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使用弹性网模型对类似精神病体验的多模式预测:在临床样本中进行外部验证

Seda Arslan¹, Merve Kaşıkçı², Osman Dağ²

¹Department of Psychology, Bilkent University Faculty of Economics Administrative and Social Sciences, Ankara, Türkiye.

Psychological medicine

|November 14, 2025

概括

环境,认知和大脑网络因素预测了青年的精神病类经历 (PLEs). 这些多模式因素也部分归纳到临床精神病,为整个精神病谱的脆弱性和弹性提供了洞察力.

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.
遗传学是一个遗传学.

背景情况:

类似精神病的经历 (PLEs) 存在于与精神病的连续上.
多模式因素有助于PLE,但缺乏全面的研究.
这项研究模拟了在大量年轻人样本中预测PLEs的多个因素.

研究的目的:

开发和验证一个模型,使用多式因素预测类似精神病的经历 (PLEs).
检查这些预测因素是否对患有精神病的个人通用.
在精神病谱中识别中间的表型.

主要方法:

从741个因素 (环境,认知,临床,大脑连接组) 中进行变量选择.
弹性网分类模型预测396名健康青少年和年轻成年人 (14-24岁) 的高/低PLEs.
在临床队列中的外部验证 (第一发精神病患者,兄弟姐妹,对照).

主要成果:

十一个因素 (环境,认知评估) 和16个结构性大脑网络属性预测了PLE.
该模型在预测PLEs方面实现了中等准确度 (75%) 和AUC (0.750).
在将精神病患者与他们的兄弟姐妹区分开来时,观察到高特异性 (84.2%).

结论:

包括环境负担,认知模式和大脑网络改变在内的多模式因素预测了PLE.
这些预测因素部分概括到临床精神病,表明共享的机制.
识别的变量可能代表与精神病脆弱性和弹性相关的中间表型.

关键词:

弹性网模拟的弹性网模型机器学习是机器学习.精神病第一集精神病第一集类似精神病的经历.结构上的连接omeome.

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