Reconciling Two Sides: Novel Analytical Statistics for Lesion Network Mapping

Recent studies have reached conflicting conclusions about the statistical significance of lesion network mapping (LNM), especially regarding the impact of regional node strength on lesion-associated network detection. Here, we present a statistical framework that clarifies the origin of these apparently conflicting findings. Based on the simplified formulation of LNM introduced by van den Heuvel et al. (2026), we propose alternative, permutation-based null models corresponding to the sensitivity and specificity tests used in standard LNM analysis. We derive analytical expressions for both the proposed null models and the node-strength-constrained approach recently introduced by Zalesky and Cash (2026), thereby providing a computationally efficient alternative to permutation-based significance testing. Using synthetic lesion and connectivity matrices, as well as clinical datasets, we compare the outcome of the proposed null models with the standard LNM analysis and demonstrate how the choice of null models influences statistical inference and the resulting lesion network maps. Our framework shows that the apparent disagreements in the recent LNM literature can be understood as a consequence of different statistical null models rather than to conflicting conclusions about LNM itself.