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Haiying Lv

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Review Open access Jul 2026

Brain imaging-wide exploration of site-specific pain: genetic correlation and Mendelian randomization study

Abstract Background Observational studies have suggested that brain imaging-derived phenotypes (IDPs) may serve as specific markers of pain-related phenotypes and severity. However, the shared genetic architecture between pain and brain IDPs and their potential causal relationships remains unclear. Methods We applied linkage disequilibrium score regression and Mendelian randomization (MR) analyses to uncover genetic correlations and potential causal links of brain structural (33,224 UK Biobank participants) and functional (47,276 UK Biobank participants) changes with site-specific pain phenotypes (approximately 500,000 Finngen participants). The scoping literature review was conducted to compare current findings with previous observational studies. Results In this study, we identified 559 significant genetic correlations between 587 structural IDPs and 13 pain-related phenotypes. Using MR analyses, we found that genetic liability to headache, migraine, joint pain, and sciatica was causally associated with alterations in 15 structural IDPs. Additionally, changes in the surface area of three brain regions were linked to a lower risk of sciatica, low back pain, and overall pain. Among the six pain-related phenotypes associated with structural IDPs, further analyses demonstrated putative causal relationships between functional IDPs and these conditions. Notably, headache exhibited both significant structural and functional changes across three key brain regions: the superior frontal gyrus, lingual gyrus, and paracentral lobule. Conclusions These findings provide novel insights into the genetic correlations and genetically inferred associations between pain and neurobiological abnormalities from neuroimaging perspectives, with structural alterations as the primary findings and functional changes as complementary exploratory evidence, advancing the understanding of pain-related mechanisms.

Weian Mao, Peipei Ye, Shuwei Wang et al. · 0 citations