基于Connectomics的休息状态功能网络改变预测了严重抑郁症中自杀倾向
Qing Wang1, Cancan He1, Zan Wang1,2
1Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
Translational psychiatry
|November 27, 2023
概括
研究了具有自杀倾向的大型抑郁障碍 (MDD) 的静止状态网络 (RSN) 功能障碍. 大脑网络连接的异常与自杀倾向的严重程度相关,可以作为MDD患者的诊断生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 医疗成像医学成像
背景情况:
- 大型抑郁症 (MDD) 带来了重大风险,包括自杀行为.
- 自杀倾向的MDD患者的动态变化和网络功能障碍尚未完全理解.
- 研究静止状态网络 (RSN) 可能会揭示MDD中自杀倾向的生物标志物.
研究的目的:
- 为了研究不同程度的自杀想法和行为的MDD患者中RSN的障碍.
- 探索这些RSN改变作为诊断生物标志物的潜力,以区分MDD与没有自杀倾向.
- 分析RSN连接,自杀倾向严重程度和临床因素之间的关系.
主要方法:
- 这是一项多中心的横截面研究,涉及528名MDD患者和998名健康对照.
- 构建和分析十个RSN,包括默认模式 (DMN),皮质下 (SUB),腹部注意力 (VAN) 和视觉网络 (VIS).
- 基于网络的统计分析和支持向量机 (SVM) 建模,以评估连接性和歧视患者群体.
主要成果:
- 观察到网络内部和网络之间的连接异常,与"自杀倾向梯度"相关.
- 连接值显示了复杂的增加和减少模式,随着自杀倾向的增加而增加.
- 在基于自杀倾向梯度的MDD患者区分方面,SVM模型实现了高精度 (AUC 0.73-0.99).
结论:
- 在MDD患者中发现了大脑网络连接的破坏,MDD患者具有不同的自杀倾向.
- 这些发现为MDD中自杀的潜在病理生理机制提供了洞察力.
- RSN的变化显示出作为评估MDD自杀倾向的潜在生物标志物具有前途.
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