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临床研究
基于DTI对白质微结构介导发病年龄和抑郁症严重程度的研究
王云 赵天 谢杰 王琪 李月峰

Cite this article as: Wang Y, Zhao T, Xie J, et al. The study of white matter micro-structures mediating onset age and the severity of depressive disorder based on DTI[J]. Chin J Magn Reson Imaging, 2021, 12(6): 1-4, 15.本文引用格式:王云, 赵天, 谢杰, 等. 基于DTI对白质微结构介导发病年龄和抑郁症严重程度的研究[J]. 磁共振成像, 2021, 12(6): 1-4, 15. DOI:10.12015/issn.1674-8034.2021.06.001.


[摘要] 目的 探讨抑郁症患者发病年龄、脑白质微结构及抑郁症严重程度之间的关系以及脑白质微结构在发病年龄与抑郁症严重程度之间的作用。材料与方法 本研究采用前瞻性设计。对镇江市精神卫生中心收集的60例抑郁症患者(早发性抑郁症组26例、晚发性抑郁症组34例)进行磁共振扩散张量成像(diffusion tensor imaging,DTI)扫描,并应用基于纤维束示踪的空间统计方法(tract-based spatial statistics,TBSS)进行分析。应用汉密尔顿抑郁量表(Hamilton Depression Scale,HAMD)评估抑郁症严重程度。采用两样本t检验和广义线性模型比较早发性抑郁症组和晚发性抑郁症组的白质微结构。发病年龄、差异性白质微结构、抑郁症严重程度三者的相互关系采用Pearson相关分析。采用中介效应模型探讨差异性白质微结构在发病年龄与抑郁症严重程度潜在联系中的作用。结果 早发性和晚发性抑郁症组的发病年龄与抑郁症严重程度均呈显著性正相关(r=0.512,P=0.007;r=0.435,P=0.010)。与早发性抑郁症组相比,晚发性抑郁症组左侧内囊、右侧矢状层(包括下额枕束和下纵束) FA值显著降低(P<0.05)。在控制协变量后,差异仍具有统计学意义(P<0.05)。Pearson相关分析显示在控制协变量后,抑郁症患者左侧内囊、右侧矢状层(包括下额枕束和下纵束)FA值分别与发病年龄、HAMD评分呈显著性负相关(r=-0.434,P=0.001;r=-0.594,P=0.001;r=-0.565,P=0.001;r=-0.370,P=0.004)。中介效应模型显示左侧内囊FA值在发病年龄和抑郁症严重程度之间存在显著性中介效应(ab path=0.155,SE=0.055,95% CI:0.059~0.276)。结论 左侧内囊FA值介导了抑郁症患者发病年龄对抑郁症严重程度的影响。
[Abstract] Objective To investigate the relationship among onset age, white matter (WM) micro-structures and the severity of depressive disorder and explore the effect of WM micro-structures on the association between onset age and the severity of depressive disorder. Materials andMethods Prospective design was used in this study. Sixty depressive disorder patients (26 early-onset and 34 later-onset depressive disorder groups) collected from Zhenjiang Mental Health Center underwent DTI-MRI scanning, and analysis was performed using the tract-based spatial statistics (TBSS). The severity of depressive disorder was assessed by Hamilton Depression Rating Scale (HAMD). The WM micro-structures between the early-onset and the later-onset depressive disorder groups were compared with two-sample t-test and generalized linear models. The correlation among onset age, abnormal WM micro-structures and the severity of depressive disorder were analyzed by Pearson correlation. The mediation effect models were used to explore the influence of abnormal WM micro-structures in the potential association between onset age and the severity of depressive disorder.Results The onset age was positively correlated with the severity of depressive disorder in the early-onset and later-onset depressive disorder groups (r=0.512, P=0.007; r=0.435, P=0.010). Compared with the early-onset depressive disorder group, FA values of the left internal capsule and right sagittal stratum including inferior fronto-occipital fasciculus and inferior longitidinal fasciculus significantly decreased in later-onset depressive disorder group (P<0.05). The differences remained statistically significant (P<0.05), even after controlling for covariates. Pearson correlation analysis showed that the FA values of the left internal capsule and right sagittal stratum including inferior fronto-occipital fasciculus and inferior longitidinal fasciculus were respectively significantly negatively correlated with onset age and HAMD scores (r=-0.434, P=0.001; r=-0.594, P=0.001; r=-0.565, P=0.001, r=-0.370, P=0.004) after controlling for covariates. The mediation effect model showed that FA values of the left internal capsule had a significant mediation effect on the association between onset age and the severity of depressive disorder (ab path=0.155, SE=0.055, 95% CI: 0.059—0.276).Conclusions FA values of the left internal capsule mediated the influence of onset age on the severity of depressive disorder.
[关键词] 抑郁症;发病年龄;白质微结构;抑郁症严重程度;扩散张量成像
[Keywords] depressive disorder;onset age;white matter micro-structures;the severity of depressive disorder;diffusion tensor imaging

王云    赵天    谢杰    王琪    李月峰 *  

江苏大学附属医院医学影像科,镇江 212001

李月峰,E-mail:jiangdalyf123@163.com

全体作者均声明无利益冲突。


基金项目: 国家自然科学基金 81871343 江苏省社会发展面上项目 BE2017698
收稿日期:2021-01-19
接受日期:2021-03-08
DOI: 10.12015/issn.1674-8034.2021.06.001
本文引用格式:王云, 赵天, 谢杰, 等. 基于DTI对白质微结构介导发病年龄和抑郁症严重程度的研究[J]. 磁共振成像, 2021, 12(6): 1-4, 15. DOI:10.12015/issn.1674-8034.2021.06.001.

       抑郁症是一种全球性的、普遍的精神疾病,以情感、认知、记忆和躯体症状为特征,严重危害人类健康[1-3]。发病年龄是抑郁症发展过程中关键的影响因素,发病年龄越大预示着越不利的结果,包括抑郁症状越重和持续抑郁的风险[4]。然而,发病年龄与抑郁症严重程度之间具体对应关系的潜在机制尚未明确。先前的研究已经揭示了发病年龄与大脑结构和功能、压力事件、炎症标志物、基因多态性及蛋白活性等生物学指标的联系[5-11],这些指标可能是介导发病年龄与抑郁症严重程度的潜在候选因子。其中,白质微结构作为脑组织的重要构成部分及脑功能的结构基础,在神经精神疾病机制探索的研究中一直备受关注。另外,扩散张量成像(diffusion tensor imaging,DTI)是目前唯一能在活体人脑组织显示白质纤维束的走行、方向、排列、紧密度、髓鞘化情况等信息的非侵入性脑成像技术[12-13]。因此,本研究拟应用DTI技术探讨抑郁症患者发病年龄、脑白质微结构及抑郁症严重程度之间的关系以及脑白质微结构在发病年龄与抑郁症严重程度之间的作用,旨在进一步揭示发病年龄与抑郁症严重程度之间关系的潜在机制,为抑郁症患者的预后提供科学的理论指导。

1 材料与方法

1.1 一般资料

       本研究为前瞻性研究。于2019年6月至2020年5月纳入镇江市精神卫生中心收治的拟入院的抑郁症患者。抑郁症患者入组标准:(1)符合美国精神障碍诊断与统计手册第5版的抑郁症诊断标准;(2) HAMD-17评分>7分;(3)年龄18~60岁;(4)发病年龄18~45岁;(5)性别不限;(6)右利手。排除标准:(1)有其他精神疾病史或精神病症状的终生病史;(2)有颅脑器质性病变(如脑震荡、中风、梗死、肿瘤或神经炎症性疾病);(3)有长期慢性病史(如自身免疫性疾病、心脏病、糖尿病、慢性肾脏病或肝病等);(4)有影响脑血流和代谢的情况(如高血压);(5)既往长期服用精神类药物或酗酒;(6)有抑郁症家族遗传史;(7)有MR扫描的禁忌证。共计收集完整抑郁症患者资料60例,根据发病年龄将其分为早发性抑郁症组(26例,发病年龄为18~29岁)和晚发性抑郁症组(34例,发病年龄为30~45岁)。在招募时所有受试者于江苏大学附属医院接受了MR检查,并由两名高年资的精神科医师应用汉密尔顿抑郁量表(Hamilton Depression Scale,HAMD) 17项版本评估了抑郁症严重程度[14],随后进入入院标准化治疗程序。所有受试者对研究具有知情同意权并签署书面知情同意书,研究经镇江市精神卫生中心伦理委员会审核批准(ZJJS-2019017)。

1.2 MR检查方法

       采用Siemens-Magnetom stero 3.0 T扫描仪(8通道头颅相控阵线圈)进行MRI采集。受试者佩戴降噪耳机仰卧于检查床,双目微闭,保持静息状态。受试者头部与线圈空隙用专用软垫填充固定。扫描参数:扩散敏感单次激发回波成像序列(single-shot echo-planar imaging sequence,SS-EPI),射频脉冲TR=20 500 ms,TE=103 ms,翻转角=25°,FOV=230 mm×230 mm,矩阵=128×128,扩散敏感系数(b值)=0、1000 s/mm2,扩散敏感梯度方向为25,层厚=1.2 mm,层间距=0 mm,扫描时间约20 min 12 s。

1.3 图像分析

       将所有受试者原始扫描数据以Dicom格式从工作站导出。用牛津大学脑功能核磁共振成像研究中心软件库(FSL;https://fsl.fmrib.ox.ac.uk/fsl)处理和分析数据。预处理主要步骤如下:(1)使用MRICroN软件将原始数据转换为nii格式;(2)对DTI图像进行头动和涡流伪影校正;(3)去除颅骨、头皮、脑膜等非脑组织,并得到brainmask图像;(4)进行张量计算,使用FMRIB扩散工具箱中的DTIFit得到每个受试者的部分各向异性(fractional anisotropy,FA)图。基于纤维束示踪的空间统计方法(tract-based spatial statistics,TBSS)分析主要步骤:(1)利用线性和非线性配准将每个受试者FA图配准到标准空间;(2)基于所有配准到标准空间下的FA图构建平均FA图及纤维骨架;(3)最后将每个受试者标准空间下的FA图投射到平均FA纤维骨架图上,得到个体的FA骨架,进行统计学分析。

1.4 统计学方法

       使用R 4.0.3和SPSS 23.0对数据进行统计分析,以双尾P<0.05表示差异有统计学意义。以Shapiro-Wilk test对数据正态性进行检验,数据采用均数±标准差或率表示。组间比较采用两样本t检验或Fisher exact test进行统计分析。采用Pearson相关分析评估抑郁症患者发病年龄与抑郁症严重程度的关系。FA值的比较分析是基于非参数的随机置换检验(randomized permutation test)对两组数据标准空间中的个体FA值进行比较,同时使用无阈值簇增强(thresholdfree cluster enhancement)进行多重比较校正。组间白质微结构差异另行广义线性模型对差异性结果进行协因素校正分析。差异性白质微结构与发病年龄、抑郁症严重程度的关系采用Pearson相关分析。采用中介效应模型探讨差异性白质微结构在发病年龄与抑郁症严重程度潜在联系中的作用。

2 结果

2.1 一般资料比较

       与早发性抑郁症组相比,晚发性抑郁症组表现为更大的年龄、更大的发病年龄、更多的发病次数及更高的HAMD评分;两组性别、教育程度、体质量指数(body mass index,BMI)、持续总时间均无显著性差异。见表1

表1  早发性抑郁症组与晚发性抑郁症组的一般资料比较
Tab. 1  Comparison of general information of early-onset and later-onset depressive disorder groups

2.2 发病年龄与抑郁症严重程度的关系

       Pearson相关分析显示,在控制了年龄、发病次数后,早发性和晚发性抑郁症患者的发病年龄与抑郁症严重程度均呈显著性正相关(r=0.512,P=0.007;r=0.435,P=0.010) (图1A1B);而且这种显著相关性在所有受试者中仍存在(r=0.528,P=0.001) (图1C)。

图1  发病年龄与抑郁症严重程度的关系。A:早发性抑郁症组发病年龄与抑郁症严重程度的关系;B:晚发性抑郁症组发病年龄与抑郁症严重程度的关系;C:所有受试者发病年龄与抑郁症严重程度的关系
Fig. 1  Correlation between onset age and the severity of depressive disorder. A: The correlation between onset age and the severity of depressive disorder in the early-onset depressive disorder group; B: The correlation between onset age and the severity of depressive disorder in the later onset depressive disorder group; C: The correlation between onset age and the severity of depressive disorder in all subjects.

2.3 差异性白质微结构筛选

       与早发性抑郁症组相比,晚发性抑郁症组左侧内囊、左侧扣带(海马)、双侧钩状束及右侧矢状层(包括下额枕束和下纵束) FA值显著降低(P<0.05) (图2)。在控制了年龄、发病次数后,左侧扣带(海马)和双侧钩状束FA值的组间显著性差异消失(P>0.05),最终筛选出的差异性白质微结构是左侧内囊和右侧矢状层(包括下额枕束和下纵束)。

图2  TBSS分析显示早发性抑郁症组与晚发性抑郁症组FA值差异的白质微结构:左侧内囊、左侧扣带(海马)、双侧钩状束及右侧矢状层(包括下额枕束和下纵束)。L:左侧大脑半球
Fig. 2  TBSS analysis showed differences FA values in white matter micro-structures between the early-onset and the later-onset depressive disorder groups: Left internal capsule, left cingulate (hippocampus), bilateral uncinate fasciculus and right sagittal stratum including inferior fronto-occipital fasciculus and inferior longitidinal fasciculus. L: Left hemisphere.

2.4 差异性白质微结构与发病年龄、抑郁症严重程度的关系

       在控制了协变量后,所有受试者左侧内囊FA值与发病年龄、HAMD评分呈显著性负相关(r=-0.434,P=0.001;r=-0.594,P=0.001) (图3A3B)。右侧矢状层(包括下额枕束和下纵束) FA值与发病年龄、HAMD评分也存在相似的显著相关性(r=-0.565,P=0.001;r=-0.370,P=0.004) (图3C3D)。

图3  差异性白质微结构与发病年龄、抑郁症严重程度的关系。A、B:左侧内囊FA值与发病年龄、抑郁症严重程度的关系;C、D:右侧矢状层(包括下额枕束和下纵束) FA值与发病年龄、抑郁症严重程度的关系
Fig. 3  Correlation between differential white matter micro-structures and onset age and the severity of depressive disorder. A, B: The correlation between the FA values of left internal capsule and onset age and the severity of depressive disorder; C, D: The correlation between FA values of right sagittal stratum (including inferior fronto-occipital fasciculus and inferior longitidinal fasciculus) and onset age and the severity of depressive disorder.

2.5 差异性白质微结构介导发病年龄对抑郁症严重程度的影响

       在以左侧内囊FA值为中介因子的模型中,左侧内囊FA值在发病年龄和抑郁症严重程度之间存在显著性中介效应(ab path=0.155,SE=0.055,95% CI:0.059~0.276)。然而,在以右侧矢状层(包括下额枕束和下纵束) FA值为中介因子的模型中,右侧矢状层(包括下额枕束和下纵束)FA值在发病年龄和抑郁症严重程度之间的中介效应无统计学意义(ab path=0.047,SE=0.065,95% CI:-0.069~0.191)。见图4

图4  中介效应模型分析。路径系数a代表发病年龄作用于左侧内囊FA值的效应,路径系数b代表左侧内囊FA值作用于抑郁症严重程度的效应。发病年龄到抑郁症严重程度的实线和虚线分别代表发病年龄和抑郁症严重程度之间的总效应(路径系数c)和直接效应(路径系数c’) (*P<0.05)
Fig. 4  Mediation effect model analysis. The a path represent the effect of onset age on FA values of internal capsule, b path represent the effect of FA values of internal capsule on the severity of depressive disorder. The solid and dotted lines from onset age to the severity of depressive disorder represent the total (c path) and direct effect (c’ path) respectively (*P<0.05).

3 讨论

3.1 发病年龄、白质微结构、抑郁症严重程度的关系

       本研究基于DTI技术首次尝试将发病年龄、白质微结构改变与抑郁症严重程度联系起来,并采用中介效应模型探索差异性白质微结构在发病年龄与抑郁症严重程度关系中的作用。研究结果显示,抑郁症患者的发病年龄与抑郁症严重程度呈显著性正相关。左侧内囊和右侧矢状层(包括下额枕束和下纵束)是早发性抑郁症组与晚发性抑郁症组比较所筛选出的差异性脑区。另外,左侧内囊和右侧矢状层(包括下额枕束和下纵束)FA值不仅均与发病年龄呈显著性负相关,而且与HAMD评分均呈显著性负相关。最后,中介效应模型显示左侧内囊FA值显著性介导了发病年龄和抑郁症严重程度之间的关系。

3.2 白质微结构是介导发病年龄与抑郁症严重程度的潜在因子

       本研究发现抑郁患者的发病年龄与抑郁症严重程度呈显著性正相关,提示发病年龄越大症状越严重,与先前的研究报道具有较高的一致性。随着年龄增大,抑郁症患者沮丧感、孤独感会越来越强烈,相应的抑郁症状也会越发突出[15]。另外,本研究发现左侧内囊和右侧矢状层(包括下额枕束和下纵束) FA值降低是晚发性抑郁症组特征性白质微结构改变。新近的研究证据表明内囊包含大量连接额叶-皮层下神经回路神经元的白质纤维束,与情绪处理和调节密切相关[16];下额枕束将枕叶皮质、颞叶、顶上小叶的白质纤维连接到额叶,其在注意、记忆和语言功能中起到了重要的作用[17]。与此同时,FA值反映的是水分子各向异性成分占整个扩散张量的比例,间接反映了白质纤维束的完整性[18-20]。因此,左侧内囊和右侧矢状层(包括下额枕束和下纵束)作为人类情感调节的基础,其FA值降低代表连接情感环路的白质纤维遭到了破坏,从而导致认知控制和情绪调节功能的缺陷,这进一步提示抑郁症病程中更严重的临床症状[6,21]。该研究结果不仅表明白质微结构改变与抑郁症的发展过程密切相关,也为发病年龄与抑郁症严重程度之间已知联系的潜在机制探索提供了靶区和方向,即左侧内囊和右侧矢状层也许可以作为后续脑结构以及功能研究的切入点。

3.3 左侧内囊FA值介导发病年龄对抑郁症严重程度的影响

       白质微结构作为抑郁症神经解剖机制中关键的危险因素,大量的相关研究表明不同发病年龄的抑郁症患者白质微结构改变不同[5-6],以及白质微结构的完整性降低会导致更严重的抑郁症状[22-23]。例如,Li等[24]和Kim等[25]报道老年抑郁症患者前额叶皮质、下额枕束、钩状束以及边缘环路等脑区白质微结构的改变与抑郁症严重程度呈负相关。值得注意的是,与这些研究相比,本研究首次将白质微结构这个影响因素引入到发病年龄与抑郁症严重程度的联系中,研究结果发现白质微结构(左侧内囊和右侧矢状层)的FA值不仅受发病年龄影响,而且与抑郁症疾病的发展密切相关,这强烈提示了白质微结构作为连接发病年龄和抑郁症严重程度的中间介导因子的作用。同时,中介效应模型显示左侧内囊FA值在发病年龄对抑郁症严重程度影响中有显著性的中介效应,提示发病年龄越大的抑郁症患者左侧内囊FA值降低越显著,导致的抑郁症严重程度越高,该结果进一步证实了白质微结构的介导作用,从而有力地阐明了发病年龄与抑郁症严重程度之间已知联系的潜在机制。

       总而言之,本研究发现发病年龄与抑郁症严重程度密切相关以及两者之间已知联系可以由差异性白质微结构介导,进一步阐明了发病年龄与抑郁症严重程度之间已知联系的潜在机制,为抑郁症患者的预后提供了理论依据和临床指导。但本研究仍存在些许不足,本研究仅作为一种横向的研究,不能确定不同发病年龄抑郁症的预后情况以及最终结局。另外,本研究样本量偏小,可能对结果产生影响。

1
Gotlib I, Joormann J. Cognition and depression: Current status and future directions[J]. Annu Rev Clin Psychol, 2010, 6(1): 285-312. DOI: 10.1146/annurev.clinpsy.121208.131305.
2
梅兰, 邱丽华. 抑郁症性别差异的影像学研究进展[J]. 磁共振成像, 2018, 9(11): 853-856. DOI: 10.12015/issn.1674-8034.2018.11.011.
Mei L, Qiu LH. Advances in imaging studies of gender differences in depression[J]. Chin J Magn Reson Imaging, 2018, 9(11): 853-856. DOI: 10.12015/issn.1674-8034.2018.11.011.
3
Zhang NN, Qin JS, Yan JC, et al. Increased ASL-CBF in the right amygdala predicts the first onset of depression in healthy young first-degree relatives of patients with major depression[J]. J Cereb Blood Flow Metab, 2020, 40(1): 54-66. DOI: 10.1177/0271678X19861909.
4
张毅梅, 周朝当, 王继才. 抑郁症初发年龄,病程与症状的相关分析[J]. 四川精神卫生, 2012, 25(4): 232-232.
Zhang YM, Zhou CD, Wang JC. Correlation analysis of onset age, course and symptoms of depression[J]. Mental Health in Sichuan, 2012, 25(4): 232-232.
5
Cheng YQ, Xu J, Yu HJ, et al. Delineation of early and later adult onset depression by diffusion tensor imaging[J]. PLoS One, 2014, 9(11): e112307. DOI: 10.1371/journal.pone.0112307.
6
Wu F, Kong LT, Zhu Y, et al. The influence of myelin oligodendrocyte glycoprotein on white matter abnormalities in different onset age of drug-naïve depression[J]. Front Psychiatry, 2018, 9(6): 186. DOI: 10.3389/fpsyt.2018.00186.
7
Ye J, Shen ZL, Xu XF, et al. Abnormal functional connectivity of the amygdala in first-episode and untreated adult major depressive disorder patients with different ages of onset[J]. Neuroreport, 2017, 28(4): 214-221. DOI: 10.1097/WNR.0000000000000733.
8
Clark DL, Konduru N, Kemp A, et al. The impact of age of onset on amygdala intrinsic connectivity in major depression[J]. Neuropsychiatr Dis Treat, 2018, 14(9): 343-352. DOI: 10.2147/NDT.S145042.
9
Weber K, Giannakopoulos P, Herrmann FR, et al. Stressful life events and neuroticism as predictors of late-life versus early-life depression[J]. Psychogeriatrics, 2013, 13(4): 221-228. DOI: 10.1111/psyg.12024.
10
Gazal M, Jansen K, Souza LD, et al. Association of interleukin-10 levels with age of onset and duration of illness in patients with major depressive disorder[J]. Braz J Psychiatry, 2015, 37(4): 296-302. DOI: 10.1590/1516-4446-2014-1452.
11
Watanabe SY, Iga JI, Numata S, et al. Polymorphism in the promoter of the gene for the serotonin transporter affects the age of onset of major depressive disorder in the Japanese population[J]. J Affect Disord, 2015, 183(11): 156-158. DOI: 10.1016/j.jad.2015.05.009.
12
陈慧铀, 冯源, 陈谦, 等. DKI与DTI评估不同部位急性脑梗死的脑结构变化的作用[J]. 中国医学计算机成像杂志, 2018, 24(4): 281-286. DOI: 10.3969/j.issn.1006-5741.2018.04.002.
Chen HY, Feng Y, Chen Q, et al. The role of DKI and DTI in evaluating brain structure changes in different parts of acute cerebral infarction[J]. Chin J Med Comput Imaging, 2018, 24(4): 281-286. DOI: 10.3969/j.issn.1006-5741.2018.04.002.
13
陈宇, 吴明祥, 陈文娇, 等. 运用TBSS技术对梗死后抑郁症患者的扩散张量成像研究[J].磁共振成像, 2018, 9(4): 253-257. DOI: 10.12015/issn.1674-8034.2018.04.003.
Chen Y, Wu MX, Chen WJ, et al. Study of diffuse tensor imaging in patients with PSD based on TBSS technique[J]. Chin J Magn Reson Imaging, 2018, 9(4): 253-257. DOI: 10.12015/issn.1674-8034.2018.04.003.
14
Duan YP, Wei J, Geng WQ, et al. Research on cognitive function in anxious depression patients in China[J]. J Affect Disord, 2021, 280(4): 121-126. DOI: 10.1016/j.jad.2020.11.078.
15
Charlton RA, Lamar M, Ajilore O, et al. Preliminary analysis of age of illness onset effects on symptom profiles in major depressive disorder[J]. Int J Geriatr Psychiatry, 2013, 28(11): 1166-1174. DOI: 10.1002/gps.3939.
16
Mithani K, Davison B, Meng Y, et al. The anterior limb of the internal capsule: Anatomy, function, and dysfunction[J]. Behav Brain Res, 2020, 387(9): 112588. DOI: 10.1016/j.bbr.2020.112588.
17
Surbeck W, Hänggi J, Scholtes F, et al. Anatomical integrity within the inferior fronto-occipital fasciculus and semantic processing deficits in schizophrenia spectrum disorders[J]. Schizophr Res, 2020, 218(4): 267-275. DOI: 10.1016/j.schres.2019.12.025.
18
Hinton KE, Lahey BB, Villalta-Gil V, et al. White matter microstructure correlates of general and specific second-order factors of psychopathology[J]. Neuroimage Clin, 2019, 22(4): 101705. DOI: 10.1016/j.nicl.2019.101705.
19
Ganzola R, McIntosh AM, Nickson T, et al. Diffusion tensor imaging correlates of early markers of depression in youth at high-familial risk for bipolar disorder[J]. J Child Psychol Psychiatry, 2018, 59(8): 917-927. DOI: 10.1111/jcpp.12879.
20
Dillon DG, Gonenc A, Belleau E, et al. Depression is associated with dimensional and categorical effects on white matter pathways[J]. Depress Anxiety, 2018, 35(5): 440-447. DOI: 10.1002/da.22734.
21
Zaremba D, Dohm K, Redlich R, et al. Association of brain cortical changes with relapse in patients with major depressive disorder[J]. JAMA Psychiatry, 2018, 75(5): 484-492. DOI: 10.1001/jamapsychiatry.2018.0123.
22
Repple J, Mauritz M, Meinert S, et al. Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder[J]. Molecular psychiatry, 2019, 25(11): 1550-1558. DOI: 10.1038/s41380-019-0603-1.
23
Yu DH, Yuan K, Zhao L, et al. White matter integrity affected by depressive symptoms in migraine without aura: a tract-based spatial statistics study[J]. NMR in Biomedicine, 2013, 26(9): 1103-1112. DOI: 10.1002/nbm.2924.
24
Li H, Lin X, Liu L, et al. Disruption of the structural and functional connectivity of the frontoparietal network underlies symptomatic anxiety in late-life depression[J]. Neuroimage Clin, 2020, 28(4): 102398. DOI: 10.1016/j.nicl.2020.102398.
25
Kim YK, Han KM. Neural substrates for late-life depression: A selective review of structural neuroimaging studies[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2021, 104(8): 110010. DOI: 10.1016/j.pnpbp.2020.110010.

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