Renan H. Matsuda

This paper introduces multichannel TMS (mTMS) for robust voxel-wise brain mapping, showing it outperforms single-coil TMS in accuracy and noise resilience.

This paper introduces an adaptive Bayesian method for localizing motor representation areas using TMS, reducing the number of stimuli needed for accurate localization.

This paper develops an EEG-guided robotic TMS system that controls both pulse timing and location in real time, improving closed-loop neurostimulation for research and clinical applications.

This paper uses machine learning to predict TMS stimulation sites using tractography and myographic responses, showing improved performance in some subjects.

This paper develops an automated algorithm for determining motor hotspots and thresholds in TMS, achieving faster and more accurate results than previous methods.

This paper characterizes an open-source electronic-robotic mTMS platform that improves the accuracy and automation of brain stimulation targeting.

This paper develops a real-time tractography-assisted TMS neuronavigation system and demonstrates its feasibility through comparison with offline tractography results.

This study examines coactivation and overlapping of cortical motor representations for forearm and hand muscles using nTMS, finding high overlap and stronger stimulation intensity effects on size than topography.