大脑网络连接体的特征和基于连接体的有效性预测模型在双相抑郁症中
Caixi Xi1,2, Bin Lu3, Xiaonan Guo1,2
1Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Molecular psychiatry
|July 4, 2025
概括
双极性抑郁症中大脑连接模式与健康个体不同,特定的网络变化预测了提氨酸治疗反应. 这些发现突出了个性化双极性抑郁症治疗的潜在神经生物标志物.
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:
- 异常功能连接 (FC) 与双相情感障碍 (BD) 有关,但研究结果不一致.
- 对于双极性抑郁症治疗结果的FC的预测价值仍未得到充分研究.
研究的目的:
- 通过基于连接组的分析,确定双极性抑郁症的强有力的神经生物标志物.
- 使用机器学习预测双极性抑郁症中奎蒂阿平治疗反应.
- 识别特定于治疗疗效的脑网络.
主要方法:
- 休息状态功能性MRI (rs-fMRI) 数据来自580名双极性抑郁症患者和116名健康对照者.
- 机器学习 (支持向量回归) 模型以预测基于治疗前大脑连接组的治疗反应.
- 分析网络内部和网络之间的连接,以及全球网络拓.
主要成果:
- 与双极性抑郁症相比,双极性抑郁症中观察到不同的大脑网络连接模式.
- 在默认模式网络 (DMN),传感器运动网络 (SMN) 和皮层下网络 (SC) 中,网络内部连接性提高.
- 机器学习模型成功预测了奎胺治疗疗效 (r=0.4493,p<0.001),并确定了疗效特异性网络.
结论:
- 异常的大脑网络连接组模式是双极性抑郁症的特征.
- 在治疗前,大脑连接组显示了对奎胺反应的预测潜力.
- 已识别的连接网络可以作为精确的双极性抑郁症治疗的功能目标.
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