Cluster mass inference via random field theory
Zhang, Hui, Nichols, Thomas E. and Johnson, Timothy D.. (2009) Cluster mass inference via random field theory. NeuroImage, Vol.44 (No.1). pp. 51-61. ISSN 1053-8119Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.neuroimage.2008.08.017
Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Divisions:||Faculty of Science > Statistics
Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
|Library of Congress Subject Headings (LCSH):||Cluster analysis, Cluster analysis -- Simulation methods, Random fields, Gaussian processes, Brain -- Imaging, Mathematical statistics|
|Journal or Publication Title:||NeuroImage|
|Official Date:||1 January 2009|
|Number of Pages:||11|
|Page Range:||pp. 51-61|
|Access rights to Published version:||Restricted or Subscription Access|
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