{"ID":2846154,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02422","arxiv_id":"2511.02422","title":"Cluster Size Matters: A Comparative Study of Notip and pARI for Post Hoc Inference in fMRI","abstract":"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.","short_abstract":"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 mor...","url_abs":"https://arxiv.org/abs/2511.02422","url_pdf":"https://arxiv.org/pdf/2511.02422v1","authors":"[\"Nils Peyrouset\",\"Pierre Neuvial\",\"Bertrand Thirion\"]","published":"2025-11-04T09:54:16Z","proceeding":"math.ST","tasks":"[\"math.ST\",\"stat.AP\",\"stat.ME\"]","methods":"[\"LoRA\"]","has_code":false}