在对大脑表型预测的外部验证中,权力和可重现性
Matthew Rosenblatt1, Link Tejavibulya2, Huili Sun3
1Department of Biomedical Engineering, Yale University, New Haven, CT, USA. matthew.rosenblatt@yale.edu.
Nature human behaviour
|July 31, 2024
概括
脑表型模型的外部验证需要仔细考虑样本大小. 高效果大小需要数百名参与者,而低效果大小需要数千人才能获得足够的统计能力.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 生物统计学 生物统计学
背景情况:
- 外部验证对于评估神经成像中大脑表型预测模型的概括性至关重要.
- 传统的功率计算对于外部验证是不够的,因为样本的样本大小是双重的 (培训和外部).
研究的目的:
- 调查训练样本大小,外部样本大小,表型效应大小和脑表型预测模型中的统计能力之间的相互作用.
- 为未来的神经成像外部验证研究确定适当样本大小提供指导.
主要方法:
- 使用功能和结构连接数据进行了超过9亿次基于重新采样的模拟.
- 从多种表型 (例如,年龄,认知能力,心理健康症状) 的不同神经成像数据集 (例如,HCP,ABCD,HBN) 分析数据.
主要成果:
- 高效果大小预测在训练和外部样本大小的数百个方面实现了足够的功率.
- 低和中效果大小预测需要在培训和外部样本中数百到数千名参与者.
- 内部验证性能经常预测外部验证性能,特别是在协调的数据集中.
结论:
- 外部验证研究往往由于样本大小不足而缺乏足够的统计能力.
- 理论功率曲线需要调整以适应外部验证环境中的培训样本大小.
- 这些发现为设计足够强大的未来脑表型研究外部验证研究提供了关键的见解.
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