Optimizing Network-Level TMS-fMRI: Benchmarking a Novel TMS-Compatible "Sushi" MR Coil

IntroductionConcurrent TMS-fMRI allows to observe how stimulation affects the target region and connected brain-wide networks. However, hardware limitations represent a major constraint: standard MR head coils provide high imaging sensitivity but no room for the TMS coil, whereas available TMS-compatible MR head coils offer access but low or strongly inhomogeneous signal. MethodsWe developed and benchmarked a flexible "Sushi" MR-receive setup, assembled from two repurposed 18-channel body arrays, that allows TMS coil positioning while maintaining full-brain coverage. Resting-state (n = 12) and task-based working memory (n = 8) fMRI were acquired with the Sushi coil, a commercially available 2x7-channel Surface-coil setup, and a standard Siemens 64-channel (non-TMS-capable) head coil. The image-quality cost of TMS capability was addressed by acquiring multi-echo fMRI to allow post-hoc optimization of signal-to-noise ratio (SNR), but no TMS was delivered. ResultsFrom resting-state fMRI, the Sushi mapped known canonical resting-state networks (RSNs) comparably to the 64-channel reference and was superior to the Surface coils, in particular for the default-mode, auditory, and visual networks. Task-fMRI data showed that Sushi recovered the working memory network more similarly to the 64-channel reference than the Surface coil. Temporal SNR was optimized for all coil acquisitions yielding [~]30-50% gain and improving between-coil comparability for RSNs identification and task-related activity. ConclusionsTogether, post-hoc multi-echo optimization and the Sushi coil setup provide a low-cost, ready-to-use solution for whole-brain concurrent TMS-fMRI recordings. Combining stimulation access with reliable functional network readouts is essential to probe mechanisms of TMS and to inform effective TMS interventions targeting altered brain systems.