Year: 2026
Type: preprint
Abstract: Transcranial magnetic stimulation (TMS) is widely used for functional brain mapping and neuromodulation, but conventional neuronavigation systems typically rely on simplified volumetric brain models that do not fully capture subject-specific cortical geometry. We developed and evaluated a novel cortical-topography-guided framework for improving TMS planning, coil alignment, and stimulation reliability. The framework integrates an enhanced head-MRI co-registration strategy based on the iterative closest point (ICP) algorithm using a three-dimensional cortical mesh reconstructed from structural MRI. This approach was designed to improve neuronavigation by more accurately aligning the participant’s head with the MRI-derived cortical anatomy, thereby reducing co-registration error. Robot-assisted TMS (RA-TMS) was used to preserve the planned coil pose relative to cortical targets. Registration accuracy, coil alignment error, and motor-evoked potential (MEP) responses at the hand area of the primary motor cortex (M1) were evaluated and compared between RA-TMS and conventional manual TMS approach, which did not consistently preserve cortical topography-guided stimulation. ICP-based co-registration reduced the registration error between the head and MRI model from 5.83 ± 1.04 mm to 1.07 ± 0.03 mm. Compared with manual alignment, RA-TMS reduced translational error from 14.79 ± 8.03 mm to 5.22 ± 1.68 mm and rotational error from 7.01 ° ± 2.29 ° to 1.09 ° ± 0.33 ° ( p < 0.001). RA-TMS also produced larger and more consistent MEP responses than manual TMS (0.27 ± 0.05 mV vs. 0.03 ± 0.01 mV, p < 0.001). Incorporating cortical topography-guided planning, combined with refined co-registration and robotic stabilization, improves coil alignment accuracy and the reliability of stimulation outcomes.
Source: medRxiv
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7224094173