机器学习揭示了在区分行为障碍和神经典型青年之间基于情绪处理功能障碍的性别差异
Gregor Kohls1, Erik M Elster2, Peter Tino3
1Department of Child and Adolescent Psychiatry, Faculty of Medicine, TUD Dresden University of Technology, German Center for Child and Adolescent Health (DZKJ), partner site Leipzig/Dresden, Dresden, Germany. gregor.kohls@ukdd.de.
BMC psychiatry
|February 6, 2025
概括
机器学习揭示了情绪功能障碍与两性行为障碍 (CD) 有关. 受影响的女孩显示出更强的关联,支持"差异值"假设,但CD异质性是显著的.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 精神病学是一个精神病学.
背景情况:
- 行为障碍 (CD) 在男孩中更为普遍,理论模型涉及情绪识别,学习和调节缺陷.
- 现有的研究并没有支持这一观点.
- 这是一个差异值.
- 这种假设认为,与男孩相比,行为障碍女孩的情绪功能障碍更大.
研究的目的:
- 通过机器学习 (ML) 来调查青少年行为障碍 (CD) 中情绪功能障碍的性别特异性差异.
- 通过使用神经认知情感特征,根据性别对CD状态进行基于ML的分类.
主要方法:
- 使用多变量ML分类器分析了来自542名患有CD的青少年和710名神经类型对照 (年龄9-18岁) 的多站点数据.
- 用情绪识别,学习和调节任务数据来区分CD和对照组,对男性和女性分别进行分析.
主要成果:
- 无论是男性还是女性的ML分类器都准确地预测了基于情绪功能障碍特征的机会水平以上的CD状态.
- 与男性分类器相比,女性的ML分类器在识别患有CD的个体方面表现优越.
- 两性分类的准确性都低于临床显著的80%值,这可能是由于样本异质.
结论:
- 这些发现证实了情绪功能障碍和CD在两性之间存在强烈的关联,在女孩中更明显的联系,支持"差异值"假设.
- 这项研究强调了行为障碍中的显著神经认知异质性,表明情绪功能障碍仅对受影响个体的一小部分具有特征.
- 其他神经认知领域可能有助于行为障碍的病因,而不仅仅是情绪处理缺陷.
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