超越抑郁症症状:默认模式网络作为抗抑郁药反应的预测指标
Kaizhong Zheng1, Liangjun Chen1, Huaning Wang2
1National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Npj mental health research
|January 16, 2026
概括
大型抑郁症 (MDD) 患者的默认模式网络 (DMN) 连接性减少可能预测抗抑郁药治疗反应. 这一发现为指导MDD治疗和理解神经生物学提供了潜在的生物标志物.
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:
- 重度抑郁症 (MDD) 治疗疗效有限,潜在的神经机制尚不清楚.
- 默认模式网络 (DMN),特别是中间前额叶皮层 (mPFC) -后侧环状皮层 (PCC) 连接,与MDD有关.
- 需要验证DMN作为抗抑郁药反应生物标志物的潜力.
研究的目的:
- 调查DMN连接性与MDD中抗抑郁药治疗反应之间的关系.
- 为了确定DMN有效连接 (EC) 是否可以预测治疗结果.
- 探索DMN EC作为指导MDD治疗决策的潜在生物标志物.
主要方法:
- 分析四个大型MDD队列 (n=4271) 的静止状态fMRI数据,使用格兰杰因果关系对DMN有效连接 (EC) 的分析.
- 在使用线性混合效应模型的复发性MDD患者,首次药物未使用的患者和健康对照中比较EC.
- 培训支持矢量机 (SVM) 分类器和支持矢量回归 (SVR) 用于使用mPFC-PCC EC. EC预测治疗反应.
主要成果:
- 与对照组和第一次发病的患者相比,复发性MDD患者的mPFC-PCC EC显著减少.
- 降低EC与抗抑郁药物使用和疾病持续时间相关.
- mPFC-PCC EC通过SVM分类器高准确度预测了抗抑郁药治疗反应,独立于核心抑郁症状.
结论:
- 减少的mPFC-PCC有效连接性可以作为MDD中抗抑郁药治疗反应的预测生物标志物.
- 关于DMN连接的测量为MDD神经生物学提供了洞察力.
- 这些发现支持DMN连接对于个性化MDD治疗策略的临床潜力.
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