CINP:一种基于因果关系的框架,用于在精神分裂症中对抗精神病反应的可概括和可复制的神经图像预测
Yang Xiao1, Mingzhu Li1, Jinmin Liao1
1Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China.
Biological psychiatry
|March 16, 2026
概括
一个新的因果关系信息的神经成像预测框架 (CINP) 显著改善了对精神分裂症抗精神病治疗反应的预测. 这种方法整合了生物因果信息,提高了模型的概括性和可复制性.
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.
- 生物医学信息学 生物医学信息学
背景情况:
- 对抗精神病药物治疗精神分裂症的反应差异很大,这凸显了对预测生物标志物的需求.
- 目前基于神经成像的预测模型 (NPM) 经常依赖于相关方法,限制了它们的概括性和易受混因素的影响.
- 整合NPM与生物因果信息的框架对于可靠的预测至关重要.
研究的目的:
- 介绍CINP,一种用于因果关系信息的神经成像预测的新框架.
- 提高抗精神病反应的神经成像模型的生物可信性和预测准确性.
- 用独立的精神分裂症数据集测试CINP的可行性和通用性.
主要方法:
- 开发了CINP,将孟德尔随机化衍生的因果效应作为生物学先验.
- 利用来自两个独立的精神分裂症队列 (PKU6,ZMD) 的多式磁共振成像数据.
- 训练有素的CINP模型来预测个性化抗精神病反应 (PANSS减少) 和通过交叉验证评估准确性.
主要成果:
- 与传统的NPM相比,CINP在预测抗精神病治疗反应方面的改善是三倍的.
- 基于白质通道的CINP实现了最高的预测准确性 (平均r = 0.652) 和跨站点概括性 (平均r = 0.551).
- 跨数据集预测特征权重的高一致性证实可复制模式.
结论:
- CINP是一个可行的和有效的框架,用于预测精神分裂症的抗精神病反应.
- 该框架在结合各种因果约束来进行可靠的预测方面提供了灵活性.
- 在各种临床神经成像应用中,CINP具有推进个性化医学的潜力.
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