Evaluating the value and limits of null models for Lesion Network Mapping

Lesion Network Mapping (LNM) is a method used to map symptom-related circuits in the human brain. Recent work has raised concerns about the methodological framework of LNM and the disease specificity of its derived networks, which often show high spatial overlap across unrelated conditions. Null models have been proposed to address the raised limitations ¹ . As useful statistical tools in network neuroscience, they are reference procedures ; in LNM, the normative connectome matrix C remains the source of the derived networks also under null modelling. Their broader role as a general solution for LNM therefore warrants evaluation. Here, we examine the proposed null models step-by-step and test them empirically across multiple datasets. We show that the proposed null-model correction effectively only replaces C with a transformed matrix C′ (e.g., C′∼C−mean(C) ), one that is directly derived from C and retains much of its low-dimensional structure. Consequently, null-controlled LNM maps remain shaped by the same standard set of connectome-derived patterns and continue to show repetition and non-specificity across unrelated conditions. We discuss the scope and limitations of null models for LNM, and indicate that they provide statistical reference for LNM findings, but do not establish disease specificity.