跨区域,网络,签名和机器学习多元宇宙的负面情感特征的功能神经生物学
M Sicorello1,2, P J Gianaros3, A G C Wright4
1Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
bioRxiv : the preprint server for biology
|June 6, 2025
概括
神经病症是一种神经病症.
科学领域:
- 神经科学是一个神经科学.
- 心理学 心理学 心理学
- 精神病学是一个精神病学.
背景情况:
- 了解像神经病症这样的负面情感特征的神经基础是一个重大挑战.
- 现有的研究往往侧重于局部的大脑活动,这可能无法完全捕捉复杂的特征.
研究的目的:
- 为了确定大脑区域,网络或全脑特征是否能最好地解释负面情感特征.
- 作为神经病症的一个方面,研究压力脆弱性的神经支柱.
主要方法:
- 功能性磁共振成像 (fMRI) 在458名成年人身上执行情感面部和场景观看任务.
- 全脑机器学习模型被用来预测神经病症和压力脆弱性.
- 在14个特征和1176个模型中进行了多元分析.
主要成果:
- 神经病不能通过大脑活动来预测;贝叶斯证据不利于理论引导的神经测量.
- 全脑模型成功解码了压力脆弱性,神经病的一面,在单独的样本中进行复制.
- 神经应激脆弱性模式显示出良好的心理测量特性,这表明分布式模式是关键.
结论:
- 负面情感特征更好地表现为分布式全脑模式,而不是局部活动.
- 这些发现挑战了简单的神经生物学理论,并强调了细粒度在将心理结构与神经测量对齐方面的重要性.
- 在预测个体差异和人内情绪状态之间存在很大的差距.
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