Sign in
NeurosciencePhysics in Medicine and Biology
Peer-reviewed journal article

Characterizing an electronic-robotic targeting platform for precise and fast brain stimulation with multi-locus transcranial magnetic stimulation

Renan H. MatsudaiD, Victor Hugo SouzaiD, Thais Cunha MarchettiiD, Ana M SotoiD, Olli‐Pekka KahilakoskiiD, Mikael LaineiD, Heikki SinisaloiD, Dubravko KičićiD, Pantelis LioumisiD, Risto Juhani IlmoniemiiD, Oswaldo BaffaiD

Abstract

1.BackgroundMulti-locus TMS (mTMS) enables precise electronic control of brain stimulation targeting, eliminating the need for physical coil movement. However, with a small number of coils, the stimulation area is constrained, and manually handling the coil array is cumbersome. Combining electronic mTMS targeting with robotics will enable automated, user-independent, and precise brain stimulation protocols. ObjectiveCharacterizing an open-source electronic-robotic mTMS platform for rapid and accurate brain stimulation targeting. MethodsWe developed an automated robotic mTMS positioning platform. The accuracy of the system was quantified with a TMS characterizer that measures the TMS-induced electric field on a spherical cortex model. We used a 5-coil mTMS device equipped with a set of five coils coupled to a collaborative robot. The induced electric-field distortion generated by robot coupling was evaluated for each coil. We compared the accuracy of robotic-electronic targeting by repositioning the mTMS coil set with the robotic and the conventional manual positioning. ResultsOur collaborative robot-based system offers submillimeter precision and autonomy in positioning mTMS coil sets. The electronic-robotic mTMS platform was approximately 1.8 mm and 1.0{degrees} more accurate than the conventional manual positioning. Integrating robotics and mTMS automates brain stimulation procedures, resulting in minimal reliance on user expertise and subjective analysis. ConclusionOur open-source platform combining rapid mTMS targeting with robotic precision enhances the safety and reproducibility of brain stimulation techniques, enabling more efficient and reliable outcomes than previous techniques.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers
Citations
12
FWCI
3.95
Field percentile
top 6%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISGoetz '19Nieminen '21Matsuda '26Liu '22Koehler '23Dannhauer '22
79% semantic matchNeuroscience
Stefan M. Goetz, I. Cassie Kozyrkov, Bruce M. Luber+4 more·Journal of Neural Engineering·Jun 12, 2019· 43 citations