2025/11/04 by Nils Peyrouset, Pierre Neuvial, Peyrouset, Nils +3
Computer Science · Neuroscience · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Mathematics #Face Recognition and Perception #Functional Brain Connectivity Studies #Methodology (stat.ME) #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2511.02422
openalex created_date 2025/11/04 · openalex publication_date 2025/11/04 · openalex updated_date 2026/07/28
All Resolutions Inference (ARI) is a post hoc inference method for functional Magnetic Resonance Imaging (fMRI) data analysis that provides valid lower bounds on the proportion of truly active voxels within any, possibly data-driven, cluster. As such, it addresses the paradox of spatial specificity encountered with more classical cluster-extent thresholding methods. It allows the cluster-forming threshold to be increased in order to locate the signal with greater spatial precision without overfitting, also known as the drill-down approach. Notip and pARI are two recent permutation-based extensions of ARI designed to increase statistical power by accounting for the strong dependence structure typical of fMRI data. A recent comparison between these papers based on large voxel clusters concluded that pARI outperforms Notip. We revisit this conclusion by conducting a systematic comparison of the two. Our reanalysis of the same fMRI data sets from the Neurovault database demonstrates the existence of complementary performance regimes: while pARI indeed achieves higher sensitivity for large clusters, Notip provides more informative and robust results for smaller clusters. In particular, while Notip supports informative ``drill-down'' exploration into subregions of activation, pARI often yields non-informative bounds in such cases, and can even underperform the baseline ARI method.