Tutorial: E-field modeling using simNIBS

• The necessary steps to obtain a high resolution individualized head mesh are detailed HERE.

 

• To create individualized models, SimNIBS requireas a T1-weighted image. T2-weighted images are optional, but highly recommended. 
• Specific sequence parameters can be seen at Nielsen et al., NeuroImage, 2018.
• Headreco is the preferred method (and the fastest), but requires Matlab.

• SimNIBS offers both Python and MATLAB interfaces for setting up and running simulations. In both, a set of nested structures is used to define the simulation, and then the command run_simnibs is used to run the simulation.
• You can find detailed explanation on the various commands on the official website HERE.

• Here we detail a Python script for TMS:

from simnibs import sim_struct, run_simnibs

# Initalize a session
s = sim_struct.SESSION()
# Name of head mesh
s.fnamehead = 'ernie.msh'
# Output folder
s.pathfem = 'tutorial/'

# Initialize a list of TMS simulations
tmslist = s.add_tmslist()
# Select coil
tmslist.fnamecoil = 'Magstim_70mm_Fig8.nii.gz'

# Initialize a coil position
pos = tmslist.add_position()
# Select coil centre
pos.centre = 'C1'
# Select coil direction
pos.pos_ydir = 'CP1'

# Add another position
pos_superior = tmslist.add_position()
# Centred at C1
pos_superior.centre = 'C1'
# Pointing towards Cz
pos_superior.pos_ydir = 'Cz'

run_simnibs(s)

• SimNIBS offers both Python and MATLAB interfaces for setting up and running simulations. In both, a set of nested structures is used to define the simulation, and then the command run_simnibs is used to run the simulation.
• You can find detailed explanation on the various commands on the official website HERE.

• Here is detailed a typical tDCS script using Python:

from simnibs import sim_struct, run_simnibs

# Initalize a session
s = sim_struct.SESSION()
# Name of head mesh
s.fnamehead = 'ernie.msh'
# Output folder
s.pathfem = 'tutorial/'

# Initialize a tDCS simulation
tdcslist = s.add_tdcslist()
# Set currents
tdcslist.currents = [-1e-3, 1e-3]

# Initialize the cathode
cathode = tdcslist.add_electrode()
# Connect electrode to first channel (-1e-3 mA, cathode)
cathode.channelnr = 1
# Electrode dimension
cathode.dimensions = [50, 70]
# Rectangular shape
cathode.shape = 'rect'
# 5mm thickness
cathode.thickness = 5
# Electrode Position
cathode.centre = 'C3'
# Electrode direction
cathode.pos_ydir = 'Cz'

# Add another electrode
anode = tdcslist.add_electrode()
# Assign it to the second channel
anode.channelnr = 2
# Electrode diameter
anode.dimensions = [30, 30]
# Electrode shape
anode.shape = 'ellipse'
# 5mm thickness
anode.thickness = 5
# Electrode position
anode.centre = 'C4'

run_simnibs(s)

• When using this feature in a publication, please cite Saturnino, G. B., Siebner, H. R., Thielscher, A., & Madsen, K. H. (2019). Accessibility of cortical regions to focal TES: Dependence on spatial position, safety, and practical constraints. NeuroImage, 203, 116183.

 

• Step 1: Create a leadfield based on electrode location

 

from simnibs import sim_struct, run_simnibs

tdcs_lf = sim_struct.TDCSLEADFIELD()

# head mesh

tdcs_lf.fnamehead = ‘ernie.msh’

# output directory

tdcs_lf.pathfem = ‘folder_leadfield’

tdcs_lf.eeg_cap=’electrodes_location.csv’

 

# run the simulations

run_simnibs(tdcs_lf)

 

• Step 2: optimize electrode location and amplitude based on leadfield.

 

import simnibs

# Initialize structure

opt = simnibs.opt_struct.TDCSoptimize()

# Select the leadfield file

opt.leadfield_hdf = ‘folder_leadfield/ernie_leadfield_electrodes_location.hdf5’

# Select a name for the optimization

opt.name = ‘optimization/dual_target’

# Select a maximum total current (in A)

opt.max_total_current = 2e-3

# Select a maximum current at each electrodes (in A)

opt.max_individual_current = 1e-3

# Select a maximum number of active electrodes (optional)

opt.max_active_electrodes = 6

# Define optimization target

target1 = opt.add_target()

# Position of target, in subject space!

# please see tdcs_optimize_mni.py for how to use MNI coordinates

target1.positions = [-12, 87, 48]

# Intensity of the electric field (in V/m)

target1.intensity = 0.2

target2 = opt.add_target()

target2.positions = [-81, -54, 16]

target2.intensity = 0.2 # negative value revert the direction

# Run optimization

simnibs.run_simnibs(opt)

 

• You can find more details on the official website HERE.

• SimNIBS can help finding the best TMS position for stimulating a certain target. This is done by searching coil positions in a grid around the target position and turning the coil at various angles.
• When using this feature in a publication, please cite Weise, K., Numssen, O., Thielscher, A., Hartwigsen, G., & Knösche, T. R. (2020). A novel approach to localize cortical TMS effects. Neuroimage, 209, 116486.

 

• Typical script:

from simnibs import opt_struct

# Initialize structure
tms_opt = opt_struct.TMSoptimize()
# Select the head mesh
tms_opt.fnamehead = 'ernie.msh'
# Select output folder
tms_opt.pathfem = 'tms_optimization/'
# Select the coil model
tms_opt.fnamecoil = 'Magstim_70mm_Fig8.nii.gz'
# Select a target for the optimization
tms_opt.target = [-43.4, -20.7, 83.4]

# Run optimization to get optimal coil position
opt_pos=tms_opt.run()

• You can find more details on the official website HERE.