Investigating the methodological foundation of lesion network mapping
Martijn P van den Heuvel1,2, Ilan Libedinsky3, Sebastian Quiroz Monnens3
1Department of Neurosciences, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. martijn.vanden.heuvel@vu.nl.
Nature Neuroscience
|January 16, 2026
Summary
Lesion network mapping (LNM) may not accurately identify distinct brain networks in neurological disorders. This neuroimaging method repeatedly samples the same data, leading to similar network findings across different conditions.
Area of Science:
- Neuroimaging
- Network Neuroscience
- Computational Psychiatry
Background:
- Lesion network mapping (LNM) links brain lesions to functional alterations using functional connectivity (FC) data.
- LNM is applied across various neurological and psychiatric conditions, including addiction, depression, psychosis, and epilepsy.
- Observed similarities in identified networks across diverse conditions prompted a re-examination of LNM methodology.
Purpose of the Study:
- To investigate the methodological underpinnings of observed similarities in lesion network mapping (LNM) results.
- To identify potential limitations in the LNM framework regarding the specificity of identified brain networks.
Main Methods:
- Re-analysis of data from multiple LNM studies.
- Assessment of the methodological foundations of the LNM framework.
- Examination of the repetitive sampling of functional connectivity (FC) matrices within LNM.
Main Results:
- Lesion network mapping (LNM) fundamentally involves repetitive sampling of a single functional connectivity (FC) matrix.
- This repetitive sampling leads to the mapping of diverse brain alterations onto nonspecific FC properties.
- Consequently, LNM produces highly similar network findings across different neurological and psychiatric conditions.
Conclusions:
- A foundational limitation exists in LNM due to its reliance on repetitive sampling of the same FC matrix.
- This limitation results in nonspecific network identification, cautioning against its use for studying distinct biological networks in brain disorders.
- The findings advocate for the development of novel network-mapping methodologies based on first principles.
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