Spatial Confidence Sets - Beyond Null Hypothesis Testing of Cluster Size.

Vancouver Convention Centre 

Null hypothesis testing lies at the foundation of human brain mapping as the core method for fMRI inference. However, recent studies have shown that under optimal conditions the null hypothesis is never true [1]. As ambitious, large-sample studies have become available (e.g. Human Connectome Project, N=1,200; UK Biobank final N=100,000), this we have high-quality, high-power data for which the null hypothesis test essentially shows universal activation even with stringent correction.

To overcome this, we apply recent work [2] to develop confidence sets (CSs) on clusters found in thresholded maps. Whereas traditional inferences indicate where the null, i.e. an effect size of 0, is rejected, the CSs are statements about non-zero effect sizes analogous to confidence intervals. For a cluster constructed with cluster-forming threshold c, the CSs comprise two sets of voxels: The upper CS is smaller, giving the voxels we infer to be truly larger than c; the larger lower CS is best described by its complement -- all voxels outside this set we infer to be truly smaller than c.

Here we describe the method, evaluate it with simulations and apply it to HCP data. We focus on inference on the percentage BOLD change map.