自杀念头的晚年抑郁症中的马带皮质功能网络异常:来自EEG图形理论和机器学习的见解
Yicheng Lin1, Yijie Zeng1, Zhangying Wu1
1Geriatric Neuroscience Center, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China.
Journal of psychiatry & neuroscience : JPN
|January 23, 2026
概括
晚年抑郁症 (LLD) 与自杀念头 (SI) 显示了改变的马带大脑网络连接. 这些网络变化可能有助于识别具有自杀风险的LLD患者,帮助预防工作.
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.
- 计算神经科学是一种神经科学.
背景情况:
- 晚年抑郁症 (LLD) 是老年人自杀的重要危险因素.
- 自杀念头 (SI) 是一个关键的,但往往未表达的,在LLD的干预阶段.
- 马振荡及其在大脑网络中的失调与LLD和自杀行为有关.
研究的目的:
- 在具有和没有SI的LLD患者中研究马波段的皮质功能网络.
- 了解LLD中SI背后的神经生物学机制.
- 在LLD中确定SI的潜在网络生物标志物.
主要方法:
- 脑电图 (EEG) 从具有SI的LLD患者 (LLD-SI),没有SI的LLD (LLD-NSI) 和正常对照 (NC) 中记录下来.
- 源级图形理论分析应用于马波段功能连接.
- 基于网络特征的机器学习用于区分LLD-SI和LLD-NSI.
主要成果:
- 在LLD群体中观察到马功能连接的减少,特别是涉及轨道前皮层.
- 与LLD-NSI和NC相比,LLD-SI组表现出较低的聚类系数和特征路径长度,与LLD-NSI和NC相比,总效率提高.
- 图形理论分析在LLD中区分SI,准确度为69.35%,灵敏度为73.33%,特异性为65.63%.
结论:
- 马波段网络指数为LLD中的SI的大脑连接组提供了洞察力.
- 这些网络特征显示出在LLD中检测SI的潜在生物标志物.
- 对于神经调节的频率特定目标可以开发用于LLD的自杀预防.
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