解析人格解体-现实解体障碍的大脑动力学：动态功能网络连接分析。

Unraveling the brain dynamics of Depersonalization-Derealization Disorder: a dynamic functional network connectivity analysis.

机构信息

Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.

Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China.

出版信息

BMC Psychiatry. 2024 Oct 14;24(1):685. doi: 10.1186/s12888-024-06096-1.

DOI:10.1186/s12888-024-06096-1

PMID:39402459

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11475637/

Abstract

BACKGROUND

Depersonalization-Derealization Disorder (DPD), a prevalent psychiatric disorder, fundamentally disrupts self-consciousness and could significantly impact the quality of life of those affected. While existing research has provided foundational insights for this disorder, the limited exploration of brain dynamics in DPD hinders a deeper understanding of its mechanisms. It restricts the advancement of diagnosis and treatment strategies. To address this, our study aimed to explore the brain dynamics of DPD.

METHODS

In our study, we recruited 84 right-handed DPD patients and 67 healthy controls (HCs), assessing them using the Cambridge Depersonalization Scale and a subliminal self-face recognition task. We also conducted a Transcranial Direct Current Stimulation (tDCS) intervention to understand its effect on brain dynamics, evidenced by Functional Magnetic Resonance Imaging (fMRI) scans. Our data preprocessing and analysis employed techniques such as Independent Component Analysis (ICA) and Dynamic Functional Network Connectivity (dFNC) to establish a comprehensive disease atlas for DPD. We compared the brain's dynamic states between DPDs and HCs using ANACOVA tests, assessed correlations with patient experiences and symptomatology through Spearman correlation analysis, and examined the tDCS effect via paired t-tests.

RESULTS

We identified distinct brain networks corresponding to the Frontoparietal Network (FPN), the Sensorimotor Network (SMN), and the Default Mode Network (DMN) in DPD using group Independent Component Analysis (ICA). Additionally, we discovered four distinct dFNC states, with State-1 displaying significant differences between DPD and HC groups (F = 4.10, P = 0.045). Correlation analysis revealed negative associations between the dwell time of State-2 and various clinical assessment factors. Post-tDCS analysis showed a significant change in the mean dwell time for State-2 in responders (t-statistic = 4.506, P = 0.046), consistent with previous clinical assessments.

CONCLUSIONS

Our study suggests the brain dynamics of DPD could be a potential biomarker for diagnosis and symptom analysis, which potentially leads to more personalized and effective treatment strategies for DPD patients.

TRIAL REGISTRATIONS

The trial was registered at the Chinese Clinical Trial Registry on 03/01/2021 (Registration number: ChiCTR2100041741, https://www.chictr.org.cn/showproj.html?proj=66731 ) before the trial.

摘要

背景

人格解体-现实解体障碍（DPD）是一种普遍存在的精神疾病，它从根本上扰乱了自我意识，并可能对受影响者的生活质量产生重大影响。虽然现有研究为这种障碍提供了基础见解，但对 DPD 中大脑动态的有限探索阻碍了对其机制的更深入理解。这限制了诊断和治疗策略的进步。为了解决这个问题，我们的研究旨在探索 DPD 的大脑动态。

方法

在我们的研究中，我们招募了 84 名右利手 DPD 患者和 67 名健康对照者（HCs），使用剑桥去人格化量表和潜意识自我面孔识别任务对他们进行评估。我们还进行了经颅直流电刺激（tDCS）干预，以了解其对功能磁共振成像（fMRI）扫描的大脑动态的影响。我们的数据预处理和分析采用独立成分分析（ICA）和动态功能网络连接（dFNC）等技术，为 DPD 建立了全面的疾病图谱。我们使用方差分析（ANACOVA）测试比较 DPD 和 HCs 之间的大脑动态状态，通过 Spearman 相关分析评估与患者体验和症状的相关性，并通过配对 t 检验检查 tDCS 的效果。

结果

我们使用组独立成分分析（ICA）在 DPD 中识别出与额顶网络（FPN）、感觉运动网络（SMN）和默认模式网络（DMN）相对应的不同大脑网络。此外，我们发现了四个不同的 dFNC 状态，状态 1 显示 DPD 和 HC 组之间存在显著差异（F=4.10，P=0.045）。相关分析显示，状态 2 的停留时间与各种临床评估因素呈负相关。tDCS 后分析显示，反应者的状态 2 的平均停留时间有显著变化（t 统计量=4.506，P=0.046），与之前的临床评估一致。