探索结构共变网络在诊断精神分裂症方面的预测价值
Clara S Vetter1,2,3, Annika Bender1, Dominic B Dwyer1,4,5
1Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.
Frontiers in psychiatry
|June 24, 2025
概括
结构共变网络 (SCN) 显示出作为精神分裂症诊断生物标志物的前景. 不同的SCN计算方法捕获了不同的疾病方面,组合模型提高了分类准确性.
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.
- 生物标志物发现发现
背景情况:
- 精神分裂症与大脑连接受损有关.
- 结构共变网络 (SCN) 反映了协调的神经发育过程和共享的形态变异.
研究的目的:
- 评估SCNs作为精神分裂症生物标志物的诊断潜力.
- 将两个SCN计算方法 (REF-SCN和KLS-SCN) 与区域灰质体积 (GMV) 进行比较.
主要方法:
- 在154名精神分裂症患者和366名健康对照中比较了来自GMV的REF-SCN和KLS-SCN.
- 使用了带有嵌套交叉验证和堆叠概括的逐步线性支向量机器.
- 在独立样本上进行外部验证的模型 (NPAT=71,NHC=74).
主要成果:
- 基于SCN的模型将精神分裂症患者分类为与区域GMV (BAC=67.10%在外部验证中) 结合时表现改善的精神分裂症患者.
- 在发现样本 (BAC=67.03%) 中,REF-SCN单模模型实现了最高的性能.
- 身体运动,默认模式,控制,视觉和腹部注意力网络的变化是模型决策的关键驱动因素.
结论:
- 不同的SCN计算方法捕捉了精神分裂症的不同方面.
- 对于将精神分裂症与对照区分开来,REF-SCN非常有价值.
- KLS-SCNs可以捕捉微妙的症状水平特征,与临床变量如BMI和负面症状相关联.
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