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      Can transcranial electrical stimulation motor threshold estimate individualized tDCS doses over the prefrontal cortex? Evidence from reverse-calculation electric field modeling

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          Abstract

          To the editor We recently conducted a study examining whether transcranial electrical stimulation (TES) motor threshold (MT), reverse-calculation transcranial direct current stimulation (tDCS) electric field modeling, or both could potentially be used as methods of individualizing tDCS doses(1). We found that TES MT significantly correlates with a reverse-calculated tDCS dosage in the motor cortex and were intrigued by the possibility of using TES MT as an MRI-free method of individually dosing tDCS(1). A limitation of this previous work was that we did not test the utility of TES MT to estimate reverse-calculation tDCS doses outside of the motor cortex. Here we extend this research by assessing whether TES MT correlates with reverse-calculation electric field models of prefrontal stimulation in a common F3-F4 electrode montage that has been used in depression [2], drug craving [3], working memory [4], and many other conditions. In this study we used the same dataset as in Ref. [1], in which we acquired transcranial magnetic stimulation (TMS) MT, TES MT, and anatomical T1w MRI scans for 29 healthy adults (15 women, mean age = 26.9, SD = 9.1). We previously described the two-visit study protocol in depth in Ref. [1] but briefly describe it here. In Visit 1, we placed a plastic cap on each participant’s head and used a closed-loop TMS-motor evoked potential (MEP) acquisition setup using single pulses of TMS (Magstim BiStim machine with 70mm figure-of-eight Remote Coil; Whitland, Wales, UK) over the left motor hotspot and electromyography (EMG) electrodes over the contralateral right hand [5]. We defined a positive MEP as having a peak-to-peak amplitude of ≥0.05mV, and used PEST software (https://www.clinicalresearcher.org/software.htm) to determine the next stimulation intensity for MT acquisition [6]. After determining the TMS MT, we cut through the plastic cap to place a 35 × 20mm electrode (Natus Neurology Inc., Pleasanton, CA, USA) on the head at the left motor hotspot and placed a 55 × 42mm electrode (Natus Neurology Inc., Pleasanton, CA, USA) on the left deltoid. We used a Digitimer DS7A (Letchworth Garden City, England, UK) to send single pulses of electrical stimulation through the electrodes, with a pulse width of 200 ms, maximum voltage of 400V, and initial stimulation intensity of 58.0mA. Using this left M1-left deltoid electrode configuration and these stimulation parameters, TES was safe, tolerable, and relatively pain-free for each participant (****See Supplemental Materials S1 in Ref. [1] for tolerability and pain ratings). In addition, a modified PEST algorithm allowed our determination of a TES MT for each participant with just 5 TES pulses [1]. In Visit 2, we acquired anatomical T1w MRI scans for each participant to be used for electric field modeling. To segment each person’s MRI scan we used headreco (https://simnibs.github.io/simnibs/build/html/documentation/command_line/headreco.html), a command that calls SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) and CAT12 (http://www.neuro.uni-jena.de/cat/) and converts NIFTI to MSH files [7]. Using previously published methods, we used visual inspection and a Z-score analysis to evaluate the quality of tissue segmentation of grey matter, white matter, and cerebrospinal fluid (CSF) [1]. We did not identify any improper segmentations in these data. To perform electric field modeling, we used SimNIBS 3.1.1 (https://simnibs.github.io/simnibs/build/html/index.html) [8] as it can be used to perform region of interest (ROI) analyses and has been validated against ROAST [9]. We placed rectangular 70 × 50mm electrodes over each participant’s F3 and F4, with the longer axis running left/right on the head (Fig. 1A) and 2.0mA of current input into F3 (anode) and −2.0mA for F4 (cathode). We extracted 10mm radius spherical ROIs at MNI coordinates for the cortical projections underneath the electrodes at F3 and F4. This method has previously been used to determine the MNI coordinates of ROIs at the cortical level that underlie TMS coils placed on the scalp(10)(Fig. 1A). We further measured an ROI at the cortical projection midway between the two electrodes underneath Fz [10]. Under each ROI, an average electric field was computed using a grey matter mask. We then reverse-calculated the tDCS dose at the scalp that would be required to produce the group average electric field for each person using the cross-multiplication method detailed in Fig. 1B and regressed the dose against the TES MT for each person in SPSS 25.0 (Armonk, NY, USA: IBM Corp.). The group average reverse-calculation doses were 2.045mA for the ROI underneath F3 (range = 1.444–2.515mA, SD = 0.320mA), 2.036mA for the ROI underneath F4 (range = 1.426–2.764mA, SD = 0.280mA), and 2.053mA for the ROI underneath Fz (range = 1.545–2.445mA, SD = 0.351mA). TES MT significantly correlated with the reverse-calculation dose based on the ROIs underneath F3, F(1, 27) = 12.03, R2 = 0.31, p = 0.002 and F4, F(1,27) = 6.55, R2 = 0.20, p = 0.016 and trended toward significance at the ROI underneath Fz, F(1, 27) = 3.60, R2 = 0.12, p = 0.068 (Fig. 1C-E). We did not evaluate if TMS MT correlates with prefrontal reverse-calculation doses as we previously found that TMS MT did not correlate with reverse-calculation tDCS doses over the motor hotspot and also that TMS MT only has a trending relationship with TES MT [1]. In sum, we conducted a complementary study to Ref. [1], finding that TES MT acquired over the motor cortex could help to estimate ROI-based reverse-calculation tDCS doses in the prefrontal cortex. With further evaluation in larger sample sizes and in different populations and disease states, TES MT holds promise as an MRI-free technique to individually dose tDCS over not just motor areas [1] but also for prefrontal stimulation. Evaluating MRI-free approaches to individualize tDCS dosage would help to reduce the resources and cost that are required for reverse-calculation tDCS modeling. It is unclear why the reverse-calculation tDCS electric fields underneath F3 correlated more strongly with TES MT than underneath F4 or Fz. It may be due to the TES MT being acquired over the same left hemisphere as the ROI underneath F3, rather than between hemispheres (Fz) or in the right hemisphere (F4). The Fz location between hemispheres may be particularly prone to variability since it could contain a lower and more variable number of voxels between participants. Reverse-calculation modeling and TES MT acquisition should be further refined and evaluated as methods of individually dosing tDCS. Further research should investigate the use of reverse-calculation tDCS modeling, TES MT, or both to prospectively dose tDCS.

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          Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping.

          The recent advent of multichannel near-infrared spectroscopy (NIRS) has expanded its technical potential for human brain mapping. However, NIRS measurement has a technical drawback in that it measures cortical activities from the head surface without anatomical information of the object to be measured. This problem is also found in transcranial magnetic stimulation (TMS) that transcranially activates or inactivates the cortical surface. To overcome this drawback, we examined cranio-cerebral correlation using magnetic resonance imaging (MRI) via the guidance of the international 10-20 system for electrode placement, which had originally been developed for electroencephalography. We projected the 10-20 standard cranial positions over the cerebral cortical surface. After examining the cranio-cerebral correspondence for 17 healthy adults, we normalized the 10-20 cortical projection points of the subjects to the standard Montreal Neurological Institute (MNI) and Talairach stereotactic coordinates and obtained their probabilistic distributions. We also expressed the anatomical structures for the 10-20 cortical projection points probabilistically. Next, we examined the distance between the cortical surface and the head surface along the scalp and created a cortical surface depth map. We found that the locations of 10-20 cortical projection points in the standard MNI or Talairach space could be estimated with an average standard deviation of 8 mm. This study provided an initial step toward establishing a three-dimensional probabilistic anatomical platform that enables intra- and intermodal comparisons of NIRS and TMS brain imaging data.
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            Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping

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              Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully automated open-source pipeline

              Objective. Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject’s MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool—realistic volumetric-approach to simulate transcranial electric simulation (ROAST)—is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement. Approach. ROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline. Main results. The electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS. Significance. We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.
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                Author and article information

                Contributors
                Journal
                101465726
                35618
                Brain Stimul
                Brain Stimul
                Brain stimulation
                1935-861X
                1876-4754
                3 February 2021
                19 May 2020
                Jul-Aug 2020
                18 February 2021
                : 13
                : 4
                : 1150-1152
                Affiliations
                Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA
                Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA
                Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA
                Department of Biomedical Engineering, City College of New York, USA
                Brain Stimulation Laboratory, Department of Psychiatry, Medical University of South Carolina, Charleston, SC, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, USA
                Author notes
                [* ]Corresponding author. 67 President St. 504N, Charleston, SC, 29425, USA. caulfiel@ 123456musc.edu (K.A. Caulfield).
                Article
                NIHMS1666638
                10.1016/j.brs.2020.05.012
                7891110
                32439562
                f636e381-685c-4626-92af-51d60ffb6ae3

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Neurosciences
                electric field modeling,transcranial direct current stimulation,tdcs,tdcs modeling,reverse-calculation,reverse-calculation modeling,tdcs dosing,individualized tdcs dosing

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