ARIbrain: Fast and flexible statistical analysis of fMRI data using True Discovery Proportions.

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Statistical analysis of functional MRI (fMRI) data requires correct control for multiple comparisons. Most often this is done using cluster-extent based thresholding. One of the drawbacks of this method is that it requires the setting of an arbitrary cluster-forming threshold (usually Z > 3.1) that determines the size/shape of the clusters used for further analysis. Once this threshold is set, researchers are not allowed, statistically, to redo the analysis with a different threshold. Together with the knowledge that the correct interpretation of a significant cluster is: 'there is at least one active voxel in this cluster' and not 'all voxels in this cluster are active', this leads to a (statistically) suboptimal way of analysing fMRI data.

Recent advances in statistics have led to a new range of methods based on the True Discovery Proportion (TDP) [1-3]. These methods estimate the lower bound of the number of truly active voxels, i.e.TDP, within a cluster, for any cluster in the data, as many times a researcher wants, with full control of the family-wise error rate. They do not require the setting of an arbitrary threshold as the TDP provides a simultaneous bound on the number of active voxels over all possible clusters.

In practice, these methods thus give the researcher almost unlimited freedom in selecting and analysing clusters. One can use different thresholds for different clusters, calculate the TDP for an a priori defined cluster, or search for clusters with at least a certain TDP level. This flexibility requires software that goes beyond what is available in current statistical analysis packages like FSL or SPM.