Han Hyemin, Park Joonsuk
Educational Psychology Program, University of Alabama, Tuscaloosa, AL, United States.
Department of Psychology, The Ohio State University, Columbus OH, United States.
Front Neuroinform. 2018 Feb 2;12:1. doi: 10.3389/fninf.2018.00001. eCollection 2018.
Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data. A group of methodologists and statisticians have considered Bayesian inference as a candidate methodology. However, few previous studies have attempted to provide end users of fMRI analysis tools, such as SPM 12, with practical guidelines about how to conduct Bayesian inference. In the present study, we aim to demonstrate how to utilize Bayesian inference, Bayesian second-level inference in particular, implemented in SPM 12 by analyzing fMRI data available to public via NeuroVault. In addition, to help end users understand how Bayesian inference actually works in SPM 12, we examine outcomes from Bayesian second-level inference implemented in SPM 12 by comparing them with those from classical second-level inference. Finally, we provide practical guidelines about how to set the parameters for Bayesian inference and how to interpret the results, such as Bayes factors, from the inference. We also discuss the practical and philosophical benefits of Bayesian inference and directions for future research.
最近在神经科学和心理学领域中关于传统阈值（即<0.05）的争论，促使研究人员考虑分析功能磁共振成像（fMRI）数据的替代方法。一组方法学家和统计学家已将贝叶斯推理视为一种候选方法。然而，以前很少有研究试图为fMRI分析工具（如SPM 12）的最终用户提供关于如何进行贝叶斯推理的实用指南。在本研究中，我们旨在通过分析通过NeuroVault向公众提供的fMRI数据，展示如何利用在SPM 12中实现的贝叶斯推理，特别是贝叶斯二级推理。此外，为了帮助最终用户理解贝叶斯推理在SPM 12中实际是如何工作的，我们通过将SPM 12中实现的贝叶斯二级推理的结果与经典二级推理的结果进行比较来检验这些结果。最后，我们提供关于如何设置贝叶斯推理参数以及如何解释推理结果（如贝叶斯因子）的实用指南。我们还讨论了贝叶斯推理的实际和哲学益处以及未来研究的方向。
