Jaakko O Nieminen1, Lari M Koponen2, Risto J Ilmoniemi2. 1. Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Aalto TMS Laboratory, Aalto Neuroimaging, Aalto University, Espoo, Finland; Department of Psychiatry, University of Wisconsin, Madison, WI, USA. Electronic address: jaakko.nieminen@aalto.fi. 2. Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Aalto TMS Laboratory, Aalto Neuroimaging, Aalto University, Espoo, Finland.
Abstract
BACKGROUND: In transcranial magnetic stimulation (TMS) a strong, brief current pulse driven through a coil is used for non-invasively stimulating the cortex. Properties of the electric field (E-field) induced by the pulse together with physiological parameters determine the outcome of the stimulation. In research and clinical use, TMS is delivered using a wide range of different coils and stimulator units, all having their own characteristics; however, the parameters of the induced E-field are often inadequately known by the user. OBJECTIVE: To better understand how the use of a specific TMS device may affect the resulting cortical stimulation, our objective was to develop an instrument for automated measurement of the E-fields induced by TMS coils in spherically symmetric conductors approximating the head. METHODS: We built a saline-free, robotized measurement tool based on the triangle construction. The 5-mm-wide measurement probe allows complete sampling of the induced E-field at the studied depth. We used the instrument to characterize TMS coils and stimulators made by two companies. RESULTS: The measurements revealed that all tested stimulators performed as expected, but we also found significant differences between the different stimulators. Measurements of different coil specimens of the same stimulator models agreed with each other. CONCLUSION: The presented TMS calibrator allows a straightforward characterization of the E-fields induced by TMS coils. By performing measurements using this kind of a tool helps in ensuring that an investigator knows the properties of the E-field.
BACKGROUND: In transcranial magnetic stimulation (TMS) a strong, brief current pulse driven through a coil is used for non-invasively stimulating the cortex. Properties of the electric field (E-field) induced by the pulse together with physiological parameters determine the outcome of the stimulation. In research and clinical use, TMS is delivered using a wide range of different coils and stimulator units, all having their own characteristics; however, the parameters of the induced E-field are often inadequately known by the user. OBJECTIVE: To better understand how the use of a specific TMS device may affect the resulting cortical stimulation, our objective was to develop an instrument for automated measurement of the E-fields induced by TMS coils in spherically symmetric conductors approximating the head. METHODS: We built a saline-free, robotized measurement tool based on the triangle construction. The 5-mm-wide measurement probe allows complete sampling of the induced E-field at the studied depth. We used the instrument to characterize TMS coils and stimulators made by two companies. RESULTS: The measurements revealed that all tested stimulators performed as expected, but we also found significant differences between the different stimulators. Measurements of different coil specimens of the same stimulator models agreed with each other. CONCLUSION: The presented TMS calibrator allows a straightforward characterization of the E-fields induced by TMS coils. By performing measurements using this kind of a tool helps in ensuring that an investigator knows the properties of the E-field.
Authors: Zhiyong Zeng; Lari M Koponen; Rena Hamdan; Zhongxi Li; Stefan M Goetz; Angel V Peterchev Journal: J Neural Eng Date: 2022-03-17 Impact factor: 5.043
Authors: Gabriela P Tardelli; Victor H Souza; Renan H Matsuda; Marco A C Garcia; Pavel A Novikov; Maria A Nazarova; Oswaldo Baffa Journal: Brain Topogr Date: 2022-03-09 Impact factor: 4.275
Authors: Simone Rossi; Andrea Antal; Sven Bestmann; Marom Bikson; Carmen Brewer; Jürgen Brockmöller; Linda L Carpenter; Massimo Cincotta; Robert Chen; Jeff D Daskalakis; Vincenzo Di Lazzaro; Michael D Fox; Mark S George; Donald Gilbert; Vasilios K Kimiskidis; Giacomo Koch; Risto J Ilmoniemi; Jean Pascal Lefaucheur; Letizia Leocani; Sarah H Lisanby; Carlo Miniussi; Frank Padberg; Alvaro Pascual-Leone; Walter Paulus; Angel V Peterchev; Angelo Quartarone; Alexander Rotenberg; John Rothwell; Paolo M Rossini; Emiliano Santarnecchi; Mouhsin M Shafi; Hartwig R Siebner; Yoshikatzu Ugawa; Eric M Wassermann; Abraham Zangen; Ulf Ziemann; Mark Hallett Journal: Clin Neurophysiol Date: 2020-10-24 Impact factor: 4.861