Critical-Network Lesion-Symptom Mapping

Complex datasets used for lesion-symptom mapping (LSM) present serious challenges for interpretable statistical analysis. We introduce Critical Network Lesion-Symptom Mapping (CN-LSM), a neuroanatomical atlas-based method that uses low-dimensional regression models and out-of-sample prediction to identify brain regions that contribute significantly to the prediction of clinical behavioral scores. Rather than focusing on a single most-predictive model, CN-LSM identifies a set of plausible models analyzed as an ensemble. CN-LSM offers three primary advantages over existing approaches: (1) greater robustness to analytical decisions that should be (but often are not) inconsequential to results; (2) holistic quantification of uncertainty across the entire statistical map rather than at individual voxels or regions; and (3) explicit accommodation of multiple plausible explanations, reducing false negatives while preserving stringent control over false positive rates.