TY - JOUR T1 - Statistical testing and power analysis for brain-wide association study JF - bioRxiv DO - 10.1101/089870 SP - 089870 AU - Weikang Gong AU - Lin Wan AU - Wenlian Lu AU - Liang Ma AU - Fan Cheng AU - Wei Cheng AU - Stefan Gruenewald AU - Jianfeng Feng Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/12/22/089870.abstract N2 - Brain-wide association study (BWAS) is analogous to the successful genome-wide association study (GWAS) in the genetics field. It aims to identify the voxel-wise functional connectome variations associated with complex traits. Although it has been applied to several mental disorders, such as schizophrenia [12], autism [13]and depression [14], its statistical foundations are still lacking. Therefore, we herein report the development of a rigorous statistical framework for link-wise significance testing and theoretical power analysis based on the random field theory. Peak- and cluster-level inferences are generalized to analyze functional connectivities. A novel method to identify phenotype associated voxels based on functional connectivity pattern is also proposed. Our method reduces the computational complexity of permutation-based approach in controlling the false positive rate and provides robust and reproducible findings in several real datasets, such as the 1000 Functional Connectomes Project (1000 FCP), Autism Brain Imaging Data Exchange (ABIDE), Center for Biomedical Research Excellence (COBRE) and others. ER -