Klapwijk Eduard T, Jongerling Joran, Hoijtink Herbert, Crone Eveline A
Erasmus University Rotterdam, Netherlands.
Tilburg University, Netherlands.
Dev Cogn Neurosci. 2025 Jan;71:101489. doi: 10.1016/j.dcn.2024.101489. Epub 2024 Dec 17.
Task-related functional MRI (fMRI) studies need to be properly powered with an adequate sample size to reliably detect effects of interest. But for most fMRI studies, it is not straightforward to determine a proper sample size using power calculations based on published effect sizes. Here, we present an alternative approach of sample size estimation with empirical Bayesian updating. First, this method provides an estimate of the required sample size using existing data from a similar task and similar region of interest. Using this estimate researchers can plan their research project, and report empirically determined sample size estimations in their research proposal or pre-registration. Second, researchers can expand the sample size estimations with new data. We illustrate this approach using four existing fMRI data sets where Cohen's d is the effect size of interest for the hemodynamic response in the task condition of interest versus a control condition, and where a Pearson correlation between task effect and age is the covariate of interest. We show that sample sizes to reliably detect effects differ between various tasks and regions of interest. We provide an R package to allow researchers to use Bayesian updating with other task-related fMRI studies.
与任务相关的功能磁共振成像（fMRI）研究需要有足够的样本量来进行合理的功效分析，以便可靠地检测出感兴趣的效应。但对于大多数fMRI研究来说，基于已发表的效应量通过功效计算来确定合适的样本量并非易事。在此，我们提出一种基于经验贝叶斯更新的样本量估计替代方法。首先，该方法利用来自相似任务和相似感兴趣区域的现有数据来估计所需的样本量。利用这一估计值，研究人员可以规划他们的研究项目，并在研究提案或预注册中报告根据经验确定的样本量估计值。其次，研究人员可以用新数据来扩大样本量估计值。我们使用四个现有的fMRI数据集来说明这种方法，其中科恩d值是感兴趣的任务条件与对照条件下血液动力学反应的效应量，而任务效应与年龄之间的皮尔逊相关系数是感兴趣的协变量。我们表明，可靠检测效应所需的样本量在不同任务和感兴趣区域之间存在差异。我们提供了一个R包，以便研究人员在其他与任务相关的fMRI研究中使用贝叶斯更新。
