从p-value中提取证据权重,使用贝叶斯的激活概率估计方法进行元分析
Tommaso Costa1,2,3, Jordi Manuello1,2,4, Franco Cauda1,2,3
1GCS-fMRI Group, Koelliker Hospital and Department of Psychology, University of Turin, 10124 Turin, Italy.
Brain sciences
|January 28, 2026
概括
这项研究将p值转换为贝叶斯因子边界,在神经成像元分析中提供了更强大的证据衡量标准. 这种贝叶斯式启发的方法通过揭示传统意义值之外的证据的真实权重来提高结论的可靠性.
科学领域:
- 神经成像是一种神经成像.
- 统计推理 统计推理
- 贝叶斯分析 贝叶斯分析
背景情况:
- P值被广泛使用,但无法量化假设的证据强度.
- 这种限制在神经成像元分析中至关重要,用于评估大脑激活模式.
- 目前的方法缺乏准确的证据来支持特定的发现.
研究的目的:
- 介绍一种用于将p值转换为贝叶斯因子上限的实用方法.
- 在神经成像元分析中量化替代假设的最大可信证据.
- 提高神经成像元分析结论的可解释性和可靠性.
主要方法:
- 开发了一种将p值转换为贝叶斯因子上限的方法.
- 在激活概率估计 (ALE) 框架内应用该方法.
- 为了说明,使用了73个手指敲击实验的参考数据集.
主要成果:
- 在ALE的统计学显著效应显示了广泛的证据强度 (贝叶斯因子范围从46到410).
- 传统的基于值的推断隐藏了证据权重的大量异质性.
- 证明p值并不准确地反映证据的强度.
结论:
- 拟议的方法产生了证据强度的voxel-wise地图.
- 它与现有的神经成像分析管道兼容.
- 提供了一个保守的,贝叶斯式灵感补充标准显著性地图,提高可解释性,避免p值误解.
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