Suppr超能文献

从静息态功能连接和自我参照加工理解和预测抑郁症的未来复发。

Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing.

机构信息

Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, Groningen, the Netherlands.

Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen, the Netherlands; Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India.

出版信息

J Psychiatr Res. 2023 Sep;165:305-314. doi: 10.1016/j.jpsychires.2023.07.034. Epub 2023 Jul 26.

Abstract

BACKGROUND

The recurrent nature of Major Depressive Disorder (MDD) asks for a better understanding of mechanisms underlying relapse. Previously, self-referential processing abnormalities have been linked to vulnerability for relapse. We investigated whether abnormalities in self-referential cognitions and functioning of associated brain-networks persist upon remission and predict relapse.

METHODS

Remitted recurrent MDD patients (n = 48) and never-depressed controls (n = 23) underwent resting-state fMRI scanning at baseline and were additionally assessed for their implicit depressed self-associations and ruminative behaviour. A template-based dual regression approach was used to investigate between-group differences in default mode, cingulo-opercular and frontoparietal network resting-state functional connectivity (RSFC). Additional prediction of relapse status at 18-month follow-up was investigated within patients using both regression analyses and machine learning classifiers.

RESULTS

Remitted patients showed higher rumination, but no implicit depressed self-associations or RSFC abnormalities were observed between patients and controls. Nevertheless, relapse was related to i) baseline RSFC between the ventral default mode network and the precuneus, dorsomedial frontal gyrus, and inferior occipital lobe, ii) implicit self-associations, and iii) uncontrollability of ruminative thinking, when controlled for depressive symptomatology. Moreover, preliminary machine learning classifiers demonstrated that RSFC within the investigated networks predicted relapse on an individual basis.

CONCLUSIONS

Remitted MDD patients seem to be commonly characterized by abnormal rumination, but not by implicit self-associations or abnormalities in relevant brain networks. Nevertheless, relapse was predicted by self-related cognitions and default mode RSFC during remission, suggesting that variations in self-relevant processing play a role in the complex dynamics associated with the vulnerability to developing recurrent depressive episodes.

CLINICAL TRIAL REGISTRATION

Netherlands Trial Register, August 18, 2015, trial number NL53205.042.15.

摘要

背景

复发性重度抑郁症(MDD)的反复发作性质要求我们更好地了解复发的潜在机制。先前,自我参照加工异常与复发易感性有关。我们研究了在缓解后,自我参照认知和相关脑网络的功能是否存在异常,以及这些异常是否可以预测复发。

方法

缓解后的复发性 MDD 患者(n=48)和从未抑郁的对照组(n=23)在基线时进行静息态 fMRI 扫描,此外还评估了他们的内隐抑郁自我联想和沉思行为。采用基于模板的双回归方法,研究默认模式、扣带-脑岛和额顶叶网络静息态功能连接(RSFC)的组间差异。在患者中,使用回归分析和机器学习分类器进一步研究了对 18 个月随访时复发状态的预测。

结果

缓解后的患者表现出更高的沉思,但在患者和对照组之间没有观察到内隐抑郁自我联想或 RSFC 异常。然而,复发与 i)基线时腹侧默认模式网络与后扣带回、背内侧前额叶和下顶叶之间的 RSFC,ii)内隐自我联想,以及 iii)沉思思维的不可控性有关,这些因素在控制了抑郁症状后仍然存在。此外,初步的机器学习分类器表明,在所研究的网络内的 RSFC 可以预测个体的复发。

结论

缓解后的 MDD 患者通常表现为异常的沉思,但没有内隐的自我联想或相关脑网络的异常。然而,在缓解期,自我相关认知和默认模式 RSFC 预测了复发,这表明自我相关处理的变化在与易发性相关的复杂动态中起着作用。

临床试验注册

荷兰试验登记处,2015 年 8 月 18 日,试验编号 NL53205.042.15。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验