Group level computational E-field dosimetry for optimization of clinical transcranial magnetic stimulation
Background. Transcranial magnetic stimulation (TMS) is used to study brain function and treat mental health disorders. During TMS, a coil placed on the scalp induces an E-field in the brain that modulates its activity. TMS is known to stimulate regions that are exposed to a large E-field. Clinical TMS protocols prescribe a coil placement based on scalp landmarks. There are inter-individual variations in brain anatomy that result in variations in the TMS-induced E-field at the targeted region and its outcome.
Objective. The goal of this project is to minimized E-field variations of the E-field induced during clinical TMS.
Approach. We have a database of 200 subjects with corregistered scalps and cortices. This population of head models is used to determine scalp landmarks that maximize E-field at the targeted brain region while minimizing its variation using computational methods. Our approach involves repeated execution of a computational method to determine the E-field induced in the brain for a large number of subjects and coil placements.To rapidly execute the computation a novel probabilistic matrix decomposition-based approach for rapidly evaluating the E-field induced during TMS for a large number of coil placements due to a pre-defined coil model is used.
Main results. Our approach can determine the E-field induced in over 1 Million coil placements in 9.5 h, in contrast, to over 5 years using a brute-force approach. After the initial set-up stage, the E-field can be predicted over the whole brain within 2–3 ms and to 2% accuracy. We tested our approach in over 200 subjects and achieved an error of < 2 % in most and <3 % in all subjects. Furthermore, we showed that the coil placements derived using our platform can reduce variation of E-field dose accross subjects by a factor of 3.
Significance. Our approach enables accurate the determination of population level scalp landmarks that result in a more consistent E-field dose during clinical applications of TMS potentially reducing variability in TMS outcomes and increasing its robustness.
[J14] L. J. Gomez, M. Dannhauer and A. Peterchev, " Fast computational optimization of TMS coil placement for individualized electric field targeting," NeuroImage, vol. 228, no. 3, pp. 1-13, 2021.
[J19] D. Wang (PD), N. 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, 2023.
[J20] N. 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, 2023.