An integrated cluster‐wise significance measure for fMRI analysis. Issue 8 (2nd March 2022)

• Record Type: Journal Article
• Authors: Ge, Yunjiang; Chen, Gang; Waltz, James A.; Hong, Liyi Elliot; Kochunov, Peter; Chen, Shuo
• Is Part Of: Human brain mapping. Volume 43:Issue 8(2022)
• Page Start: 2444
• Page End: 2459
• Publication Date: 2022-03-02
• Subjects: Chernoff bound -- cluster -- dependent p‐value -- fMRI -- spatial correlation; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25795
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Cluster‐wise inference is widely used in fMRI analysis. The cluster‐level statistic is often obtained by counting the number of intra‐cluster voxels which surpass a voxel‐level statistical significance threshold. This measure can be sub‐optimal regarding the power and false‐positive error rate because the suprathreshold voxel count neglects the voxel‐wise significance levels and ignores the dependence between voxels. This article aims to provide a new Integrated Cluster‐wise significance Measure (ICM) for cluster‐level significance determination in cluster‐wise fMRI analysis by integrating cluster extent, voxel‐level significance (e.g., p values), and activation dependence between within‐cluster voxels. We develop a computationally efficient strategy for ICM based on probabilistic approximation theories. Consequently, the computational load for ICM‐based cluster‐wise inference (e.g., permutation tests) is affordable. We validate the proposed method via extensive simulations and then apply it to two fMRI data sets. The results demonstrate that ICM can improve the power with well‐controlled family‐wise error (FWE). Abstract : This article provides a computationally efficient strategy—Integrated Cluster‐wise significance Measure (ICM) for cluster‐level significance determination in cluster‐wise fMRI analysis by integrating cluster extent, voxel‐level significance (e.g., p values), and activation dependence between within‐cluster voxels. The proposed method has improved power with well‐controlled family‐wise error (FWE).