Robust lesion network mapping reveals symptom-specific circuits
- 1Changping Laboratory
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.
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BibTeX
@misc{cui2026robust,
title = {{Robust lesion network mapping reveals symptom-specific circuits}},
author = {W. Cui and J. Zhu and Y. Long and F. Wang and Y. Yin and X. Wang and X. Fu and B. Zhang and Y. Lu and W. Zhang and J. Huang and L. Li and G. Luan and Y. Lin and E. M. Gordon and D. Wang and A. Zalesky and N. U. F. Dosenbach and J. Ren and H. Liu},
howpublished = {bioRxiv},
year = {2026},
month = may,
doi = {10.64898/2026.05.18.725812},
url = {https://www.biorxiv.org/content/10.64898/2026.05.18.725812v3},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Robust lesion network mapping reveals symptom-specific circuits
AU - W. Cui
AU - J. Zhu
AU - Y. Long
AU - F. Wang
AU - Y. Yin
AU - X. Wang
AU - X. Fu
AU - B. Zhang
AU - Y. Lu
AU - W. Zhang
AU - J. Huang
AU - L. Li
AU - G. Luan
AU - Y. Lin
AU - E. M. Gordon
AU - D. Wang
AU - A. Zalesky
AU - N. U. F. Dosenbach
AU - J. Ren
AU - H. Liu
PY - 2026
DA - 2026/05/18
PB - bioRxiv
DO - 10.64898/2026.05.18.725812
UR - https://www.biorxiv.org/content/10.64898/2026.05.18.725812v3
AB - 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.
ER -