Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China.
NCMIS, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Med Image Anal. 2018 Jul;47:15-30. doi: 10.1016/j.media.2018.03.014. Epub 2018 Apr 5.
The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS.
连接体关联的识别，包括检查整个大脑中两两体素之间的功能连接，在统计学和计算上都具有挑战性。尽管这种连接体方法最近已被全脑关联研究（BWAS）采用，以识别几种精神障碍（如精神分裂症、自闭症和抑郁症）中的连通性变化，但专门用于连接体分析的多重校正和功效分析方法仍然缺乏。因此，我们在此报告了一种基于高斯随机场理论的严格的连接体显著检验统计框架的开发。它包括使用拓扑推理方法控制多重假设检验的总体错误率（FWER），并计算连接体研究的功效和样本量。我们的理论框架可以准确地控制假阳性率，这在使用两个静息态 fMRI 数据集进行的实证验证中得到了证实。与 Bonferroni 校正和错误发现率（FDR）相比，它可以通过适当利用 fMRI 数据的空间信息来降低假阳性率并提高统计功效。重要的是，我们的方法避免了需要非参数置换来校正多重比较，因此，它可以有效地处理具有高分辨率 fMRI 图像的大型数据集。我们的方法在一项病例对照研究中得到了验证。我们的方法可以识别重度抑郁症数据集的功能连接变化，而现有方法则无法识别。一个软件包可在 https://github.com/weikanggong/BWAS 上获得。
