Connectivity-Based Parcellation (CBP) has become one of the most interesting application of MRI-based tractography [1], [2], [3], [4], [5], [6]. So far however performing CBP required extensive coding and technical skills.
The Parcellotron is a Connectivity-Based Parcellation software for the rest of us. It enables every researcher having access to tractography data to perform CBP from an intuitive graphical user interface, as well as from a command-line interface.
The Parcellotron was created by Leonardo Cerliani, Chris Foulon, Michel Thiebaut de Schotten at the BCBlab of the ICM in Paris and by Daniel Margulies and Marcel Falkiewicz at the MPI for Human Cognitive and Brain Sciences in Leipzig.
Follow this link to clone or download the repository.
$ git clone http://github.com/chrisfoulon/Parcellotron
Currently the Parcellotron accepts two kinds of input:
- Tractography 4D - One 4D Nifti file containing the structural connectivity maps for each ROI in the seed region
- Tractography matrix - The omatrix1 and omatrix3 output by FSL Fdt
We explain here how to perform CBP using the omatrix1 output by FSL Fdt. A tutorial on the 4D format is coming soon.
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Perform the probabilistic tractography from every voxel inside a mask containing both the seed region you intend to parcellate, and the target region, e.g the whole brain. You can learn how to do that on the FSL Fdt User Guide
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Create a folder structure similar to the one below. E.g. for a given subject S_123456 create a subfolder Tracto_mat containing:
- the omat1 directory output which is output by the FSL Fdt tractography
- two Nifti images, one for the seed region to be parcellated, the other for the target region, named respectively seedMask.nii.gz and targetMask.nii.gz
- Launch the Parcellotron with:
$ python3 parcellotron.py
- Follow the instructions in the GUI and choose the appropriate parameters according to your data type and hypotheses.
- Prefix of seed and target files: (Coming soon. For now just use the names seedMask and targetMask with no prefixes)
- Modality: Refer to the Input data section above.
- Size of the ROIs: in cubic mm. The number of seed voxels in each ROI will be rounded to fit the size specified here.
[1]: Johansen-Berg, H, Behrens, TE, Robson, MD, Drobnjak, I, Rushworth, MF, Brady, JM, Smith, SM, Higham, DJ, Matthews, PM (2004) Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc Natl Acad Sci U S A, 101:13335–13340.
[2]: Thiebaut de Schotten, M, Urbanski, M, Batrancourt, B, Levy, R, Dubois, B, Cerliani, L, Volle, E (2016) Rostro-caudal Architecture of the Frontal Lobes in Humans. Cereb Cortex.
[3]: Cerliani, L, D’Arceuil, H, Thiebaut de Schotten, M (2016) Connectivity-based parcellation of the macaque frontal cortex, and its relation with the cytoarchitectonic distribution described in current atlases. Brain Struct Funct.
[4]: Goulas, A, Stiers, P, Hutchison, RM, Everling, S, Petrides, M, Margulies, DS (2017) Intrinsic functional architecture of the macaque dorsal and ventral lateral frontal cortex. J Neurophysiol, 117:1084–1099.
[5]: Jakobsen, E, Liem, F, Klados, MA, Bayrak, Ş, Petrides, M, Margulies, DS (2016) Automated individual-level parcellation of Broca’s region based on functional connectivity. Neuroimage.
[6]: Cloutman, LL, Lambon Ralph, MA (2012) Connectivity-based structural and functional parcellation of the human cortex using diffusion imaging and tractography. Front Neuroanat, 6:34.
The development of the Parcellotron was funded by grants from the INCF (International Neuroinformatics Coordinating Facility) and from the Association Naturalia et Biologia
