FreeSurfer MRI reconstruction

This tutorial covers how to use FreeSurfer alongside MNE-Python, to handle the structural MRI data that we use to build subject-specific anatomical models of the scalp, inner/outer skull, and cortical surface.

FreeSurfer is an open source analysis toolbox for MRI data, available from https://surfer.nmr.mgh.harvard.edu/. FreeSurfer provides graphical interfaces for visualizing MRI data, several anatomical parcellations useful for creating region-of-interest (ROI) labels, template brains such as fsaverage, and several command-line tools for tasks like finding tissue boundaries or morphing brains to align analogous anatomical regions across subjects.

These FreeSurfer capabilities are necessary for MNE-Python to compute the forward model and set up the corresponding source space (a grid of dipoles located on the cortical surface or within the brain volume).

First steps

After downloading and installing FreeSurfer, there are a few steps to set up the environment. First is to define an environment variable FREESURFER_HOME and then run the FreeSurfer setup script:

$ export FREESURFER_HOME=/path/to/FreeSurfer
$ source $FREESURFER_HOME/SetUpFreeSurfer.sh

Note

The FreeSurfer home directory will vary depending on your operating system and choices you made during installation. See the FreeSurfer installation guide for more info.

Another important step is to tell FreeSurfer where to put the anatomical reconstructions of your research subjects. This is done with an environment variable called SUBJECTS_DIR, which will contain the individual subjects’ reconstructions in separate sub-folders.

$ export SUBJECTS_DIR=/path/to/your/subjects_dir

Again see the FreeSurfer installation guide for more info.

Anatomical reconstruction

The first processing stage is the creation of various surface reconstructions. Usually a full FreeSurfer reconstruction is obtained by the following commands:

$ my_subject=sample
$ my_NIfTI=/path/to/NIfTI.nii.gz
$ recon-all -i $my_NIfTI -s $my_subject -all

where i stands for “input” and s for “subject”. Executing this will create the folder $SUBJECTS_DIR/sample and populate it with several subfolders (bem, label, mri, etc). See also the FreeSurfer wiki’s recommended reconstruction workflow for more detailed explanation.

Warning

Anatomical reconstruction can take several hours, even on a fast computer.

FreeSurfer performs a hemispheric separation so most resulting files have separate left and right hemisphere versions, indicated by the prefix lh or rh. This hemispheric separation is preserved by MNE-Python (e.g., mne.SourceEstimate objects store spatial locations (vertices) for the two hemispheres separately; cf. The SourceEstimate data structure).

Below we show an example of the results of a FreeSurfer reconstruction for the left hemisphere of the Sample dataset subject, including an overlay of an anatomical parcellation (in this case, the parcellation from [1]).

import mne

sample_data_folder = mne.datasets.sample.data_path()
subjects_dir = sample_data_folder / 'subjects'
Brain = mne.viz.get_brain_class()
brain = Brain('sample', hemi='lh', surf='pial',
              subjects_dir=subjects_dir, size=(800, 600))
brain.add_annotation('aparc.a2009s', borders=False)

Use with MNE-Python

For source localization analysis to work properly, it is important that the FreeSurfer reconstruction has completed beforehand. Furthermore, for many MNE-Python functions related to inverse imaging (such as mne.setup_source_space), SUBJECTS_DIR has to be defined globally (as an environment variable or through a call to mne.set_config), or specified separately in each function call by passing the keyword argument subjects_dir='/path/to/your/subjects_dir'.

See Setting up the source space to get an idea of how this works for one particular function, and How MNE uses FreeSurfer’s outputs for more details on how MNE-Python and FreeSurfer are integrated.

‘fsaverage’

During installation, FreeSurfer copies a subject called 'fsaverage' to $FREESURFER_HOME/subjects/fsaverage. fsaverage is a template brain based on a combination of 40 MRI scans of real brains. The fsaverage subject folder contains all the files that a normal subject reconstruction would yield. See https://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage for an overview, and https://surfer.nmr.mgh.harvard.edu/fswiki/Buckner40Notes for details about the subjects used to create fsaverage. A copy of fsaverage is also provided as part of the Sample dataset and is also distributed as a standalone dataset.

One of the most common uses of fsaverage is as a destination space for cortical morphing / source estimate transformations. In other words, it is common to morph each individual subject’s estimated brain activity onto the fsaverage brain, so that group-level statistical comparisons can be made.

References

[1]

Christophe Destrieux, Bruce Fischl, Anders Dale, and Eric Halgren. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage, 53(1):1–15, 2010. doi:10.1016/j.neuroimage.2010.06.010.

Estimated memory usage: 22 MB