B0511
Title: Cluster extent inference revisited: Quantification and localization of brain activity
Authors: Jelle Goeman - Leiden University Medical Center (Netherlands) [presenting]
Abstract: Cluster inference based on spatial extent thresholding is a popular analysis method of multiple testing in spatial data and is frequently used for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding regions with some activation, the method as currently defined does not allow any further quantification or localization of signal. This gap is repaired, showing that cluster-extent inference can be used (1.) to infer the presence of signal in any region of interest and (2.) to quantify the percentage of activation in such regions. These additional inferences come for free, i.e. they do not require any further adjustment of the alpha-level of tests while retaining full familywise error control. This extension of the possibilities of cluster inference is achieved by an embedding of the method into a closed testing procedure, and solving the graph-theoretic k-separator problem that results from this embedding. The usefulness of the improved method is demonstrated in a large-scale application to neuroimaging data from the Neurovault database.
