Permutation-based inference preserves anatomical specificity in lesion network mapping

Because many neurobehavioural functions rely on distributed brain networks, anatomically diverse brain lesions can cause the same neurobehavioural deficit. Lesion network mapping (LNM) builds on this principle to better understand the functional anatomy of the brain, by mapping focal lesions onto a normative functional connectome. Yet, recent work raised concerns about anatomical specificity of LNM, showing that commonly used LNM procedures converge on nonspecific connectome properties. Here, we show that anatomically specific LNM is possible with the right statistical approach using symptom-label permutation as a null model. We demonstrate this in a multicenter dataset of 2,950 stroke patients across 12 cohorts, comparing patients with and without impairment in 6 cognitive domains. First, we showed that permutation-based LNM yielded distinct and biologically plausible network maps with modest cross-cognitive domain similarity. Second, we replicated the previously raised concern of nonspecific connectome-driven convergence when using parametric statistics. Third, we assessed specificity of our approach through simulation analyses across 10,000 null studies which confirmed that the permutation framework maintained valid type I error control. These findings demonstrate that permutation-based null models preserve anatomical specificity in LNM, enabling the identification of brain networks that are genuinely linked to distinct neurobehavioural functions. This approach may thus allow researchers to more reliably map the network basis of neurobehavioural deficits from focal brain lesions.