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抑郁症中与分离症状相关的默认模式网络和神经认知变化:一项研究方案

DMN network and neurocognitive changes associated with dissociative symptoms in major depressive disorder: a research protocol.

作者信息

Ercan Dogan Asli, Aslan Genc Herdem, Balaç Sinem, Hun Senol Sevin, Ayas Görkem, Dogan Zafer, Bora Emre, Ceylan Deniz, Şar Vedat

机构信息

Department of Psychiatry, School of Medicine, Koç University, Istanbul, Türkiye.

Department of Child and Adolescent Psychiatry, School of Medicine, Koç University, Istanbul, Türkiye.

出版信息

Front Psychiatry. 2025 Apr 1;16:1516920. doi: 10.3389/fpsyt.2025.1516920. eCollection 2025.

Abstract

INTRODUCTION

Depression is a heterogeneous disorder with diverse clinical presentations and etiological underpinnings, necessitating the identification of distinct subtypes to enhance targeted interventions. Dissociative symptoms, commonly observed in major depressive disorder (MDD) and linked to early life trauma, may represent a unique clinical dimension associated with specific neurocognitive deficits. Although emerging research has begun to explore the role of dissociation in depression, most studies have provided only descriptive analyses, leaving the mechanistic interplay between these phenomena underexplored. The primary objective of this study is to determine whether MDD patients with prominent dissociative symptoms differ from those without such symptoms in clinical presentation, neurocognitive performance, and markers of functional connectivity. This investigation will be the first to integrate comprehensive clinical evaluations, advanced neurocognitive testing, and high-resolution brain imaging to delineate the contribution of dissociative symptoms in MDD.

METHODS

We will recruit fifty participants for each of three groups: (1) depressive patients with dissociative symptoms, (2) depressive patients without dissociative symptoms, and (3) healthy controls. Diagnostic assessments will be performed using the Structured Clinical Interview for DSM-5 (SCID) alongside standardized scales for depression severity, dissociation, and childhood trauma. Neurocognitive performance will be evaluated through a battery of tests assessing memory, attention, executive function, and processing speed. Structural and functional magnetic resonance imaging (MRI) will be conducted on a 3 Tesla scanner, focusing on the connectivity of the Default Mode Network with key regions such as the orbitofrontal cortex, insula, and posterior cingulate cortex. Data analyses will employ SPM-12 and Matlab-based CONN and PRONTO tools, with multiclass Gaussian process classification applied to differentiate the three groups based on clinical, cognitive, and imaging data.

DISCUSSION

The results of this study will introduce a novel perspective on understanding the connection between major depressive disorder and dissociation. It could also aid in pinpointing a distinct form of depression associated with dissociative symptoms and early childhood stressors.

CONCLUSION

Future research, aiming to forecast the response to biological and psychological interventions for depression, anticipates this subtype and provides insights.

摘要

引言

抑郁症是一种具有多种临床表现和病因基础的异质性疾病,因此需要识别不同的亚型以加强针对性干预。分离症状在重度抑郁症(MDD)中常见,且与早期生活创伤有关,可能代表了一种与特定神经认知缺陷相关的独特临床维度。尽管新兴研究已开始探索分离在抑郁症中的作用,但大多数研究仅提供了描述性分析,这些现象之间的机制相互作用仍未得到充分探索。本研究的主要目的是确定具有明显分离症状的MDD患者在临床表现、神经认知表现和功能连接标记方面是否与没有此类症状的患者不同。这项研究将首次整合全面的临床评估、先进的神经认知测试和高分辨率脑成像,以阐明分离症状在MDD中的作用。

方法

我们将为三组中的每组招募50名参与者:(1)有分离症状的抑郁症患者,(2)无分离症状的抑郁症患者,(3)健康对照。将使用《精神疾病诊断与统计手册》第5版(DSM-5)的结构化临床访谈以及抑郁症严重程度、分离和童年创伤的标准化量表进行诊断评估。将通过一系列评估记忆、注意力、执行功能和处理速度的测试来评估神经认知表现。将在3特斯拉扫描仪上进行结构和功能磁共振成像(MRI),重点关注默认模式网络与眶额皮质、岛叶和后扣带回皮质等关键区域的连接。数据分析将采用SPM-12以及基于Matlab的CONN和PRONTO工具,应用多类高斯过程分类根据临床、认知和成像数据区分这三组。

讨论

本研究结果将为理解重度抑郁症与分离之间的联系引入一个新的视角。它还可以帮助确定一种与分离症状和幼儿期应激源相关的独特抑郁症形式。

结论

旨在预测抑郁症生物和心理干预反应的未来研究预期会出现这种亚型并提供见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4bb/11996865/1abea3832677/fpsyt-16-1516920-g001.jpg

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