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Accurate tissue segmentation from including both T1-weighted and T2-weighted MRI scans significantly affect electric field simulations of prefrontal but not motor TMS

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Accurate tissue segmentation from including both T1-weighted and T2-weighted MRI scans significantly affect electric field simulations of prefrontal but not motor TMS Keywords: Electric field (E-field) modeling Transcranial magnetic stimulation (TMS) Finite element method (FEM) T1w structural MRI scan T2w structural MRI scan Computational modeling Computational electric field (E-field) modeling is a valuable tool to simulate the cortical effects of noninvasive brain stimulation based on a person's head anatomy. E-field modeling involves seg-mentation of a structural magnetic resonance imaging (MRI) scan into different tissue layers, and creation of an anatomically accurate head model. On this head model, the effects of noninvasive brain stimulation are then simulated. Given the interest in E-field modeling for understanding dose-response relationships and even prospective E-field dosing [1], it is important to maximize accuracy by critically evaluating E-field modeling methodology. Recently, we showed that head meshes created from T1w þ T2w MRI scans more accurately represent E-fields induced by high-definition transcranial electric current (tES) over the motor cortex than meshes created from T1w scans [2]. Further analyses indicated that the higher E-field variability of T1w only models was mostly attributable to poorer tissue layer segmentation, particularly of the cerebrospinal fluid (CSF) and skull. However, the use of E-field simulations is not exclusive to tES, but also relates to transcranial magnetic stimulation (TMS). Although tES and TMS both induce cortical E-fields to noninvasively alter neural activity, their differing mechanisms of actions (i.e., electric versus electromagnetic E-field generation) imply that the results of our previous work cannot be directly extrapolated to TMS. There is reason to believe that the more accurate tissue segmentation obtained from including an additional T2w scan might be less impactful for TMS modeling as TMS simulations were found to be less susceptible to head model and tissue accuracy decreases than tES simulations [3,4]. Here, we set out to extend our prior tES results to TMS. Furthermore , we aimed to test whether there is brain region specificity to simulation accuracy by simulating TMS over the motor and pre-frontal cortices. We examined the influence of tissue thicknesses between the coil and cortex at both regions of interest (ROIs), as variations in scalp-to-cortex distance (SCD) could be a potential source of differences, given that distance is a determinant of magnetic field strength [5]. We computed E-field models in 100 healthy younger adults (57 females, 22e35 years old), randomly selected from the Human Connectome Project dataset [6]. T1w and T2w structural MRI-scans were acquired with the Siemens MAGNETOM 3T scanner (for detailed scanning parameters, see Ref. [6]). Two finite element method (FEM) tetrahedral head meshes were constructed per participant with headreco (Fig. 1A). The first mesh was based on a T1w MRI scan; the second mesh was based on a T1w þ T2w MRI scan. With SimNIBS (v3.2.3) [7], we simulated two TMS targets in each participant (one motor target, one prefrontal target), for a total of 400 E-field simulations (100 participants * 2 meshes * 2 TMS targets). All simulations were performed with a MagVenture 70mm figure-of-eight coil at 50% stimulator output on a MagPro R30 machine (dI/dt ¼ 75e6 A/s). For motor stimulation, the coil center was placed over C3 according to the electroencephalography 10e20 system, with a 45 angle to the sagittal plane. For prefrontal stimulation , the coil center was placed over F3 with a 45 angle. Standard conductivity values were used for the modeled tissues (white matter: 0.126 S/m, grey matter: 0.275 S/m, CSF: 1.654 S/m, bone: 0.01 S/m, skin: 0.465 S/m, and eyes: 0.5 S/m). For both meshes, the average E-field induced in the primary motor cortex (C3 TMS) and dorsolateral prefrontal cortex (F3 TMS) was extracted using a ROI analysis [2,7]. We centered the ROI at the subject space transformed peak MNI coordinate of the primary motor cortex (x ¼ À37, y ¼ À21, z ¼ 58) or dorsolateral prefrontal cortex (x ¼ À30, y ¼ À43, z ¼ 23) and extracted the average E-field in a 10 mm radius grey matter sphere in each model [8,9]. Linear mixed models were constructed with E-FIELD STRENGTH as the dependent variable, and MESHING APPROACH and ROI and their interaction as fixed effects. PARTICIPANT was included as random intercept. Results of the mixed model were investigated via Bonferroni-corrected post-hoc tests. The significance level was set to a ¼ 0.05. Previously, we used dice calculations to demonstrate that T1w þ T2w MRI scans produce more accurate head meshes primarily by improving skull and CSF tissue segmentation accuracy [2]. However, dice measures only provide information on whole head Abbreviations: tES, transcranial electric stimulation; TMS, transcra-nial magnetic stimulation; E-field, electric field; MRI, magnetic resonance imaging; ROI, region of interest; SCD, scalp-to-cortex distance; FEM, finite element method.
Tijdschrift: Brain stimulation (Print)
ISSN: 1935-861X
Issue: 4
Volume: 15
Pagina's: 942 - 945
Jaar van publicatie:2022
Trefwoorden:Electric field (E-field) modeling, Transcranial magnetic stimulation (TMS), Finite element method (FEM), T1w structural MRI scan, T2w structural MRI scan, Computational modeling