Robust lesion network mapping reveals symptom-specific circuits

Robust lesion network mapping (rLNM) recovers symptom-specific brain networks across 10 lesion datasets (384 lesions) and 339 task-fMRI experiments, with mean r = 0.044.

• Why it matters: Prior lesion network mapping can yield similar maps for unrelated symptoms, raising doubts about statistical validity and whether network-symptom links are truly specific.
• What they did: rLNM adds an anatomy-preserving spatial null model to control connectome hub bias and uses hypothesis-driven contrasts to test selective specificity across related symptoms.
• The result: In 56 neurosurgical patients, rLNM predicts postoperative language decline at the single-patient level and reduces similarity to the connectome hub pattern (r = 0.12 vs. 0.33).

AI summary of the abstract · may contain errors · not medical advice · read the paper.