Estimating bundle specific myelin content

Assuming you have a tractogram called tractogram.tck, a co-registered myelin volume fraction map myelin_map.nii.gz and a white-matter mask WM_mask.nii.gz in the same directory, with the following code you can perform Myelin Streamline Decomposition (MySD) to reproduce results as in Schiavi et al., 2022.

Initialize COMMIT

import commit
commit.setup() # NB: this is required only the first time you run COMMIT

Import the tractogram

# Load the tractogram
from commit import trk2dictionary
trk2dictionary.run(
     filename_tractogram = 'tractogram.tck',
     filename_mask  = 'WM_mask.nii.gz',
     fiber_shift    = 0.5,
     ndirs = 1
)

Load the myelin-sensitive map

# Set parameters
mit = commit.Evaluation()

# Load the myelin data
mit.load_data( 'myelin_map.nii.gz' )

Fit with MySD

Now that the data are loaded, you are ready to run MySD. Please note that the steps are the same as in the Getting Started tutorial, except for the model selection.

# Set model and generate the response functions
mit.set_model( 'ScalarMap' )
mit.generate_kernels()
mit.load_kernels()

# Load dictionary and buid the operator
mit.load_dictionary()
mit.set_threads()
mit.build_operator()

# Fit model to the data
mit.fit( tol_fun=1e-3, max_iter=1000 )
mit.save_results()

Once the fitting has reached convergence, you find the estimated myelin contributions of each streamline in the file COMMIT/Results_ScalarMap/streamline_weights.txt, which is a text file with one row for each streamline; you can use this information in your usual analysis pipeline to evaluate e.g. the myelination of different bundles or the myelin-weighted connectivity.