Ok i have tried binding the controller with 1 sat just like you would with a normal rx and still nothing. Lee i think i remmen=mber you saying or someone using the ikon what is the right way to bind this controller and sat i am using a rx pack from my eagle trainer for the power i will post a pic of what i got. Also i can't seem to get it to connect to my computer forgot how to find out which com port it is on. Any help would be greatso i can finish getting the 500 air ready.after this setup it should finally see more then just a spool up.lol.
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. 1.5k DownloadsAbstractNumerical simulation of the electric fields induced by non-invasive brain stimulation (NIBS), using realistic anatomical head models has gained interest in recent years for understanding the NIBS effects in individual subjects. Although automated tools for generating the head models and performing the electric field simulations have become available, individualized modelling is still not a standard practice in NIBS studies. This is likely partly explained by the lack of robustness and usability of the previously available software tools, and partly by the still developing understanding of the link between physiological effects and electric field distributions in the brain. To facilitate individualized modelling in NIBS, we have introduced the SimNIBS (Simulation of NIBS) software package, providing easy-to-use automated tools for electric field modelling. In this chapter, we give an overview of the modelling pipeline in SimNIBS 2.1, with step-by-step examples of how to run a simulation. Furthermore, we demonstrate a set of scripts for extracting average electric fields for a group of subjects, and finally demonstrate the accuracy of automated placement of standard electrode montages on the head model.
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SimNIBS 2.1 is freely available at. Non-invasive brain stimulation (NIBS) aims at modulating brain activity by inducing electric fields in the brain. The electric fields are generated either by a magnetic coil, in the case of transcranial magnetic stimulation (TMS), or by a current source and electrodes placed directly on the scalp, in the case of transcranial electric stimulation (TES). In both cases, the induced electric fields in the brain have a complex and often counter-intuitive spatial distribution, which is dependent on the individual anatomy of a target subject. In recent years, there has been a growing interest in moving away from a one-size-fits-all stimulation approach in NIBS to more individually informed protocols. The driving force behind this shift is the widely reported variation of NIBS effects within and between individuals , which could be explained in part by the interplay of the individual anatomy and the electric field propagation.
Although software tools have become available that generate realistic anatomical models of the head based on magnetic resonance imaging (MRI) scans and use those models to numerically estimate the electric field induced in the brain, they are still not predominantly used in NIBS studies. This is likely due to the lack of robustness and usability of the previous generation of tools, in turn hampering the individualized application of NIBS in both mapping the human brain function and as a rehabilitation tool in various neuropathologies ,.The aim of SimNIBS is to facilitate the use of individualized stimulation modelling by providing easy-to-use software tools for creating head models, setting up electric field simulations, and visualizing and post-processing the results both at individual and group levels. SimNIBS was first released in 2013 , had a major update in 2015, with the release of version 2 , and more recently another major update with the release of version 2.1, described in the current work.
SimNIBS 2.1 is a free software, distributed under a GPL 3 license, and runs on all major operating systems (Windows, Linux and MacOS). In this tutorial, we will concentrate on what SimNIBS 2.1 can be used for and how the analyses are performed in practice with step-by-step examples. The chapter is structured as follows: First, we give a general overview of the simulation pipeline and of its building blocks.
Next, we provide a step-by-step example of how to run a simulation in a single subject, and then we demonstrate a set of MATLAB tools developed for easy processing of multiple subjects. Finally, we conclude with an analysis of the accuracy of automated electrode positioning approaches. More information, as well as detailed tutorials and documentation can be found from the website.
1.2 Overview of the SimNIBS Workflow. Figure shows an overview of the SimNIBS workflow for an individualized electric field simulation. The workflow starts with the subject’s anatomical MRI images, and optionally diffusion-weighted MRI images. These images are segmented into major head tissues (white and grey matter, cerebrospinal fluid, skull and scalp). From the segmentations, a volume conductor model is created, and used for performing the electric field simulations. The simulations can be set up in a graphical user interface (GUI) or by scripting.
Finally, the results can be mapped into standard spaces, such as the Montreal Neurological Institute (MNI) space or FreeSurfer’s FsAverage. The minimum requirement for running an individualized SimNIBS simulation is a T1-weighted structural scan of a subject’s head anatomy. Although SimNIBS will run on almost all types of T1-weighted scans, we have found that setting the readout bandwidth low to ensure a good signal-to-noise ratio in the brain region and using a fat suppression method, such as selective water excitation, to minimize the signal from spongy bone, typically ensure a high quality of the resulting head models. For an example of good quality scans we found to work well with SimNIBS and for the details of the sequences. 1.3.1 Hello SimNIBS: How to Process a Single SubjectHere we describe how to run a TMS and a tDCS simulation on a single example subject. The example subject “Ernie” can be downloaded from the SimNIBS website, and the steps below can be reproduced step by step to get familiar with SimNIBS. Generating the Volume Conductor ModelOpen a terminal and go to the directory “ernie” to access the example data set.
Copy the content of the “org”-subfolder to another location in order to not overwrite the files of the original example dataset. Next, go to the folder where you copied the data, and call headreco to generate the volume conductor model:headreco all -cat ernie ernieT1.nii.gz ernieT2.nii.gzIn the command, the first argument, “ all”, tells headreco to run all reconstruction steps including: segmentation, clean-up of tissue maps, surface meshing, and volume meshing. The second argument, “ -cat” is a flag for using the CAT12 toolbox for accurate reconstruction of the cortical surface. The third argument, “ ernie”, is a subject identifier (subID), which is used to name generated folders, e.g. M2mernie, and output files, e.g. The two final arguments are the paths to the T1- and T2-weighted structural scans.-d no-conform Adding this option will prevent headreco from modifying, i.e.
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Transforming and resampling, the original MRI scan. This might be desirable when a one-to-one correspondence between the head model coordinates and the neural navigation system coordinates is required.-v This option allows you to set the resolution, or vertex density (nodes per mm 2), of the FEM mesh surfaces. By default, SimNIBS uses 0.5 nodes/mm 2 as the value.In general, we recommend using the -cat option; however, the execution time will be longer compared to omitting the option. In addition, if you want to process scans with pathologies, you should not use CAT12, as the cortical reconstruction is not designed to work with pathologies.After headreco has finished, please check the quality of the head model by calling:headreco check ernie.eegpositions/ Folder containing the 10-10 electrode positions for the subject both as a “.csv”, used for acquiring electrode positions, and a “.geo” file, used for visualization of the positions in Gmsh. If you have custom electrode positions, they should be added here as a.csv file.maskprep/ Folder containing the cleaned tissue maps along with the white matter and pial surface files if CAT12 was used. In case there are errors in the segmentation, the masks can be manually corrected and a new head model can subsequently be generated. Note that the CAT12 WM and GM surfaces can currently not be modified.headrecolog.html, a log-file with output from the headreco run.
If something goes wrong, the log-file helps with troubleshooting, and should be sent as an attachment when contacting the SimNIBS support email list ([email protected]).ernie.msh, the FEM head model used for the simulations.ernieT1fsconform.nii.gz, the input scan in the conform space defined by the –d option. This scan has the same millimetre space as the head model, and can be used to annotate landmarks which can then be directly transformed onto the head model.
In the GUI, there are two types of tabs, one for tDCS simulations, and another for TMS simulations, shown respectively in the top and bottom of Fig. The tDCS tabs define a single tDCS field simulation with an arbitrary number of electrodes. On the other hand, TMS tabs can define several TMS field simulations using the same coil. For this example, we will set up a tDCS simulation with a 5 × 5 cm anode placed over C3 and a 7 × 5 cm cathode placed over AF4, and a TMS simulation with the coil placed over the motor cortex, pointing posteriorly.
Details on how to use the graphical interface can be found on the website ( ). After the simulation setup, click on the Run button to start the simulations. Running both simulations takes 10–15 minutes, depending on the computer, and uses around 6 GB of memory. As a note, before starting the simulations, you can set additional options (in the menu Edit➔Simulation Options) to let SimNIBS write out the results as surface data or NifTI volume data. This is not further covered in this basic example, but the output files created in these cases are described in the next example.
The results of the simulation will be written in the output folder specified in the GUI, in this case “ simnibssimulation/”. The folder has the files shown below in Table.“ ernieTDCS1scalar.msh” is the output from the tDCS simulation, in Gmsh “.msh” format. The first part of the file name, “ ernie”, is the subID. The second part, “TDCS”, informs us that this is a tDCS simulation. The third part, “ 1”, denotes that this was the first simulation we have defined in the GUI, and finally, “ scalar” tells us have used scalar (as opposed to anisotropic) conductivities for the simulations.“ ernieTMS2-0001Magstim70mmFig8niiscalar.msh” is the output of the second simulation, also in gmsh “.msh” format. As is the case for the tDCS output, the first part of the file name is the subID, and the second is the number of the simulation in the simulation list.
We next see the number of the TMS position, as it might happen that several TMS positions are defined in a single TMS list. Following this, “ Magstim40mmFig8nii” gives us the name of the coil used for the simulation, and “ scalar” the type of conductivity.“ ernieTMS2-0001Magstim70mmFig8niicoilpos.geo” is a Gmsh “.geo” file which shows the coil position for the corresponding simulation.“ simnibssimulation2011.log” is a text file with a detailed log of the simulation steps. This file can be used for troubleshooting. Here, the second part of the file is date and time information of when the simulation started.“ simnibssimulation2011.mat” is a MATLAB data file with the simulation setups. This file can be loaded into the GUI or MATLAB at a later time to check the simulation parameters, or to change them and re-run the simulation.Visualizing Fields.
The electric field E is a vector field meaning that the electric field has both a norm (i.e. Vector length or magnitude) and a direction in space, as shown in Fig. As visualizations of the entire vector are challenging and often unclear, in SimNIBS we usually visualize the norm (or strength) of the electric field instead. The norm of the electric field corresponds to the size of the electric field vector, and therefore is always positive and does not contain any information about the direction of the electric field. The first lines in Table show that the displayed data is the field “norm E”, that is the norm or strength of the electric field, calculated in the region number 2, which corresponds to the GM volume.
Afterwards, we have information on the peak electric fields. We see that the value of 0.161 V/m corresponds to the 95th percentile of the norm of the electric field, the value of 0.201 V/m to the 99th percentile and 0.249 to the 99.9th percentile. We also have information about the focality of the electric field. Here, focality is measured as the GM volume with an electric field greater or equal to 50% or 75% of the peak value.
To avoid the effect of outliers, the peak value is defined as the 99.9th percentile. 1.9Visualization in Gmsh of ( a) electric field vectors around central gyrus for the tDCS simulation and ( b) TMS electric field depth profile in the hotspot 1.3.2 Advanced Usage: Group AnalysisNow, we want to simulate one tDCS montage, with a 5 × 5 cm electrode over C3 and a 5 × 7 cm electrode over AF4 in five subjects, called “sub01”, “sub09”, “sub10”, “sub12”, “sub15” and visualize the results in a common space, namely the FsAverage surface. The subjects and example scripts can be downloaded from: Head MeshingFor each subject, follow the steps in section “ ”.
Write a Python or MATLAB Script. To define the rectangular electrodes, we need two coordinates. The “ centre” defines where the electrode will be centred, and “ posydir” how the electrode will be rotated.
More precisely, the electrode’s “y” axis is defined as a unit vector starting at “centre” and pointing towards “ posydir”. Shows one of the cathodes (return electrode) defined using the script above, with the coordinate system and EEG positions overlaid. We can see that the electrode is centred in AF4, and its Y axis points towards F6. “ posydir” does not need to be set when the electrodes are round.
1.1050 × 70 mm electrode defined with a “centre” in AF4 and a “posydir” in F6When the maptofsavg option is set to true, SimNIBS computes the electric fields in a surface located in the middle of the GM layer. This cortical surface, along with the norm, normal and tangent components of the electric field at the cortical surface and the angle between the electric field and the cortical surface can found in the subjectoverlays folder, for both the left hemisphere ( lh) and for the right hemisphere ( rh) as shown in Table. Afterwards, these quantities are transformed into the FsAverage space. The transformed quantities can be found in the fsavgoverlays folder, as shown in Table. Additionally, we have the electric field and its norm in MNI space in the mnivolumes folder. Calculations using method A require no user input and are automatically performed in both mri2mesh and headreco head modelling pipelines, while calculations using method B require the user to manually select the fiducial positions.To compare the methods A and B to position the electrodes, we calculated the EEG 10-10 positions using both ways for MR data of 17 subjects.
The data was acquired as part of a larger study. The subjects gave written informed consent before the scan, and the study was approved by the local ethics committee of the University of Greifswald (Germany). The 17 datasets were acquired on a 3-Tesla Siemens Verio scanner (Siemens Healthcare, Erlangen, Germany) using a 32-channel head coil (T1: 1 × 1 × 1 mm 3, TR 2300 ms, TE 900 ms, flip angle 9°, with selective water excitation for fat suppression; T2: 1 × 1 × 1 mm 3, TR 12770 ms, TE 86 ms, flip angle 111°). For method B, the fiducials were manually located for each subject by a trained investigator on the T1- and T2-weighted images. The later had no knowledge of the automatically determined positions.
The fiducials Nz, Iz, LPA and RPA were set in freeview, following the procedure described in and additionally verified using the SimNIBS GUI. The subject-specific coordinates of the fiducials were extracted, and these manually set positions were then compared with those calculated by the automatic algorithm in each individual. 1.12Positioning error for electrodes in the EEG 10-10 system.
The error is calculated by comparing the positions calculated based on manually selected fiducials to positions calculated based on non-linear MNI transformationsThe errors for all electrodes are below 1 cm, indicating that the two algorithms for placing EEG electrodes are in agreement. We can also see that the errors in the EEG positions obtained from headreco are on average lower than the ones obtained from mri2mesh.
It also seems that the anterior electrodes have less errors than the posterior electrodes. Interestingly, the location of the errors is different across the two pipelines, with mri2mesh being more inaccurate in superior regions and headreco more inaccurate in posterior regions. This might be caused by differences in the way FSL ( mri2mesh) and SPM ( headreco) calculate non-linear MNI transformations is different. The average error across all positions was 5.6 mm for mri2mesh head models and 4.9 mm for headreco head models indicating good accuracy. 1.5 Conclusion.
We presented SimNIBS 2.1 ( ), a software for individualized modelling of electric fields caused by non-invasive brain stimulation. SimNIBS is free software and avaliable for all major platforms. SimNIBS does not require the installation of any additional software in order to run simulations on the example dataset.
To construct head models, SimNIBS relies either on MATLAB, SPM12 and CAT12 ( headreco) or on FSL and FreeSurfer ( mri2mesh).We also presented two examples of workflows in SimNIBS. In the first example, we started by using headreco to construct a head model. Following this, we used the GUI to set up a tDCS and a TMS simulation in an interactive way, and finally visualized the results. In the second example, we constructed several head models and used a MATLAB script to run simulations for each subject. We then calculated the mean and the stardard deviation of the electric field norm across all subjects, using the FreeSurfer’s FsAverage brain template. Finally, we show results validating our automatic procedure to obtain electrode positions for the EEG 10-10 system.SimNIBS is still being actively developed, and we expect further updates to be implemented in the future.
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