05.014
Frye, M.A., Watzl, J., Banakar, S., et al. (2007) Increased Anterior Cingulate/Medial PREFRONTAL Cortical Glutamate and Creatine in Bipolar Depression. Neuropsychopharmacology, 32, 2490-2499.
https://doi.org/10.1038/sj.npp.1301387
Port, J.D., Unal, S.S., Mrazek, D.A., et al. (2008) Metabolic Alterations in Medica-tion-Free Patients with Bipolar Disorder: A 3T CSF-Corrected Magnetic Resonance Spectroscopic Imaging Study. Psychiatry Research: Neuroimaging, 162, 113-121.
https://doi.org/10.1016/j.pscychresns.2007.08.004
Jett, J.D., Bulin, S.E., Hatherall, L.C., et al. (2017) Deficits in Cognitive Flexibility Induced by Chronic Unpredictable Stress Are Associated with Impaired Glutamate Neurotrans-mission in the Rat Medial Prefrontal Cortex. Neuroscience, 346, 284-297.
https://doi.org/10.1016/j.neuroscience.2017.01.017
Buchanan, R.J., Gjini, K., Modur, P., et al. (2016) In Vivo Measurements of Limbic Glutamate and GABA Concentrations in Epileptic Patients during Affective and Cognitive Tasks: A Microdialysis Study. Hippocampus, 26, 683-689.
https://doi.org/10.1002/hipo.22552
Huber, R.S., Kondo, D.G., Shi, X.-F., et al. (2018) Relationship of Executive Function-ing Deficits to N-acetyl Aspartate (NAA) and Gamma-Aminobutyric Acid (GABA) in Youth with Bipolar Disorder. Journal of Affec-tive Disorders, 225, 71-78.
https://doi.org/10.1016/j.jad.2017.07.052
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