Fast estimation and adjustment of TMS electric fields with motion-tracking and a-priori simulations
Poster No:
65
Submission Type:
Abstract Submission
Authors:
Sarah Grosshagauer1, Michael Woletz1, Christian Windischberger2
Institutions:
1Medical University of Vienna, Vienna, Austria, 2Medical University of Vienna, Vienna, Vienna
First Author:
Co-Author(s):
Introduction:
Precise control of delivered stimulation is essential for the accuracy and effectiveness of TMS experiments and therapies. We present a methodology which allows fast interpolation of the induced electric fields in a target region, which can be applied e.g. for motion-tracking informed dose adjustments. Compared to previous methods, we a-priori perform simulations in a large volume around the target, which reduces computational effort during adjustments to a minimum and allows incorporation of dose adjustments in a variety of TMS protocols.
Methods:
The developed methodology includes estimating the average E-field magnitude in a defined target ROI (e.g. 5 mm diameter) based on a-priori simulations. To allow for swift simulation of a large number of positions, we applied the fast ADM method (Gomez et al., 2021) as implemented within SimNIBS 4.0 (Thielscher et al., 2015). Sampling is performed in a defined volume of interest (e.g. cylinder, r=20 mm, h=10 mm) centered above a cortical target which should cover the expected range of motion in terms of translation and includes a large variety in possible coil orientations and tilts.
For fast evaluation of the induced E-fields, search trees were constructed for available coil positions of the a-priori sampling using the Euclidean distance as distance metric, as well as for all orientations, using the minimal angle between rotations as distance metric. The E-field is then estimated using an inverse-distance sampling scheme based on the orientations, whose result is subsequently interpolated using a similar scheme based on the positions in a neighborhood.
We validated this algorithm using different sampling schemes and random coil walks within the simulated volume of interest. We performed 100 walks with 100 steps each and compared the interpolation results to full simulations of the respective positions to get an estimate for interpolation accuracy.
Finally, we tested the interpolation algorithm on a motion time course recorded with a healthy male participants during TMS/fMRI applied to the left dorsolateral prefrontal cortex. The experiment included TMS pulses every 30 s and the participant was asked to slightly move their head during specific time windows to create exaggerated motion patterns. Motion of coil and participant was tracked inside the MR scanner using a Polaris camera and 3D printed retro reflective trackers. We extracted coil positions at the TMS triggers and compared the delivered E-field in case of static and adjusted stimulation intensity.
For fast evaluation of the induced E-fields, search trees were constructed for available coil positions of the a-priori sampling using the Euclidean distance as distance metric, as well as for all orientations, using the minimal angle between rotations as distance metric. The E-field is then estimated using an inverse-distance sampling scheme based on the orientations, whose result is subsequently interpolated using a similar scheme based on the positions in a neighborhood.
We validated this algorithm using different sampling schemes and random coil walks within the simulated volume of interest. We performed 100 walks with 100 steps each and compared the interpolation results to full simulations of the respective positions to get an estimate for interpolation accuracy.
Finally, we tested the interpolation algorithm on a motion time course recorded with a healthy male participants during TMS/fMRI applied to the left dorsolateral prefrontal cortex. The experiment included TMS pulses every 30 s and the participant was asked to slightly move their head during specific time windows to create exaggerated motion patterns. Motion of coil and participant was tracked inside the MR scanner using a Polaris camera and 3D printed retro reflective trackers. We extracted coil positions at the TMS triggers and compared the delivered E-field in case of static and adjusted stimulation intensity.
Results:
Based on 10.000 random coil positions, the Pearson correlation of interpolated E-fields vs. full simulations in the target ROI was between 0.828 (low density sampling) and 0.963 (high density sampling), the mean relative error was 7.2% for low density and 2.2% for the tested high density sampling (see figure 1). Time per interpolation on a standard PC is approximately 3 ms per sample. An exemplary motion time course during TMS/fMRI and the respective changes in target E-field are shown in figure 2. It can be clearly seen that using the adjustment method based on the fast interpolation method allows for a significant reduction in E-field variability in the target ROI.
Conclusions:
The developed algorithm allows to interpolate the E-field induced in the target region with high accuracy and fast speed, also in comparison to previously presented fast E-field calculation methods (Park et al., 2024). This allowed for embedding in a dedicated Python-based software combining motion tracking, interpolation and stimulator communication to adjust intensity. Finally, this enables integration in a concurrent TMS/fMRI setup, where the timing of stimulations is known in advance and stimulation intensity can be adjusted shortly prior to the stimulation. Therefore, this method can also be combined with intermittent Theta Burst protocols, increasing its clinical relevance.
Brain Stimulation:
TMS 1
Modeling and Analysis Methods:
Methods Development 2
Motion Correction and Preprocessing
Other Methods
Keywords:
Transcranial Magnetic Stimulation (TMS)
Other - E-field modelling, motion tracking
1|2Indicates the priority used for review
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Provide references using APA citation style.
Park, T. Y., Franke, L., Pieper, S., Haehn, D., & Ning, L. (2024). A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation. Biomedical Engineering Letters, 14(3), 393–405. https://doi.org/10.1007/s13534-024-00373-4
Thielscher, A., Antunes, A., & Saturnino, G. B. (2015). Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS? 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 222–225. https://doi.org/10.1109/EMBC.2015.7318340