精神分裂症谱系障碍中负面紧迫性和自杀倾向的电路相关:一项LASSO回归研究
Tyler Pia1, Enna Sanghvi1, Mark Shuquan Chen2
1Department of Psychiatry, Weill Cornell Medicine, New York, NY, United States.
Frontiers in psychiatry
|December 25, 2025
概括
精神分裂症谱系障碍 (SSD) 和负紧迫性准确地预测了自杀风险. 机器学习模型结合SSD症状和负紧迫性确定了高风险个体,大脑数据提供了轻微的预测改进.
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.
- 机器学习 机器学习
背景情况:
- 自杀是一种主要的公共卫生问题,精神分裂症谱系障碍 (SSD) 和自杀念头和行为 (SIB) 之间存在着强烈的联系.
- 基于情绪的冲动性或紧迫性是SSD和自杀风险的背景下较少探索的因素.
研究的目的:
- 使用机器学习方法调查SSD症状,急迫性和自杀风险之间的关联.
- 确定SSD患者中高自杀风险的关键预测因素.
主要方法:
- 最小绝对缩小和选择运营商 (LASSO) 的逻辑回归被用来将个人分为SIBs的高风险和低风险组.
- 模型1包括人口统计,童年创伤,SSD症状,认知能力和紧迫性. 模型2还包括神经成像数据.
主要成果:
- 在模型1中,敌意,内和负面紧迫感预测了更高的自杀风险,而抽象思维困难预测了更低的风险.
- 在模型2中,敌意预测了更高的风险,而前 cingulate 厚度和特定额头和前 cingulate 大脑区域的活动预测了更低的风险.
- 负紧迫性和SSD症状是高风险和低风险组的准确分类器.
结论:
- 精神分裂症谱系障碍症状和负面紧迫性是自杀风险的重要预测因素.
- 机器学习模型可以有效地识别高风险SIB的个体,神经成像数据提供增量预测价值.
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