Toolbox to perform Connectivity Based Parcellation from Diffusion MRI data used in the paper "A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort".
Please cite the corresponding paper, if using the code:
Coelho, A., Magalhaes, R., Moreira, P. S., Amorim, L., Portugal-Nunes, C., Castanho, T., Santos, N. C., Sousa, N., & Fernandes, H. M. (2022). A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort. Hum Brain Mapp. https://doi.org/10.1002/hbm.25773
- Matlab
- FSL (follow instructions from their website: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation
- Python with the following libraries (all available to install with pip):
numpynipypenibabelcc3dpandaspicklescipymatplotlibsklearnSimpSOMCluster_Ensemblestqdmmultiprocessingjoblib
bash pipeline.sh code_dir in_dir wd data_dir subj_list atlas nrois min_k max_k transform thr nsim size_thr steps
Parameters:
code_dir: path to this repository's directoryin_dir: path to folder with connectivity matriceswd:path to working directorydata_dir: path to folder with image datasubj_list: file with participants IDsatlas: atlas namenrois: atlas total number of regionsmin_k: mininum number of clustersmax_k: maximum number of clusterstransform: name of transform to normalize matricesthr: threshold value for matricesnsim: number of simulations for SOMssize_thr: minimum size for clusterssteps: array with the steps of the pipeline to run
Input data (one folder per participant):
- structural connectivity matrices (fdt_matrix2.dot from probtrackX)
- b0 and anatomical images with the corresponding brain masks
Atlas image file should be in the working directory