Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

中文翻译：

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通过多级建模通过贝叶斯透镜处理神经成像的多重性。

在这里，我们以大规模单变量方法解决当前效率低下和过度惩罚的问题，然后进行多次测试的校正，并提出了一种更有效的模型，该模型可以在大脑区域之间汇总和共享信息。使用贝叶斯多级（BML）建模，我们控制了两种类型的误差，它们比常规的误报率（FPR）更相关：符号错误（类型S）和幅度大小（类型M）。BML还旨在实现两个目标：1）通过使用一个集成模型来提高建模效率，从而解决多重测试问题； 2）通过校准S型，将传统的零假设有效测试（NHST）的重点放在FPR上，转变为质量控制同时保持合理水平的推理效率。通过在感兴趣区域（ROI）级别的应用程序证明了该方法的性能和有效性，所有区域都处于平等地位：与NHST下的当前方法不同，小区域不仅仅因为其物理尺寸而处于不利地位。此外，与大规模单变量方法相比，BML可以同时实现更高的空间特异性和推理效率，并促进结果报告的整体性和透明性。使用实验数据集在性能和质量检查中说明了BML的好处。该方法还避免了当前在实践中采用尖锐和任意阈值的做法。小区域并不仅仅因为其物理尺寸而处于不利地位。此外，与大规模单变量方法相比，BML可以同时实现更高的空间特异性和推理效率，并促进结果报告的整体性和透明性。使用实验数据集在性能和质量检查中说明了BML的好处。该方法还避免了当前在实践中采用尖锐和任意阈值的做法。小区域并不仅仅因为其物理尺寸而处于不利地位。此外，与大规模单变量方法相比，BML可以同时实现更高的空间特异性和推理效率，并促进结果报告的整体性和透明性。使用实验数据集在性能和质量检查中说明了BML的好处。该方法还避免了当前在实践中采用尖锐和任意阈值的做法。减少多维数据的p值漏斗。作为常规用途的AFNI套件的一部分，可以使用BML方法及其辅助工具。