The bystander effect: lesion spatial extent as a foundational artifact in Lesion Network Mapping
- 1Hebrew University in Jerusalem
Abstract
Lesion network mapping (LNM) has provided a compelling framework in clinical neuroscience, linking focal brain damage at heterogeneous anatomical locations to shared functional networks across behavioural syndromes. Recently, the methodological specificity of LNM has come under discussion following observations that derived network maps tend to converge onto the normative connectomes generic degree distribution. While current efforts largely treat this convergence as a statistical inference challenge addressable via post-hoc spatial null models or individual-level analyses, we identify a foundational upstream measurement artifact operating within individual patients: the lesion "bystander effect". Because pathological lesions follow biological constraints rather than functional brain parcellations, a typical lesion encapsulates both a symptom-driving critical node and surrounding asymptomatic bystander tissue. By averaging connectivity profiles across the entire lesion mask, LNM mechanically dilutes focal clinical signals. Across empirical clinical cohorts, individual-level machine learning classifiers, synthetic within-cohort lesion unions, and whole-brain spherical simulations, we demonstrate that increasing lesion extent deterministically drives network maps toward the connectome degree vector following a mathematical power law. Fulfilling the clinical promise of LNM will therefore require computational strategies that isolate the causal functional core from surrounding bystander tissue prior to network projection.
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BibTeX
@misc{edelman2026bystander,
title = {{The bystander effect: lesion spatial extent as a foundational artifact in Lesion Network Mapping}},
author = {S. Edelman and U. Elias and S. Arzy},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.17.26365701},
url = {https://www.medrxiv.org/content/10.64898/2026.10.17.26365701v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - The bystander effect: lesion spatial extent as a foundational artifact in Lesion Network Mapping
AU - S. Edelman
AU - U. Elias
AU - S. Arzy
PY - 2026
DA - 2026/10/17
PB - medRxiv
DO - 10.64898/2026.10.17.26365701
UR - https://www.medrxiv.org/content/10.64898/2026.10.17.26365701v1
AB - Lesion network mapping (LNM) has provided a compelling framework in clinical neuroscience, linking focal brain damage at heterogeneous anatomical locations to shared functional networks across behavioural syndromes. Recently, the methodological specificity of LNM has come under discussion following observations that derived network maps tend to converge onto the normative connectomes generic degree distribution. While current efforts largely treat this convergence as a statistical inference challenge addressable via post-hoc spatial null models or individual-level analyses, we identify a foundational upstream measurement artifact operating within individual patients: the lesion "bystander effect". Because pathological lesions follow biological constraints rather than functional brain parcellations, a typical lesion encapsulates both a symptom-driving critical node and surrounding asymptomatic bystander tissue. By averaging connectivity profiles across the entire lesion mask, LNM mechanically dilutes focal clinical signals. Across empirical clinical cohorts, individual-level machine learning classifiers, synthetic within-cohort lesion unions, and whole-brain spherical simulations, we demonstrate that increasing lesion extent deterministically drives network maps toward the connectome degree vector following a mathematical power law. Fulfilling the clinical promise of LNM will therefore require computational strategies that isolate the causal functional core from surrounding bystander tissue prior to network projection.
ER -