Robust lesion network mapping reveals symptom-specific circuits
Year: 2026
Type: preprint
Abstract: Lesion network mapping (LNM) is a powerful tool for linking distributed brain lesions to symptom-relevant networks, but similar LNM maps across unrelated symptoms have raised concerns about its statistical validity and specificity. Here we introduce robust LNM (rLNM), a statistically principled framework that combines two complementary controls: an anatomy-preserving spatial null model to account for connectome-derived hub bias shared across symptoms, and hypothesis-driven contrasts to resolve selective specificity among related symptoms. rLNM effectively controls both false positives and negatives. Across 10 lesion datasets (384 lesions) and two task-fMRI meta-analyses (339 experiments), rLNM recovers distinct symptom-specific networks (mean r = 0.044), which show significantly reduced similarity to the connectome hub pattern (r = 0.12 vs. 0.33 in conventional LNM). In 56 patients with neurosurgical lesions, rLNM predicts postoperative language decline at the single-patient level. rLNM thus resolves these concerns and lays a unified statistical foundation for network-symptom inferenc.
Institutions: Changping Laboratory, Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, First Affiliated Hospital of Fujian Medical University, University of Electronic Science and Technology of China, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Capital Medical University, Beijing Sanbo Brain Hospital, Peking University, Washington University in St. Louis, The University of Melbourne
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Motion-robust brain MRI for infants, PEDIATRIC BRAIN INJURY RECOVERY VIA USE-DRIVEN FUNCTIONAL NETWORK REORGANIZATION, Visual biofeedback to reduce head motion during MRI scans, Solving the MRI motion problem with Framewise Integrated Real-Time MRI Monitoring (FIRMM) software, Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD, Behavioral feedback and rewards for improving functional brain mapping in presurgical pediatric patients
OpenAlex ID: W7161627084