aKey Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, ChinabUniversity of Chinese Academy of Sciences, Beijing 100049, ChinacNCMIS, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, ChinadSchool of Mathematical Sciences, Fudan University, Shanghai 200433, ChinaeInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, ChinafShanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, ChinagBeijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, ChinahDepartment of Computer Science, University of Warwick, Coventry CV4 7AL, UK
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HighlightsWe propose a novel statistical framework for brain-wide association study.Random field theory is developed to control the false-positive rate for connectivity-wise significance testing and conduct theoretical power analysis.Extensive simulation and real data analysis is performed to validate our method.Graphical abstractThe 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.