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Updated: Jun 5, 2026
10:25
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Robust lesion network mapping reveals genuine symptom-specific networks.
Weigang Cui1, Jiayi Zhu1,2, Yang Long1,2
1Changping Laboratory, Beijing, China.
Biorxiv : the Preprint Server for Biology
|June 4, 2026
Summary
Robust lesion network mapping (rLNM) improves statistical validity and symptom specificity for brain lesion studies. This new framework effectively controls false positives, revealing meaningful brain networks associated with specific symptoms.
Area of Science:
- Neuroscience
- Brain Imaging
- Network Science
Background:
- Lesion network mapping (LNM) integrates brain lesions with functional networks but faces statistical validity and specificity debates.
- Existing methods struggle to differentiate true symptom networks from coincidental findings.
Purpose of the Study:
- Introduce a null model-based robust LNM (rLNM) framework.
- Enable rigorous sensitivity and symptom-specificity testing for brain lesion data.
Main Methods:
- Developed a novel rLNM framework incorporating null model-based statistical testing.
- Applied rLNM to diverse lesion-based and task-based neuroimaging datasets.
- Validated network findings against known biological and clinical data.
Main Results:
- rLNM successfully identified biologically meaningful functional networks associated with specific symptoms.
- The framework demonstrated high specificity, effectively controlling for false positives across datasets.
- rLNM analysis of 10 lesion conditions (333 lesions) and 4 task conditions (706 experiments) confirmed its robustness.
Conclusions:
- The rLNM framework provides a statistically robust method for lesion network analysis.
- rLNM enhances the reliability of identifying brain networks linked to neurological symptoms.
- This approach offers improved diagnostic and prognostic potential in clinical neuroscience.
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