Functional connectivity profiles in remitted depression and their relation to ruminative thinking

IF 3.4 2区医学 Q2 NEUROIMAGING

Neuroimage-Clinical Pub Date : 2024-11-26 DOI:10.1016/j.nicl.2024.103716

Zhuo Fang , Emma Lynn , Verner J. Knott , Natalia Jaworska

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引用次数: 0

Abstract

The triple network model suggests that dysfunction in three major brain networks – the default mode network (DMN), central executive network (CEN), and salience network (SN) – might contribute to cognitive impairments in various psychiatric disorders, including major depressive disorder (MDD). While hyperconnectivity in the DMN, hypoconnectivity in the CEN, and abnormal SN connectivity have been observed in acutely depressed patients, evidence for network alterations during remission is limited. Further, there are few studies examining connectivity in people in remission from MDD (rMDD) during emotional processing tasks, including during affective cognition (i.e., tasks that encompass affective processing in the context of cognitive processes, such as inhibition).

To address these literature gaps, this study compared functional connectivity (FC) between resting and task conditions, specifically during the emotional Stroop (eStroop) task, as well as between rMDD and healthy volunteers (HVs), within and between nodes of the three networks. We also explored how FC relates to rumination in the rMDD group, given that rumination tends to persist in rMDD and involves affective and cognitive networks.

We unexpectedly found greater FC during the task vs. rest condition within the DMN, and decreased FC during the task vs. rest conditions within the CEN and SN across the groups. Greater FC during the task vs. rest condition between DMN and SN nodes, as well as between CEN and SN nodes were also observed. These effects were more pronounced in the rMDD group as per our exploratory analyses. Additionally, the rMDD vs. HV group showed higher FC between DMN-CEN nodes, regardless of condition. Higher hopeless rumination scores were associated with decreased resting FC within the DMN, while higher active problem-solving scores were associated with increased task FC within the DMN in the rMDD group.

These findings suggest that tasks engaging affective cognition processes influence FC within and among the three networks, with this effect more pronounced in the rMDD group. This might indicate potential protective and compensatory mechanisms in rMDD and expands our understanding of large-scale intrinsic network connectivity alterations during remission from depression. However, given the limited sample and the exploratory nature of some of our analyses, replication is necessary.

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抑郁症缓解期的功能连接特征及其与反刍思维的关系

三重网络模型表明，默认模式网络（DMN）、中央执行网络（CEN）和突出网络（SN）这三个主要大脑网络的功能障碍可能导致包括重度抑郁症（MDD）在内的各种精神疾病的认知障碍。虽然在急性抑郁症患者中观察到DMN的超连通性，CEN的低连通性和SN异常连通性，但缓解期间网络改变的证据有限。此外，很少有研究检查重度抑郁症（rMDD）缓解者在情绪处理任务期间的连通性，包括情感认知（即，在认知过程（如抑制）的背景下包含情感处理的任务）。为了解决这些文献空白，本研究比较了休息和任务条件之间的功能连接（FC），特别是在情绪Stroop （eStroop）任务期间，以及rMDD和健康志愿者（HVs）之间在三个网络节点内和节点之间的功能连接（FC）。考虑到反刍倾向于在rMDD中持续存在，并且涉及情感和认知网络，我们还探讨了FC与rMDD组反刍的关系。我们意外地发现，在DMN的任务条件下比休息条件下FC更高，而在CEN和SN的任务条件下比休息条件下FC降低。在DMN和SN节点之间，以及CEN和SN节点之间，也观察到在任务条件下比休息条件下更大的FC。根据我们的探索性分析，这些影响在rMDD组中更为明显。此外，无论病情如何，rMDD组与HV组在DMN-CEN节点之间表现出更高的FC。在rMDD组中，较高的无望反刍得分与DMN内静息FC减少有关，而较高的主动解决问题得分与DMN内任务FC增加有关。这些发现表明，涉及情感认知过程的任务会影响三个网络内部和网络之间的FC，这种影响在rMDD组中更为明显。这可能表明rMDD的潜在保护和补偿机制，并扩展了我们对抑郁症缓解期间大规模内在网络连接改变的理解。然而，鉴于有限的样本和我们的一些分析的探索性，复制是必要的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neuroimage-Clinical NEUROIMAGING-

CiteScore

7.50

自引率

4.80%

发文量

368

审稿时长

52 days

期刊介绍： NeuroImage: Clinical, a journal of diseases, disorders and syndromes involving the Nervous System, provides a vehicle for communicating important advances in the study of abnormal structure-function relationships of the human nervous system based on imaging. The focus of NeuroImage: Clinical is on defining changes to the brain associated with primary neurologic and psychiatric diseases and disorders of the nervous system as well as behavioral syndromes and developmental conditions. The main criterion for judging papers is the extent of scientific advancement in the understanding of the pathophysiologic mechanisms of diseases and disorders, in identification of functional models that link clinical signs and symptoms with brain function and in the creation of image based tools applicable to a broad range of clinical needs including diagnosis, monitoring and tracking of illness, predicting therapeutic response and development of new treatments. Papers dealing with structure and function in animal models will also be considered if they reveal mechanisms that can be readily translated to human conditions.