一个系统的评估的性能多个大脑年龄算法在两个年轻人队伍的年轻人队伍
Cleanthis Michael1, Natasha S Jones1, Jamie L Hanson2,3
1Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA.
Human brain mapping
|January 30, 2026
概括
大脑年龄算法显示年轻人的表现是可变的. 一些模型可以准确地预测不同年轻人群中的大脑成熟度,但普遍应用仍然不确定.
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
- 医疗成像医学成像
背景情况:
- 青春期的大脑发育很快,受环境因素的影响.
- 大脑年龄,衡量相对大脑成熟度,是潜在的风险标志物.
- 现有的大脑年龄算法主要训练成年人,白人和优势人口.
研究的目的:
- 在基于人口的青年队伍中系统地比较五个有影响力的大脑年龄算法.
- 为了评估算法预测的准确性,对各种因素的敏感性和趋同.
- 为了对未来关于不同年轻人的大脑成熟度的研究进行基准算法.
主要方法:
- 五个大脑年龄算法的比较 (Drobinin,Whitmore,Pyment,Kaufmann,Centile). 这是一个非常好的例子.
- 在两个青年队伍中进行评估:一组双胞胎的初级样本 (N=593,年龄9-19岁) 和一组二级样本 (N=198,年龄15-17岁).
- 预测准确性的评估 (与时间学年龄的相关性,平均绝对误差),对扫描参数的敏感性,人口统计学和遗传相似性 (单胞胎双胞胎).
主要成果:
- 三种算法 (Drobinin,Pyment,Centile) 在初级双胞胎队列中显示出强有力的预测 (年龄相关性为0.51-0.68).
- 皮门特算法展示了最强的年龄相关性和对获取序列,图像质量,性别和青春期的稳定性.
- 所有五种算法都在第二个更均的青年队伍中表现出较弱的预测.
结论:
- 大脑年龄估计算法在不同的年轻人群中显示了可变的效用.
- 算法的性能取决于研究人口的背景和年龄范围.
- 需要进一步的研究来完善对不同青年群体和特定研究问题的大脑年龄估计.
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