Cortical fold geometry modulates transcranial magnetic stimulation electric field strength and peak displacement

• Published in: Scientific reports Vol. 15; no. 1; pp. 19361 - 10
• Main Authors: Wang, Jinting, Zhai, Jiayu, Wang, Yiding, Lin, JiuGe, Pan, Donghua, Li, Liyi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.06.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/1453/2105; 639/766/747; Brain research; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Electric field analysis; Electric fields; Geometry; Gyral morphology; Humanities and Social Sciences; Humans; Magnetic fields; Male; Morphology; multidisciplinary; Optimization; Science; Science (multidisciplinary); Simplified gyrus model; Transcranial magnetic stimulation; Transcranial magnetic stimulation (TMS); Transcranial Magnetic Stimulation - methods; Transcranial magnetic stimulation (TMS); Gyral morphology; Simplified gyrus model; Electric field analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-01911-5

This study investigated how cortical folding morphology influences transcranial magnetic stimulation (TMS)-induced electric fields. We constructed a simplified multi-layered curved cortical fold model to quantitatively analyze the relationships between key morphological parameters ( e.g. , cross-sectional shape and gyral crest curvature) and spatial electric field characteristics. The results demonstrated that deeper cortical folds enhance peak electric field strength and promote field penetration into deeper brain regions, while crest curvature governs directional field intensity variations and modulates peak displacement distances. Validation in realistic head models further confirmed that cross-sectional shape impacts field strength, and apical curvature drives spatial shifts in peak locations. The findings establish actionable connections between cortical morphology and electric field metrics, offering practical guidance for adjusting stimulation parameters in scenarios where precise field modeling is unavailable. Furthermore, the identified morphological predictors may expedite coil placement optimization in subject-specific models, improving the efficiency of TMS protocol design.