月经期,青春期时间和大脑:女性特有的大脑成熟模式超出了与年龄相关的发展
Nina Gottschewsky1,2, Dominik Kraft3, Tobias Kaufmann4,5,6
1Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, Tübingen, Germany. nina.gottschewsky@maxplanckschools.de.
Biology of sex differences
|March 27, 2024
概括
机器学习使用脑部扫描准确地分类青春期时间,为青少年神经发育提供了一个新的客观标志物. 这种方法揭示了与青春期有关的独特的大脑模式,超过了传统的基于年龄的评估.
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
- 放射学 放射学是一门学科.
背景情况:
- 青春期涉及到青少年的重大社会和生物学变化.
- 青少年的大脑发育通常是根据年龄来研究的,但青春期的时间可能是一个更敏感的指标.
- 关于青春期时间和大脑发育之间的关系的研究是有限的.
研究的目的:
- 在结构性MRI数据上使用机器学习对月经前和月经后状态进行分类.
- 为了比较与初潮相关的神经发育模式与与年龄相关的模式.
- 评估机器学习和大脑年龄差距模型对青春期成熟度测量的敏感性.
主要方法:
- 机器学习对初潮状态 (前与后) 的分类,使用来自年龄匹配的青少年女性 (ABCD队列) 的结构性MRI数据.
- 在独立数据上训练一个大脑年龄预测模型,并将其应用于ABCD队列,以计算大脑年龄差距.
- 测试分类和大脑年龄差距模型对初潮年龄和青春期状态的敏感性.
主要成果:
- 机器学习模型在分类初潮状态方面取得了统计学上显著的准确性.
- 通过初潮分类和大脑年龄预测,共享和独特的神经发育模式被确定.
- menarche类概率,但不是大脑年龄差距,与初潮时的年龄有关.
结论:
- 机器学习模型可以从结构性MRI中对初潮状态进行分类,并考虑与年龄相关的神经发育.
- menarche类概率显示对青春期时间的敏感性.
- 这种方法可能会导致一种客观的,基于大脑的青春期发育标志物.
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