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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Clin Neurophysiol. 2021 Jul 10;132(9):2199–2207. doi: 10.1016/j.clinph.2021.06.015

A reexamination of motor and prefrontal TMS in tobacco use disorder: time for personalized dosing based on electric field modeling?

Kevin A Caulfield 1, Xingbao Li 1, Mark S George 1,2
PMCID: PMC8384673  NIHMSID: NIHMS1723335  PMID: 34298414

Abstract

Objective:

In this study, we reexamined the use of 120% resting motor threshold (rMT) dosing for transcranial magnetic stimulation (TMS) over the left dorsolateral prefrontal cortex (DLPFC) using electric field modeling.

Methods:

We computed electric field models in 38 tobacco use disorder (TUD) participants to compare figure-8 coil induced electric fields at 100% rMT over the primary motor cortex (M1), and 100% and 120% rMT over the DLPFC. We then calculated the percentage of rMT needed for motor-equivalent induced electric fields at the DLPFC and modeled this intensity for each person.

Results:

Electric fields from 100% rMT stimulation over M1 were significantly larger than what was modeled in the DLPFC using 100% rMT (p < 0.001) and 120% rMT stimulation (p = 0.013). On average, TMS would need to be delivered at 133.5% rMT (range = 79.9 to 247.5%) to produce motor-equivalent induced electric fields at the DLPFC of 158.2V/m.

Conclusions:

TMS would have to be applied at an average of 133.5% rMT over the left DLPFC to produce equivalent electric fields to 100% rMT stimulation over M1 in these 38 TUD patients. The high interindividual variability between motor and prefrontal electric fields for each participant supports using personalized electric field modeling for TMS dosing to ensure that each participant is not under- or over-stimulated.

Significance:

These electric field modeling in TUD data suggest that 120% rMT stimulation over the DLPFC delivers sub-motor equivalent electric fields in many individuals (73.7%). With further validation, electric field modeling may be an impactful method of individually dosing TMS.

Keywords: Transcranial magnetic stimulation (TMS), electric field modeling, finite element method, motor threshold, personalized dosing, tobacco use disorder

1. Introduction

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation approach that is a United States FDA-approved treatment for depression (George et al., 2010; Levkovitz et al., 2015; O’Reardon et al., 2007), obsessive compulsive disorder (OCD)(Carmi et al., 2019), tobacco use disorder (TUD), and migraine headache with aura (Starling et al., 2018). In most clinical and experimental applications, TMS is first applied over the hand representation in the left primary motor cortex (M1) and titrated to elicit an observable finger twitch in the contralateral right hand (Badran et al., 2020; Hallett, 2007; Wassermann et al., 1996). This resting motor threshold (rMT) procedure is a dosing paradigm to ensure that stimulation is sufficiently powered to reach the cortex and cause neural activation (McConnell et al., 2001). However, it was initially unclear how to best apply the rMT as a dosing procedure for stimulation targets outside of the motor system where an immediate behavioral response cannot be observed, such as the dorsolateral prefrontal cortex (DLPFC) in depression.

Researchers first applied clinical TMS to the DLPFC at sub-threshold stimulation intensities of 80% rMT due to safety considerations (George et al., 1995b). Despite subsequent increases to 100% rMT stimulation, multiple groups observed that older depression patients had more modest mood improvements from TMS compared to younger patients (Figiel et al., 1998; Kozel et al., 2000). Contemporaneous neuroimaging research suggested that increased structural atrophy (Gur et al., 1991), greater sulcal flattening and depth (Kochunov et al., 2005), and decreased metabolism (Yoshii et al., 1988) in normal aging without depression could decrease the degree of mood improvements from TMS. These findings, in addition to a growing understanding of the structural (Bartzokis et al., 2000; Mechtcheriakov et al., 2007) and functional brain changes (Benowitz, 2008; George et al., 1995a; Ko et al., 2013; Mayberg et al., 1999; Phan et al., 2002; Rose, 2007) in depression and addiction, supported the notion of dose-adjusting TMS intensity based on scalp-to-cortex distance in older depression populations and perhaps stimulating above 100% rMT.

In one study, Nahas and colleagues (2004) used anatomical MRI scans of depression patients aged 55–75 years old, distance measurements between the TMS coil and cortex, and physical properties of electromagnetic signal decay to calculate the stimulation intensity over the DLPFC for motor-equivalent TMS (Nahas et al., 2004). They determined that an average of 114% rMT was required for an equivalent stimulation intensity over the DLPFC as 100% rMT stimulation over M1 (range = 103–141% rMT), and found that 28% of these 55–75 year old patients met response criteria and 22% were remitters. In contrast, patients above the age of 50 had previously responded poorly with 100% rMT stimulation (Kozel et al., 2000). As a simple proxy, clinicians and researchers using figure-8 TMS coils began using the 120% rMT stimulation intensity. A similar method is to use anatomical MRI scans to account for individual scalp-to-cortex distances in estimating the dose to reach the prefrontal cortex, which Stokes and colleagues (2005) have developed and implemented(Stokes et al., 2005). The adoption of this 120% rMT dosing parameter is now widespread and was used in the pivotal TMS trials that resulted in FDA-approval (George et al., 2010; O’Reardon et al., 2007). Nevertheless, the use of a certain percentage of rMT as a dosing mechanism for non-motor brain areas has not been thoroughly reexamined since the mid-2000s.

In this study, we performed electric field modeling on 38 T1w anatomical MRI scans previously acquired as a part of a recently published positive clinical trial using TMS over the DLPFC for tobacco use disorder (TUD) adult participants (Li et al., 2020). We first evaluated how TMS-induced electric fields from 100% rMT stimulation over M1 compare to the rMT values, and to 100% rMT stimulation over the DLPFC. Next, we tested whether TMS applied at 100% rMT or 120% rMT over the DLPFC would produce similar electric fields to 100% rMT stimulation over M1. We then calculated the percentage of rMT that would produce an M1-equivalent electric field for the DLPFC, and applied this stimulation intensity for each participant. Finally, we assessed the dose-response relationship between electric field strength and change in number of cigarettes smoked from 2 weeks of DLPFC stimulation.

2. Methods

2.1. Study overview

In this Medical University of South Carolina IRB-approved, randomized, double-blind, sham-controlled trial, we enrolled 42 treatment-seeking TUD participants. Of these 42 participants, 38 (21 women, mean age = 42.5, SD = 10.3, range = 21–60) underwent multimodal MRI scanning and completed the full treatment course and were included in this study. The multimodal imaging included a high resolution T1w MPRAGE anatomical MRI scan for each participant (Siemens 3T TIM Trio system with a 12-element receive-only head coil; TR = 2250ms, TE = 4.18ms, voxel size: 1.0 × 1.0 × 1.0mm, 177 slices, full-brain with no gap)(Figure 1A). Each participant’s T1w anatomical MRI scan was uploaded to Brainsight neuronavigation (Rogue Research, Inc., Montreal, Quebec, Canada), and used for individual anatomically-based DLPFC stimulation target determination by a physician (Mylius et al., 2013)(Figure 1B). The individualized MNI coordinates for each participant were noted and used for electric field modeling.

Figure 1: Experimental and Electric Field Modeling Overview.

Figure 1:

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1A: Experimental Timeline. Each participant underwent a high-resolution T1w anatomical magnetic resonance imaging (MRI) scan prior to the first TMS visit. Following this, each participant’s resting motor threshold (rMT) was acquired, and 10 active (N = 21) or sham (N = 17) transcranial magnetic stimulation (TMS) treatments were administered. 1B: Coil Positioning. The TMS coil was individually placed based on anatomical landmarks from the MRI scan and uploaded to the neuronavigation software. Here we show the coil position on the scalp surface of the MNI-152 template brain, angled down from the sagittal plane at 45°. 1C: Electric Field Modeling Overview. The electric field modeling pipeline takes five steps, as visualized on a representative participant. First, each participant underwent a high resolution T1w anatomical MRI scan. We then segmented each participant’s brain scan into skin, bone, cerebrospinal fluid, gray matter, and white matter (top to bottom). Next, volumetric meshing combined the tissue layers into a 3D model with different, experimentally determined tissue values. Following this, we computed four electric field models: 1. Primary motor cortex (M1) TMS at 100% resting motor threshold (rMT). 2. Dorsolateral prefrontal cortex (DLPFC) TMS at 100% rMT. 3. DLPFC TMS at 120% rMT. 4. DLPFC TMS at the calculated group percentage required for M1-equivalent stimulation (See Equation 1). Finally, we computed a region of interest (ROI) analysis for each simulation, at the cortical projection underneath the center of the TMS coil (indicated with gray spheres), with 10mm spherical radius ROIs and gray matter masks. This process was repeated for each participant.

Using the Neuronetics Neurostar system (Neuronetics Inc., Malvern, PA, USA), TMS was first applied over the left primary motor cortex (M1) at the representation of the contralateral right abductor pollicis brevis (APB) muscle to acquire a rMT for each participant using a standard, visual observation method (Badran et al., 2020). Next, repetitive TMS (rTMS) was applied over the left DLPFC at 100% rMT for 10 daily TMS treatments over two weeks, with stimulation delivered at 10Hz in 5 second trains and an intertrain interval of 10 seconds, for 60 trains and 3000 pulses per session. All TMS, over both the motor cortex and DLPFC, was delivered with the coil angled 45° to the sagittal plane. The behavioral outcome data were recently reported in (Li et al., 2020).

2.2. Electric Field Modeling Overview

All modeling was done blind to clinical outcomes. As our objective of evaluating rMT as a dosing paradigm was not based on clinical outcome, we performed electric field modeling for all 38 participants irrespective of active or sham condition. After completing all modeling, we unblinded the electric field modeler/data analyzer and separated participants by condition in order to evaluate the dose-response relationship of electric field strength on clinical outcome in the 21 active rTMS participants.

For tissue segmentation and volumetric meshing, we used headreco (https://simnibs.github.io/simnibs/build/html/documentation/command_line/headreco.html) combined with CAT12 (http://www.neuro.uni-jena.de/cat/ )(Nielsen et al., 2018)(Figure 1C). Headreco segments each participant’s T1w anatomical MRI scan into skin, bone, cerebrospinal fluid, white matter, and gray matter(Nielsen et al., 2018)(Figure 1C). After tissue segmentation, headreco creates a volumetric mesh that combines the tissue layers into a 3D model (Figure 1C). We used the default vertex density setting in headreco of 0.5 nodes per mm2. The head meshes contained an average of 736,705 nodes (SD = 73,622, range = 598,140–901,679), 938,647 triangles (SD = 90,176, range = 831,852–1,159,962), and 4,066,314 tetrahedra (SD = 414,636, range = 3,305,210–5,012,507). The same two participants had the lowest and highest number of nodes, triangles, and tetrahedra. All segmentation and volumetric meshing were assessed to be accurate, using previously published quality control methods including careful visual inspection and Z-score determination to confirm that there were no tissue volume outliers(Caulfield et al., 2020a; Caulfield et al., 2020b; Caulfield et al., 2020c).

Following segmentation and meshing, we used SimNIBS 3.1.1 (https://simnibs.github.io/simnibs/build/html/index.html)(Saturnino et al., 2019) to simulate the TMS-induced electric fields in the cortex. SimNIBS uses the finite element method (FEM) to determine electric field values using default tissue conductivity values: Gray matter: 0.275S/m, white matter: 0.126S/m, cerebrospinal fluid (CSF): 1.654S/m, bone: 0.01S/m, and skin: 0.465S/m. We simulated two electric field models for each participant with a Neuronetics Neurostar coil(Deng et al., 2013; Li et al., 2020)(Figure 1C). We first modeled stimulation with the center of the TMS coil placed over C3 as a surrogate location for the motor hotspot and at 100% rMT. While we did not save the actual motor hotspot location for each participant in the parent trial, we visually confirmed that the C3 location was in the prefrontal gyrus for each participant. Nevertheless, it is a limitation that this location may have slightly differed from the motor hotspot location that we further discuss in the Discussion Section. We then extracted each person’s average induced electric field using a 10mm spherical region of interest (ROI) analysis with a gray matter mask at the individualized cortical projection directly underneath the center of the TMS coil. By using a gray matter mask, only the electric fields in gray matter tissue were recorded; the electric fields in non-gray matter components such as white matter, CSF, bone, and skin did not contribute to our calculation of the average electric field in the ROI.

We then computed an electric field model for stimulation applied over the DLPFC (Figure 1C). Here we used each individual’s DLPFC target MNI coordinates to place the modeled Neuronetics TMS coil over the left DLPFC, and cortical projections to place a 10mm spherical ROI centered on the gray matter voxel directly underneath the center of the coil. We individually placed these ROIs based on the individually determined anatomical MRI-guided targets, but report the group average cortical MNI coordinate here for reference: −42.1 37.5 31.3. In the model, stimulation was applied at 100% rMT. We used the results of the electric field model and multiplied by 1.2 to simulate 120% motor threshold stimulation. Following this, we calculated the group average percentage of rMT that would produce the same electric field from DLPFC stimulation as 100% rMT stimulation over M1 by creating a proportion using

Proporation=M1ElectricFieldfrom100%rMTDLPFCElectricFieldfrom100%rMT. Equation 1:

We calculated this proportion for each person and then computed the group average. Following this, we multiplied each individual’s motor threshold by this value. In every model in both the motor and prefrontal cortex, the TMS coil was oriented at 45° from the sagittal plane (Figure 1B). In Supplementary Section 1, we show the electric field distribution in histograms for 10 randomly selected participants.

2.3. Statistical Analysis

All statistical analyses were conducted in SPSS 25.0 (IBM Corp., Armonk, NY, USA) with two-tailed significance threshold set at p < 0.05.

3. Results

3.1. Correlation Between Induced Electric Fields from 100% rMT Over M1 and rMT Machine Output Values

We first correlated the electric fields from 100% rMT over M1 and the rMT machine output values in all 38 TUD participants using a Pearson’s correlation. These values significantly correlated, R = 0.90, p < 0.001 (Figure 2). We further investigated the relationship between electric field strength from a uniform stimulation intensity and the rMT machine output values in Supplementary Section 2.

Figure 2: Correlation Between Electric Fields from 100% Resting Motor Threshold (rMT) Over the Primary Motor Cortex (M1) and Transcranial Magnetic Stimulation (TMS) Machine Output.

Figure 2:

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M1 electric fields significantly correlated with TMS machine output (p < 0.001). This significant correlation validates E-fields as a method of measuring stimulation intensity.

3.2. Correlation Between Induced Electric Fields from 100% rMT Over M1 and 100% rMT Over DLPFC

We next calculated the relationship between the induced electric fields at M1 and DLPFC with 100% rMT intensities using a Pearson’s correlation. These values significantly correlated in these 38 TUD participants, R = 0.535, p < 0.001, although the DLPFC electric fields were lower than those at M1 (Figure 3).

Figure 3: Correlation Between Electric Fields at the Dorsolateral Prefrontal Cortex (DLPFC) and Primary Motor Cortex (M1) with 100% resting motor threshold (rMT) Stimulation.

Figure 3:

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We found that the transcranial magnetic stimulation (TMS)-induced electric fields at M1 and the DLPFC from 100% rMT stimulation significantly correlate (p < 0.001).

3.3. Induced Electric Fields from 100% rMT Over M1 vs. 100% rMT and 120% rMT Over the DLPFC

We used a paired t-test to evaluate the difference in electric fields induced by 100% rMT applied over M1 and the DLPFC. The induced electric fields at M1 were significantly greater than those at the DLPFC, t(37) = 6.88, p < 0.001 (Figures 4 and 5). The mean electric field at M1 was 158.2V/m (SD = 40.4V/m, range = 76.7–263.2V/m) while it was 118.5V/m at the DLPFC (SD = 31.8V/m, range = 55.5–212.4V/m). This result was fairly consistent across participants, with 32 out of 38 participants (84.2%) having a higher electric field at M1 than the DLPFC (Figure 4). Next, we multiplied each DLPFC electric field by 1.2x to determine the electric fields induced by 120% rMT DLPFC stimulation. Here, the M1 electric fields at 100% rMT were still significantly greater than those at the DLPFC at 120% rMT, t(37) = 2.61, p = 0.013 (Figures 4 and 5). The mean electric field at M1 of 158.2V/m (SD = 40.4V/m, range = 76.7–263.2V/m) was higher than the mean electric field at the DLPFC from 120% rMT stimulation of 142.2V/m (SD = 38.1V/m, range = 66.6–254.9V/m). The induced electric fields were still higher at 100% rMT over M1 than those from 120% rMT over the DLPFC in 28 out of 38 participants (73.7%)(Figure 4).

Figure 4: Quantitative Comparison of Electric Fields Between the Motor and Prefrontal Cortices.

Figure 4:

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Using paired t-tests, we found that the induced electric fields produced from 100% resting motor threshold (rMT) stimulation over the primary motor cortex (M1) was significantly greater than 100% rMT stimulation over the dorsolateral prefrontal cortex (DLPFC; ***p < 0.001). In addition, the induced electric fields produced from 100% rMT stimulation over M1 were still significantly greater than 120% rMT stimulation over the DLPFC (*p = 0.013). This suggests that the common use of 120% rMT TMS for DLPFC stimulation may not be sufficient to compensate for increased scalp-to-cortex distance and differing tissue conductivities between M1 and the DLPFC in many participants. When we forced the group average electric field to be the same, it took an average of 133.5% rMT stimulation (range = 79.9–247.5% rMT). over the DLPFC for equivalent electric fields to 100% rMT over M1.

Figure 5: Visual Comparison of Electric Fields Between the Motor and Prefrontal Cortices.

Figure 5:

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Here we averaged the electric fields from all 38 participants and visualized the results in fsaverage Space. Qualitatively, the induced electric fields from 100% resting motor threshold (rMT) stimulation over the primary motor cortex (M1) were similar to the induced electric fields from 133.5% rMT stimulation over the dorsolateral prefrontal cortex (DLPFC). In contrast, 120% rMT and 100% rMT stimulation over the DLPFC produced visually weaker electric fields.

3.4. Percentage of rMT for Induced Electric Field Equivalency Between M1 and DLPFC

Using the electric field data from 100% rMT stimulation, we divided the M1 electric field by the DLPFC electric field for each participant. This created a proportion for each person (Equation 1), indicating the percentage that the electric field induced at M1 was higher or lower than the electric field induced at the DLPFC.

In these 38 TUD participants, the average proportion was 1.335 (SD = 0.359, range = 0.799–2.475). That is, for an equivalent group-level induced electric field at the DLPFC, stimulation would have to be delivered, on average, at 133.5% rMT, with a range of 79.9 to 247.5% rMT (Figure 6). The participant who would receive 247.5% rMT stimulation was a slight outlier at 3.06 SD above the mean; without the inclusion of this participant, the range of motor equivalent stimulation over the DLPFC was 79.9 to 211.0% rMT, with an average of 131.6% rMT. However, as the range and average rMT required for M1-equivalent stimulation did not substantially change, and as we modeled in a small sample size that may not fully account for the full range of electric fields, we chose to include this participant in the electric field modeling analyses.

Figure 6: Distribution of Percentage of Resting Motor Threshold (rMT) Needed to Produce Equivalent Dorsolateral Prefrontal Cortex (DLPFC) Electric Fields Compared to Motor Electric Fields.

Figure 6:

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It would take an average of 133.5% rMT stimulation over the DLPFC to produce equivalent electric fields to 100% rMT stimulation over the motor cortex (range = 79.9–247.5%).

We then computed electric field models using 133.5% rMT stimulation over the DLPFC for each person. The induced electric fields at 100% rMT over M1 were higher than those from 133.5% rMT over the DLPFC in 20 out of 38 participants (52.6%)(Figures 4 and 5). By mathematically forcing the group average electric field to be equivalent, there were no significant differences between the induced electric fields between the 100% rMT stimulation over M1 and 133.5% rMT stimulation over the DLPFC, t(37) = 0, p = 1.00. The induced electric fields were 158.2V/m in both conditions (M1 SD = 40.4V/m, M1 range = 76.7–263.2V/m; DLPFC SD = 42.4V/m, DLPFC range = 74.1–283.7V/m)(Figures 4 and 5).

3.5. The Electric Field Strength-Response Relationship from 100% rMT TMS Over the DLPFC

Lastly, we used a Pearson’s correlation to test the relationship between electric field strength and TMS therapeutic response of the change in number of cigarettes smoked from pre- to post-10 sessions of 10Hz rTMS over two weeks in the 21 active stimulation participants. We did not find a significant relationship between these variables, R = −0.09, p = 0.70 (Figure 7).

Figure 7: Correlation Between Electric Field from 100% Resting Motor Threshold (rMT) Stimulation Over the Dorsolateral Prefrontal Cortex (DLPFC) and Percentage Change in Number of Cigarettes Smoked Per Day.

Figure 7:

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We found that the induced electric field over the DLPFC at the stimulation intensity used in the Tobacco Use Disorder clinical trial (100% rMT) did not significantly impact the change in number of cigarettes smoked per day (p = 0.70).

4. Discussion

In this study, we examined how TMS-induced electric fields compare between left M1 and the left DLPFC in 38 TUD patients, with the goal of reevaluating whether the commonly used 120% rMT dosing parameter sufficiently accounts for the increased scalp-to-cortex distance and differing tissue compositions between the prefrontal cortex in comparison to the motor cortex. We found that the induced electric fields over M1 at 100% rMT were significantly greater than those at 100% rMT and 120% rMT over the DLPFC at a group level (Figures 4 and 5). On a group level, we found that an average of 133.5% rMT would be required to produce an equivalent DLPFC electric field to the M1 electric field. Notably, the spread in percentage of rMT values was a wide range of 79.9% to 247.5%. This indicates that not only were some participants likely understimulated but also some may have been overstimulated. This wide interindividual variance supports the use of individualized electric field modeling as a dosing paradigm, as a few participants had higher electric fields from 100% rMT DLPFC stimulation than 100% rMT M1 stimulation. Stimulating at 120% rMT over the DLPFC appears to incompletely compensate for increased distance and differing tissue composition than M1 as 28 out of 38 participants (73.7%) still had higher electric fields from 100% rMT stimulation over M1. Therefore, these data suggest that using 120% rMT would deliver subthreshold stimulation to 73.7% of TUD patients, although it is unclear if these findings would translate in the treatment resistant depression populations that were used to initially determine the 120% rMT threshold. Although induced electric fields from 100% rMT stimulation at the DLPFC tended to be lower than those from 100% rMT stimulation over M1, these values significantly correlated, supporting the use of percentage of the motor threshold to dose TMS in non-motor brain regions (Figure 3).

While these data support the use of dosing by individualized electric field modeling, we also calculated the average percentage of motor threshold needed for equivalent electric fields as 100% rMT stimulation over M1. Using Equation 1, we determined that it would take an average of 133.5% rMT (range = 79.9–247.5% rMT) over the DLPFC to produce the same group average electric field of 158.2V/m as 100% rMT stimulation over M1 (Figures 4 and 5). By stimulating at this 133.5% rMT intensity, only 20 out of 38 participants (52.6%) would have higher electric fields from 100% rMT TMS over M1 (Figure 4). Notably, 133.5% rMT over the DLPFC for motor equivalent electric fields is higher than what was found by (Nahas et al., 2004), who used scalp-to-cortex distance in chronic recurrent depressed patients without consideration of tissue composition and coil angle to estimate that an average of 114% rMT (range = 103–141% rMT) would be required to produce an equivalent stimulation intensity over the DLPFC as 100% rMT over M1. Our percentage of rMT range is also larger (79.9–247.5% compared to Nahas et al.’s 103–141% rMT), and is perhaps due to the increased number of mathematical combinations when accounting for differing tissue compositions using electric field modeling as opposed to measuring scalp-to-cortex distance alone; more information accounted for in electric field modeling would be expected to increase the range of possible values. This large range in percentage of rMT values for DLPFC stimulation additionally underscores the possible utility in using electric field modeling to personally dose TMS. However, it remains unclear whether stimulating at 133.5% rMT would actually impact treatment response in comparison to 114% or 120% rMT stimulation. It is also important to note that stimulating at a higher intensity would potentially impact the stimulation parameters due to safety considerations such as the intertrain interval, stimulation train duration, and total number of pulses(Rossi et al., 2009).

Furthermore, when we compared the electric field strength in the 21 active rTMS TUD patients with their treatment outcomes, we did not find a dose-response relationship between the electric field strength from 100% rMT stimulation over the DLPFC and percentage change in number of cigarettes smoked per day (Figure 7). In one sense, this finding is not surprising. First, researchers have previously reported that there is no electric field-response finding from 120% rMT TMS over the DLPFC in depression(Deng et al., 2019). Second, electric field modeling is a valuable tool, but it only captures the induced electric field that would be produced by a single TMS pulse; other critical stimulation parameters such as the pulse pattern (Blumberger et al., 2018; Moisset et al., 2015), number of pulses (Bohning et al., 2003; Schulze et al., 2018), and number of treatment sessions (Loo and Mitchell, 2005; Schulze et al., 2018) are not taken into account. Third, prior results have suggested that certain repetitive pulse patterns, delivered at subthreshold stimulation intensities, can affect behavior and functional neurobiology. For instance, theta burst stimulation applied at subthreshold intensities of 80% rMT can produce long term potentiation (LTP)-like effects (Chung et al., 2018; Huang et al., 2005). Further still, subthreshold rTMS or theta burst stimulation intensities can produce measurable functional brain changes from pre- to post-stimulation at subthreshold stimulation intensities, as measured with electroencephalography (Chung et al., 2018; Zmeykina et al., 2020) and fMRI (Cárdenas-Morales et al., 2011; Hubl et al., 2008; Zhang et al., 2019). Therefore, in patterned bursts of stimulation or with high frequency repetitive TMS, it may be the case that subthreshold stimulation can still cause LTP-like or therapeutic effects. The impact of electric field strength, or stimulation intensity as a percentage of rMT, may also be less important over the course of multiple stimulation sessions, such as the 10 sessions of 10Hz rTMS that was used in the parent study (Li et al., 2020), or in accelerated, high-dose protocols(Cole et al., 2020; Fitzgerald et al., 2020; George et al., 2020; Konstantinou et al., 2020; Williams et al., 2018) where the spread of treatments over time may also play an outsized role in therapeutic durability (Caulfield, 2020). Nevertheless, this relationship between the number of treatment sessions and impact of electric field strength on treatment efficacy has never been systematically explored and should be further investigated.

There are several future directions and limitations in this study. It is unclear whether the 133.5% rMT stimulation intensity over the DLPFC would be the same intensity required to produce equivalent induced electric fields to 100% rMT TMS over M1 in a different population outside of TUD patients. In addition, it is unclear how these findings would translate across other age groups that may have different tissue compositions and skull-to-cortex distances due to normal aging. The mean age of 42.5 in this study is younger in comparison to mean age of 61.2 of the depression patients treated by (Nahas et al., 2004) and used to establish the 120% rMT stimulation paradigm over the DLPFC (See Supplementary Section 3 for a correlation between age and DLPFC electric field amplitude). Thus, on one hand, the older patients included in Nahas and colleagues’ study may have been expected to have higher stimulation intensities than in the cohort included in this study. However, there might be reason to believe that there could be structural and functional brain changes specific to addiction, particularly gray matter thinning and atrophy in multiple brain regions including the prefrontal cortex from nicotine (Gallinat et al., 2006; Goriounova and Mansvelder, 2012; Karama et al., 2015; Liao et al., 2012). Therefore, it is possible that our finding that 133.5% rMT stimulation over the DLPFC would be necessary for equivalent induced electric fields to 100% rMT over M1 is particularly pronounced in individuals with TUD, and this relationship should be assessed in other clinical populations and in healthy adults of different ages. Additionally, it is unclear how these findings would generalize in other TMS stimulators and coils, particularly in H-coils which achieve deeper penetration depth that may be able to better compensate for greater scalp-to-cortex distance in the prefrontal cortex(Lu and Ueno, 2017). In the recent FDA approval TUD study, patients were stimulated with an H-coil, which stimulates deeper than figure-8 coils(Deng et al., 2013; Tendler et al., 2017). It is unclear if this deeper penetration is needed or could have overcome the observation of 133.5% rMT needed over DLPFC for M1-equivalent electric fields noted in this manuscript. Lastly, it may also be useful to further develop electric field modeling as a prospective method of dosing TMS, particularly at stimulation targets outside of the motor cortex or in populations with structural brain abnormalities or damage who may be underdosed or overdosed by applying a certain percentage of rMT. While this approach would require the use of anatomical MRI scans for each participant, it may be a useful method for dosing to produce a more similar electric field across participants and between M1 and DLPFC stimulation targets.

Limitations and Future Directions

There were several limitations in this study. This was an electric field modeling reexamination of existing data in 38 TUD patients. Further research in larger samples would be required to make more broadly applicable conclusions, particularly as the most common clinical use of TMS is in depression and not TUD. We did not save the precise motor hotspot location for each participant, instead using the EEG coordinate of C3 as a surrogate motor location. While it is common to use EEG coordinates to estimate a cortical location (e.g. F3 for DLPFC stimulation)(Beam et al., 2009), this approach might have caused slight discrepancies between the true and surrogate motor hotspot locations. More sophisticated future electric field modeling experiments might take gyral/sulcal orientation into account as a further cause of electric field variability between participants. Lastly, our electric field estimates are relatively high. There are a few possible explanations. First, these participants may have had above average motor threshold values that resulted in higher electric fields. In addition, the TMS device manufacturer has not published their dI/dt value needed for modeling, so perhaps our best estimate of a maximum stimulator output (MSO) of 131.5A/μs overestimates the actual MSO and therefore upshifted the electric field estimates. Our method of ROI analysis using a 10mm spherical gray matter mask centered underneath the center of the coil may have extracted a highly focal region with larger electric fields than what other researchers have used (e.g. 95th percentile of electric fields). Thus, using our ROI analysis approach may also have resulted in relatively high electric fields compared to a percentile approach. Nevertheless, these factors would have equally impacted each electric field estimate and therefore do not impact our conclusions. We are comparing the ROI and percentile approaches in ongoing research. In addition, researchers are refining the ROI approach in electric field modeling by using prospective MEP measurements, which could provide increasingly accurate values in the future(Weise et al., 2020). Future directions also include more rigorous comparisons of electric field modeling to functional neuroimaging and other prospective TMS measurements, which are required to further validate and determine the utility of electric field dosimetry.

4.1. Conclusions

In summary, this TMS electric field modeling study suggests that dosing TMS in figure-8 coils for the DLPFC at 120% rMT may stimulate at subthreshold intensities for 73.7% of TUD individuals. These preliminary data in 38 TUD participants support using electric field modeling as a method of personalizing TMS dose. At a group average level, our models suggest that an average of 133.5% rMT stimulation over the DLPFC would be required to the motor equivalent electric field of 158.2V/m from 100% rMT stimulation, with substantial between-subject differences (range = 79.9 to 247.5% rMT). However, further experiments are required to assess whether personally dosing TMS using electric field modeling or using 133.5% rMT would result in greater therapeutic effects, as we did not find a dose-response relationship of electric field strength from 100% rMT stimulation over the DLPFC for TUD in this study. Future TMS clinical trials that use prefrontal targets and figure-8 coils should consider reevaluating the 120% rMT protocol and adjust as needed for different diagnoses that may have prefrontal atrophy, older populations, or both.

Supplementary Material

1

Highlights.

  • In 38 tobacco use disorder participants, we used electric field modeling to reexamine TMS % resting motor threshold (rMT) dosing.

  • Prefrontal TMS at 133.5% rMT would be required to produce equivalent electric fields as motor TMS at 100% rMT.

  • The wide between-subjects range of 79.9–247.5% rMT for motor equivalent prefrontal electric fields supports using personalized electric field dosing.

Acknowledgments

We would like to thank Dr. Zhi-De Deng for his guidance on electric field modeling using the Neuronetics Neurostar system. Neuronetics Inc. donated SenStars (inserts needed between the TMS coil and the subject’s head for the device to work) for the 10 TMS treatments per participant. However, Neuronetics, Inc. had no role in the study design, electric field modeling, study implementation, data analysis, or interpretation of results.

Financial Support

This work was supported by an U.S. NIH/NIDA grant (number: 1R21DA036752-01A1) to Dr. Xingbao Li.

Footnotes

Conflict of Interest Statement

We confirm that there are no known conflicts of interest associated with this publication and there was no financial support for this work that could have influenced its outcome. Dr. George was the co-PI (uncompensated) on the recent Brainsway TUD multisite trial that led to FDA approval.

Data Statement

The data used for electric field modeling are available upon reasonable request.

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