Group-level computational electric field dosimetry for optimization of clinical transcranial magnetic stimulation

Background. Transcranial magnetic stimulation (TMS) is used to study brain function and to treat neuropsychiatric disorders. Clinical protocols prescribe the coil placement using scalp landmarks, but inter-individual differences in head and brain anatomy mean that the same landmark produces different electric field doses at the targeted region in different people, which is one likely contributor to variable treatment outcomes.

Objective. Determine coil placements, defined in terms of scalp landmarks that clinicians can use without individual MRI, that deliver a strong and consistent electric field dose to the targeted brain region across the population.

Approach. We assembled a population of 200 MRI-derived head models with co-registered scalps and cortices. For each subject and each of a large number of candidate coil placements we compute the electric field at the target, then choose the landmark-based placement that maximizes the electric field while minimizing its variation across subjects. The repeated simulations are made tractable by our probabilistic matrix decomposition auxiliary dipole method, which predicts the electric field for any coil placement in milliseconds after a short set-up stage.

Main results. The fast solver reproduces brute-force electric fields with less than 2% error in most subjects and less than 3% in all 200 subjects, and evaluates over one million coil placements in 9.5 hours instead of more than five years. The resulting group-level coil placements reduce the variation of the electric field dose across subjects by a factor of about three relative to standard landmark-based placement.

Significance. Population-level scalp landmarks derived from computational dosimetry give clinical TMS a more consistent electric field dose without requiring per-patient imaging, potentially reducing variability in outcomes and increasing the robustness of TMS protocols.

Publications.

N. I. Hasan (G), D. Wang (PD), and L. J. Gomez, "Fast and accurate computational E-field dosimetry for group-level transcranial magnetic stimulation targeting," Computers in Biology and Medicine, vol. 167, pp. 107614, 2023. link

D. Wang (PD), N. I. Hasan (G), M. Dannhauer, A. C. Yucel, and L. J. Gomez, "Fast computational E-field dosimetry for transcranial magnetic stimulation using adaptive cross approximation and auxiliary dipole method (ACA-ADM)," NeuroImage, vol. 267, pp. 119850, 2023. link

L. J. Gomez, M. Dannhauer, and A. V. Peterchev, "Fast computational optimization of TMS coil placement for individualized electric field targeting," NeuroImage, vol. 228, pp. 117696, 2021. link

M. Dannhauer, L. J. Gomez, P. L. Robins, D. Wang (PD), N. I. Hasan (G), A. Thielscher, H. R. Siebner, Y. Fan, and Z.-D. Deng, "Electric Field Modeling in Personalizing Transcranial Magnetic Stimulation Interventions," Biological Psychiatry, vol. 95, no. 6, pp. 494-501, 2024 (invited). link

N. Goswami, M. Shen, L. J. Gomez, M. Dannhauer, M. A. Sommer, and A. V. Peterchev, "A semi-automated pipeline for finite element modeling of electric field induced in nonhuman primates by transcranial magnetic stimulation," Journal of Neuroscience Methods, vol. 408, pp. 110176, 2024. link

N. I. Hasan (G) and L. J. Gomez, "Group-Level Optimum Coil Placement for Transcranial Magnetic Stimulation (TMS)," International Applied Computational Electromagnetics Society Symposium, May 2024.

N. I. Hasan (G) and L. J. Gomez, "Group-Level Optimized E-field Dosimetry Estimation in Transcranial Magnetic Stimulation (TMS)," Progress In Electromagnetics Research Symposium, April 2024.