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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.

Original publication




Journal article



Publication Date





477 - 482


Cluster analysis, Derivative, Permutation, Recursive, Recursive clustering, Threshold-free, fMRI