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bioRxiv · 10.64898/2026.01.27.701271

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

Xiong, Y.·Burke, M.·Melo, L.·Takahashi, K.·Lueckel, M.·Bergmann, T. O.·Nitsche, M. A.·Genc, E.·Chiappini, E.
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Abstract

Concurrent TMS-fMRI can map how stimulation affects both the targeted cortex and connected brain-wide networks, but this requires MR receive hardware that allows TMS coil placement while preserving reliable whole-brain BOLD sensitivity. We developed and benchmarked a practical TMS-compatible "Sushi" MR receive setup assembled from two flexible 18-channel body arrays. Across six experiments, we tested functional readout validity, signal quality, and active TMS-fMRI compatibility. Resting-state fMRI (n = 12) and verbal N-back task-fMRI (n = 8) were acquired with Sushi, a commercially available 2x7-channel Surface setup, and a standard 64-channel head/neck array. Functional similarity to the 64-channel reference was quantified with spatial overlap, and multi-echo combination (MEcomb) was tested as a post-acquisition signal optimization strategy. Sushi recovered subject-specific resting-state networks that more closely matched the 64-channel reference than Surface, with no significant difference from the 64-channel test-retest reference. For task-fMRI, MEcomb increased task-map similarity for Sushi, whereas setup comparisons within each pipeline were not significant. MEcomb also improved resting-state similarity and increased temporal signal-to-noise ratio (tSNR) across receive setups. In phantom measurements, TMS coil placement produced spatially graded tSNR reductions relative to the no-TMS-coil condition, strongest near the coil. In one participant, active interleaved single-pulse TMS-fMRI over two cortical sites showed no detectable pulse-locked image artifacts; whole-brain MEcomb tSNR during active TMS-fMRI was reduced by 2.6-6.9% relative to the no-coil/no-stimulation reference. Together, Sushi and MEcomb provide complementary hardware and processing tools for TMS-compatible whole-brain fMRI. This combination supports network-level functional readouts while preserving feasibility for active interleaved TMS-fMRI.

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Xiong, Y., Burke, M., Melo, L., Takahashi, K., Lueckel, M., Bergmann, T. O., Nitsche, M. A., Genc, E., Chiappini, E.. 2026-01-29. Optimizing Network-Level TMS-fMRI: Benchmarking a Novel TMS-Compatible "Sushi" MR Coil. https://doi.org/10.64898/2026.01.27.701271

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