OBJECTIVE: Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique used for research and clinical applications. Existent TMS coils are limited in their precision of spatial targeting (focality), especially for deeper targets. This paper presents a methodology for designing TMS coils to achieve optimal trade-off between the depth and focality of the induced electric field (E-field), as well as the energy required by the coil. APPROACH: A multi-objective optimization technique is used for computationally designing TMS coils that achieve optimal trade-offs between E-field focality, depth, and energy (fdTMS coils). The fdTMS coil winding(s) maximize focality (minimize the volume of the brain region with E-field above a given threshold) while reaching a target at a specified depth and not exceeding predefined peak E-field strength and required coil energy. Spherical and MRI-derived head models are used to compute the fundamental depth-focality trade-off as well as focality-energy trade-offs for specific target depths. MAIN RESULTS: Across stimulation target depths of 1.0-3.4 cm from the brain surface, the suprathreshold volume can be theoretically decreased by 42%-55% compared to existing TMS coil designs. The suprathreshold volume of a figure-8 coil can be decreased by 36%, 44%, or 46%, for matched, doubled, or quadrupled energy. For matched focality and energy, the depth of a figure-8 coil can be increased by 22%. SIGNIFICANCE: Computational design of TMS coils could enable more selective targeting of the induced E-field. The presented results appear to be the first significant advancement in the depth-focality trade-off of TMS coils since the introduction of the figure-8 coil three decades ago, and likely represent the fundamental physical limit.
OBJECTIVE: Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique used for research and clinical applications. Existent TMS coils are limited in their precision of spatial targeting (focality), especially for deeper targets. This paper presents a methodology for designing TMS coils to achieve optimal trade-off between the depth and focality of the induced electric field (E-field), as well as the energy required by the coil. APPROACH: A multi-objective optimization technique is used for computationally designing TMS coils that achieve optimal trade-offs between E-field focality, depth, and energy (fdTMS coils). The fdTMS coil winding(s) maximize focality (minimize the volume of the brain region with E-field above a given threshold) while reaching a target at a specified depth and not exceeding predefined peak E-field strength and required coil energy. Spherical and MRI-derived head models are used to compute the fundamental depth-focality trade-off as well as focality-energy trade-offs for specific target depths. MAIN RESULTS: Across stimulation target depths of 1.0-3.4 cm from the brain surface, the suprathreshold volume can be theoretically decreased by 42%-55% compared to existing TMS coil designs. The suprathreshold volume of a figure-8 coil can be decreased by 36%, 44%, or 46%, for matched, doubled, or quadrupled energy. For matched focality and energy, the depth of a figure-8 coil can be increased by 22%. SIGNIFICANCE: Computational design of TMS coils could enable more selective targeting of the induced E-field. The presented results appear to be the first significant advancement in the depth-focality trade-off of TMS coils since the introduction of the figure-8 coil three decades ago, and likely represent the fundamental physical limit.
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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