文章基本信息

标题：A landscape-based cluster analysis using recursive search instead of a threshold parameter
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作者：Thomas E. Gladwin ; Matthijs Vink ; Roger B. Mars 等
期刊名称：MethodsX
印刷版ISSN：2215-0161
电子版ISSN：2215-0161
出版年度：2016
卷号：3
页码：477-482
DOI：10.1016/j.mex.2016.06.002
语种：English
出版社：Elsevier
摘要：Graphical abstract Display Omitted Abstract Cluster-based analysis methods in neuroimaging provide control of whole-brain false positive rates without the need to conservatively correct for the number of voxels and the associated false negative results. The current method defines clusters based purely on shapes in the landscape of activation, instead of requiring the choice of a statistical threshold that may strongly affect results. Statistical significance is determined using permutation testing, combining both size and height of activation. A method is proposed for dealing with relatively small local peaks. Simulations confirm the method controls the false positive rate and correctly identifies regions of activation. The method is also illustrated using real data. • A landscape-based method to define clusters in neuroimaging data avoids the need to pre-specify a threshold to define clusters. • The implementation of the method works as expected, based on simulated and real data. • The recursive method used for defining clusters, the method used for combining clusters, and the definition of the “value” of a cluster may be of interest for future variations.
关键词：fMRI;Cluster analysis;Threshold-free;Permutation;Recursive;Derivative