Dataset Information

Fast computational optimization of TMS coil placement for individualized electric field targeting.

ABSTRACT:

Background

During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort.

Objective

To improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols.

Method

ADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles.

Results

Using ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 min on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. ADM is implemented in SimNIBS 3.2.

Conclusion

ADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 min enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability.

SUBMITTER: Gomez LJ

PROVIDER: S-EPMC7956218 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fast computational optimization of TMS coil placement for individualized electric field targeting.

Gomez Luis J LJ Dannhauer Moritz M Peterchev Angel V AV

NeuroImage 20201230

<h4>Background</h4>During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Determination of the coil position and orientation that best achieve this objective presently requires a large computational effort.<h4>Objective</h4>To improve the accuracy of T ...[more]

PMID: 33385544

Similar Datasets

Project description:PurposeMultichannel Transcranial Magnetic Stimulation (mTMS) arrays enable multiple sites to be stimulated simultaneously or sequentially under electronic control without moving the system's stimulation coils. Here, we build and characterize the performance of a novel modular 3-axis TMS coil that can be utilized as a unit element in large-scale multichannel TMS arrays.MethodsWe determined the basic physical characteristics of the 3-axis TMS coil x-, y- and z-elements using a custom 2-channel programmable stimulator prototype. We mapped the temporal rate-of-change of the induced magnetic field (dB/dt) on a 2D plane parallel to the coil surface (including an extended line for full spatial coverage) and compared those values with predictions from magnetic field simulations. Temperature measurements were carried out to assess the incorporated air-cooling method. We measured the mutual and self-inductances of the x/y/z-elements to assess coupling between them. Additionally, we measured and calculated the coupling between z-elements in the array configuration. Finally, we performed electric field simulations to evaluate the stimulation intensity and focality of the coil and compared the results to conventional TMS coils as well as demonstrated suitability of the 3-axis coil for a multichannel array configuration.ResultsThe experimentally obtained dB/dt values validated the computational model of the 3-axis coil and therefore confirmed that both the coil and stimulator system are operating as intended. The air-cooling system was effective for brief high-frequency pulse trains and extended single- and paired-pulse TMS protocols. The electromagnetic simulations suggested that an array of the 3-axis coils would have comparable stimulation intensity to conventional TMS coils, therefore enabling clearly suprathreshold stimulation of the human brain. The recorded coil coupling between the x/y/z-elements was < 1% and the maximal coupling between z-elements in the array configuration was 1.8% and 3.4% for the measured and calculated values, respectively.ConclusionWe presented a 3-axis coil intended for multichannel TMS arrays. The electromagnetic measurements and simulations verified that the coil fabrication met the desired specifications and that the inductive coupling between the elements was negligible. The air-cooled 3-axis TMS coil appears suitable to be used as an element in multichannel TMS arrays.

Project description:BackgroundUnique amongst brain stimulation tools, transcranial direct current stimulation (tDCS) currently lacks an easy or widely implemented method for individualizing dosage.ObjectiveWe developed a method of reverse-calculating electric-field (E-field) models based on Magnetic Resonance Imaging (MRI) scans that can estimate individualized tDCS dose. We also evaluated an MRI-free method of individualizing tDCS dose by measuring transcranial magnetic stimulation (TMS) motor threshold (MT) and single pulse, suprathreshold transcranial electrical stimulation (TES) MT and regressing it against E-field modeling. Key assumptions of reverse-calculation E-field modeling, including the size of region of interest (ROI) analysis and the linearity of multiple E-field models were also tested.MethodsIn 29 healthy adults, we acquired TMS MT, TES MT, and anatomical T1-weighted MPRAGE MRI scans with a fiducial marking the motor hotspot. We then computed a "reverse-calculated tDCS dose" of tDCS applied at the scalp needed to cause a 1.00 V/m E-field at the cortex. Finally, we examined whether the predicted E-field values correlated with each participant's measured TMS MT or TES MT.ResultsWe were able to determine a reverse-calculated tDCS dose for each participant using a 5 × 5 x 5 voxel grid region of interest (ROI) approach (average = 6.03 mA, SD = 1.44 mA, range = 3.75-9.74 mA). The Transcranial Electrical Stimulation MT, but not the Transcranial Magnetic Stimulation MT, significantly correlated with the ROI-based reverse-calculated tDCS dose determined by E-field modeling (R2 = 0.45, p < 0.001).ConclusionsReverse-calculation E-field modeling, alone or regressed against TES MT, shows promise as a method to individualize tDCS dose. The large range of the reverse-calculated tDCS doses between subjects underscores the likely need to individualize tDCS dose. Future research should further examine the use of TES MT to individually dose tDCS as an MRI-free method of dosing tDCS.

Dataset Information

Fast computational optimization of TMS coil placement for individualized electric field targeting.

Background

Objective

Method

Results

Conclusion

Publications

Fast computational optimization of TMS coil placement for individualized electric field targeting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets