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青少年重度抑郁症的动态网络特征:注意网络中介了快感缺失与注意力缺陷之间的关联。

Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit.

机构信息

Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, China.

出版信息

Hum Brain Mapp. 2023 Dec 1;44(17):5749-5769. doi: 10.1002/hbm.26474. Epub 2023 Sep 8.

DOI:10.1002/hbm.26474
PMID:37683097
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10619388/
Abstract

Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.

摘要

注意力缺陷是一种严重的症状,会损害患有重度抑郁症(MDD)的青少年的社交功能。在这项研究中,我们旨在探讨与注意力缺陷相关的动态神经网络活动及其与 MDD 青少年临床结局的关系。我们纳入了 188 名 MDD 青少年和 94 名健康对照者。通过结合心理物理学、静息态脑电图(EEG)和功能磁共振成像(fMRI)技术,我们旨在通过研究 MDD 青少年的 EEG 微状态特征及其相关时间网络特征,来确定动态网络特征。在基线时,微状态分析显示,患者组中微状态 C 的出现频率低于健康对照组,而微状态 D 的持续时间和覆盖范围增加。中介分析显示,从微状态 C 到 D 的转换概率在 MDD 组中介导了快感缺失和注意力缺陷。fMRI 结果显示,MDD 患者的背侧注意网络(DAN)的时间变异性明显弱于健康对照组。重要的是,DAN 的时间变异性介导了患者组中快感缺失和注意力缺陷之间的关系。在急性治疗阶段后,反应预测组(RP)在微状态 C 和 D 方面的表现优于非反应预测组(NRP)。对于静息态 fMRI 数据,DAN 的时间变异性在 RP 组中明显高于 NRP 组。总的来说,这项研究丰富了我们对 MDD 患者注意力缺陷的神经机制的理解,并提供了新的临床生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c924/10619388/18b085bc31b3/HBM-44-5749-g003.jpg
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2
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Front Psychiatry. 2022 May 9;13:877417. doi: 10.3389/fpsyt.2022.877417. eCollection 2022.
3
Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging.
时变动态网络模型用于 fMRI 和 MEG 成像中的动态静息态功能连接。
Neuroimage. 2022 Jul 1;254:119131. doi: 10.1016/j.neuroimage.2022.119131. Epub 2022 Mar 23.
4
Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium.重度抑郁症静息态功能连接组改变:来自 PsyMRI 联盟的 mega 分析。
Transl Psychiatry. 2021 Oct 7;11(1):511. doi: 10.1038/s41398-021-01619-w.
5
Prediction of Clinical Outcomes With EEG Microstate in Patients With Major Depressive Disorder.脑电图微状态对重度抑郁症患者临床结局的预测
Front Psychiatry. 2021 Aug 16;12:695272. doi: 10.3389/fpsyt.2021.695272. eCollection 2021.
6
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J Child Psychol Psychiatry. 2022 Jan;63(1):34-46. doi: 10.1111/jcpp.13445. Epub 2021 May 21.
7
The neuroprogressive nature of major depressive disorder: evidence from an intrinsic connectome analysis.重度抑郁症的神经进展性:来自内在连接组学分析的证据。
Transl Psychiatry. 2021 Feb 4;11(1):102. doi: 10.1038/s41398-021-01227-8.
8
Repetitive Transcranial Magnetic Stimulation (rTMS) for the Treatment of Depression in Adolescence: A Systematic Review.重复经颅磁刺激(rTMS)治疗青少年抑郁症的系统评价。
J Affect Disord. 2021 Jan 1;278:460-469. doi: 10.1016/j.jad.2020.09.058. Epub 2020 Sep 15.
9
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Neuropsychopharmacology. 2020 Nov;45(12):2030-2037. doi: 10.1038/s41386-020-0749-1. Epub 2020 Jun 26.
10
Structural brain networks in remitted psychotic depression.缓解期精神病性抑郁症的结构脑网络。
Neuropsychopharmacology. 2020 Jun;45(7):1223-1231. doi: 10.1038/s41386-020-0646-7. Epub 2020 Feb 28.