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dc.contributor.authorGe, Yunjiang
dc.contributor.authorChen, Gang
dc.contributor.authorWaltz, James A
dc.contributor.authorHong, Liyi Elliot
dc.contributor.authorKochunov, Peter
dc.contributor.authorChen, Shuo
dc.date.accessioned2022-03-09T13:21:23Z
dc.date.available2022-03-09T13:21:23Z
dc.date.issued2022-03-02
dc.identifier.urihttp://hdl.handle.net/10713/18184
dc.description.abstractCluster-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). © 2022 The Authors.en_US
dc.description.urihttps://doi.org/10.1002/hbm.25795en_US
dc.language.isoenen_US
dc.publisherWiley-Blackwellen_US
dc.relation.ispartofHuman Brain Mappingen_US
dc.rights© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.en_US
dc.subjectChernoff bounden_US
dc.subjectclusteren_US
dc.subjectdependent p-valueen_US
dc.subjectfMRIen_US
dc.subjectspatial correlationen_US
dc.titleAn integrated cluster-wise significance measure for fMRI analysis.en_US
dc.typeArticleen_US
dc.identifier.doi10.1002/hbm.25795
dc.identifier.pmid35233859
dc.source.journaltitleHuman brain mapping
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States
dc.source.countryUnited States


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