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Implementation Errors in the GingerALE Software: Description and Recommendations

机译:GingerALE软件中的实现错误:说明和建议

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摘要

Neuroscience imaging is a burgeoning, highly sophisticated field the growth of which has been fostered by grant-funded, freely distributed software libraries that perform voxel-wise analyses in anatomically standardized three-dimensional space on multi-subject, whole-brain, primary datasets. Despite the ongoing advances made using these non-commercial computational tools, the replicability of individual studies is an acknowledged limitation. Coordinate-based meta-analysis offers a practical solution to this limitation and, consequently, plays an important role in filtering and consolidating the enormous corpus of functional and structural neuroimaging results reported in the peer-reviewed literature. In both primary data and meta-analytic neuroimaging analyses, correction for multiple comparisons is a complex but critical step for ensuring statistical rigor. Reports of errors in multiple-comparison corrections in primary-data analyses have recently appeared. Here, we report two such errors in GingerALE, a widely used, US National Institutes of Health (NIH)-funded, freely distributed software package for coordinate-based meta-analysis. These errors have given rise to published reports with more liberal statistical inferences than were specified by the authors. The intent of this technical report is threefold. First, we inform authors who used GingerALE of these errors so that they can take appropriate actions including re-analyses and corrective publications. Second, we seek to exemplify and promote an open approach to error management. Third, we discuss the implications of these and similar errors in a scientific environment dependent on third-party software.
机译:神经科学影像学是一个新兴的,高度复杂的领域,其发展受到了受资助的免费分发的软件库的支持,该软件库可以在解剖学标准化的三维空间中对多对象,全脑,原始数据集进行体素化分析。尽管使用这些非商业性计算工具取得了不断的进步,但是单个研究的可复制性是公认的局限性。基于坐标的荟萃分析为解决这一局限性提供了一种切实可行的解决方案,因此,在过滤和巩固同行评审文献中报道的功能性和结构性神经影像学结果的巨大主体方面起着重要作用。在原始数据和荟萃分析神经影像分析中,对多个比较进行校正是确保统计严格性的复杂但关键的步骤。最近出现了在主要数据分析中进行多次比较校正的错误报告。在这里,我们报告了GingerALE中的两个此类错误,GingerALE是一种广泛使用的,由美国国立卫生研究院(NIH)资助,免费分发的软件包,用于基于坐标的荟萃分析。这些错误导致发表的报告具有比作者指定的更为宽松的统计推断。本技术报告的目的是三方面的。首先,我们将这些错误告知使用GingerALE的作者,以便他们可以采取适当的措施,包括重新分析和更正出版物。第二,我们试图举例说明并推广一种开放的错误管理方法。第三,我们讨论了在依赖第三方软件的科学环境中这些错误和类似错误的含义。

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