Fast and accurate computational E-field dosimetry for group-level transcranial magnetic stimulation targeting

• Published in: Computers in biology and medicine Vol. 167; p. 107614
• Main Authors: Hasan, Nahian I., Wang, Dezhi, Gomez, Luis J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2023; Elsevier Limited
• Subjects: Accuracy; Brain; Brain - physiology; Brain Mapping - methods; Computational neuroscience; Dosimetry; Group-level TMS; Humans; Internal Medicine; Low-rank matrix approximation; Magnetic fields; Other; Placement; Probabilistic matrix decomposition (PMD); Scalp; Transcranial magnetic stimulation; Transcranial magnetic stimulation (TMS); Transcranial Magnetic Stimulation - methods; Variation; Transcranial magnetic stimulation (TMS); Probabilistic matrix decomposition (PMD); Group-level TMS; Low-rank matrix approximation
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107614

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. These variations across individuals could in principle be minimized by developing a large database of head subjects and determining scalp landmarks that maximize E-field at the targeted brain region while minimizing its variation using computational methods. However, this approach requires repeated execution of a computational method to determine the E-field induced in the brain for a large number of subjects and coil placements. We developed a 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. 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 <3.5% in all subjects. We will present several examples of bench-marking analysis for our tool in terms of accuracy and speed. Furthermore, we will show the methods’ applicability for group-level optimization of coil placement for illustration purposes only. The software implementation link is provided in the appendix. [Display omitted] •A method for practical E-field informed group-level TMS coil placement is developed.•Determination of E-field informed optimal coil placement in seconds.•Enables close-loop and on-the-fly reconfiguration of TMS.•The initial set-up stage takes less than 10 hours.•The E-field is predicted for any coil placement in the brain within 3 milliseconds.