Noble Stephanie, Scheinost Dustin, Constable R Todd
Department of Radiology and Biomedical Imaging, Yale School of Medicine.
Department of Statistics and Data Science, Yale University.
Curr Opin Behav Sci. 2021 Aug;40:27-32. doi: 10.1016/j.cobeha.2020.12.012. Epub 2021 Jan 20.
The test-retest reliability of functional neuroimaging data has recently been a topic of much discussion. Despite early conflicting reports, converging reports now suggest that test-retest reliability is poor for standard univariate measures-namely, voxel- and region-level task-based activation and edge-level functional connectivity. To better understand the implications of these recent studies requires understanding the nuances of test-retest reliability as commonly measured by the intraclass correlation coefficient (ICC). Here we provide a guide to the measurement and interpretation of test-retest reliability in functional neuroimaging and review major findings in the literature. We highlight the importance of making choices that improve reliability so long as they do not diminish validity, pointing to the potential of multivariate approaches that improve both. Finally, we discuss the implications of recent reports of low test-retest reliability in the context of ongoing work in the field.
功能神经影像数据的重测信度近来成为了诸多讨论的话题。尽管早期报告存在矛盾,但目前越来越多的报告表明,对于标准单变量测量(即体素和区域水平的基于任务的激活以及边水平的功能连接)而言,重测信度较差。为了更好地理解这些近期研究的影响,需要了解通过组内相关系数(ICC)通常测量的重测信度的细微差别。在此,我们提供了一份关于功能神经影像中重测信度测量与解释的指南,并回顾了文献中的主要发现。我们强调了做出能够提高信度的选择的重要性,只要这些选择不会降低效度,并指出了能同时提高两者的多变量方法的潜力。最后,我们在该领域当前工作的背景下讨论了近期关于低重测信度报告的影响。