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Visualising ROAST results in MRIcroGL

To visualise the results of your ROAST simulations on MRIcroGL, you can follow these steps:

  • Open MRIcroGL and load the T1-weighted image (e.g., sw{name_of_your_scan}.nii). If you have used as T1 icbm_avg_152_t1_tal_nlin_symmetric_VI.nii, this will be swicbm_avg_152_t1_tal_nlin_symmetric_VI.nii.
  • File -> Add Overlay -> Select the ROAST output file you want to visualise (e.g., sw*emag*.nii, sw*e*.nii)
  • Adjust the overlay opacity setting of the smoothed and normalised nifti image to your liking (click on the layer of the scan, and adjust the opacity).
  • Lower down the opacity of the electric field nifti image to make the T1-weighted image more visible.
  • Switch to the layer of the electric field magnigude / vector and select blue2red as colour map.
  • Change the Darkest setting to 0 and the Brightest to 0.35 (or adjust it to your liking). This will make the electric field more visible.

The output should look similar to the image below: MRIcroGL Example

Visualising ROAST results in Python and applying ROIs

The nifti files generated by ROAST can also be opened and analysed with Python. Libraries such as nibabel and nilearn can be used to handle nifti files. You can also apply regions of interest (ROIs) to the ROAST results using these libraries or perform group-level analyses. This allows for more advanced statistical analyses and visualisations of the electric field distributions across different subjects or conditions. However, this is beyond the scope of this workshop, as it requires a more advanced knowledge of Python and its libraries.

If you are interested in using Python for visualising ROAST results and applying ROIs, I recommend checking out the python environment section of the repository, where I explain how to set up a Python environment with the necessary libraries installed. You can then use this environment to load the ROAST nifti files and perform your analyses.