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, Chris C Camp2
1Department of Biomedical Engineering, Yale University, New Haven, CT.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
在神经成像研究中,外部验证往往缺乏统计能力,因为样本大小小小. 这项研究表明,培训和外部数据集大小如何对大脑表型关联研究的力量产生重大影响.
科学领域:
- 神经成像是一种神经成像.
- 认知神经科学 认知神经科学
- 精神病学遗传学 精神病学遗传学
背景情况:
- 在神经成像研究中,识别可复制和可概括的大脑表型关联是至关重要的.
- 预测框架用于评估大脑表型模型,但外部验证提供了更强大的概括性评估.
- 在神经成像中,外部验证研究的统计力尚未得到充分研究.
结论:
- 为了在神经成像中进行有效的外部验证,必须仔细考虑培训和外部数据集的样本大小.
- 数据集内部的性能可以作为基准,通常在r=0.2的跨数据集性能内相关.
- 这项研究提供了关键的见解,为未来的脑表型研究的外部验证研究提供动力.
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