micapipe is developed by MICA-lab at McGill University for use at the Neuro, McConnell Brain Imaging Center (BIC).
The main goal of this pipeline is to provide a semi-flexible and robust framework to process MRI images and generate ready to use modality based connectomes.
Themicapipeutilizes a set of known software dependencies, different brain atlases, and software developed in our laboratory. The basic cutting edge processing of our pipelines aims the T1 weighted images, resting state fMRI and Diffusion weighted images.
You can find the documentation in micapipe.readthedocs.io
You can find the latest version of the container in Docker
- Microstructure Profile Covariance (Paquola C et al. Plos Biology 2019).
- Multiple parcellations (18 x 3).
- Includes cerebellum and subcortical areas.
- Surface based analysis.
- Latest version of software dependencies.
- Ready to use outputs.
- Easy to use.
- Standardized format (BIDS).
Raúl R. Cruces, Jessica Royer, Peer Herholz, Sara Larivière, Reinder Vos de Wael, Casey Paquola, Oualid Benkarim, Bo-yong Park, Janie Degré-Pelletier, Mark Nelson, Jordan DeKraker, Christine Tardif, Jean-Baptiste Poline, Luis Concha, Boris C. Bernhardt. (2022). Micapipe: A Pipeline for Multimodal Neuroimaging and Connectome Analysis. bioRxiv 2022.01.31.478189. doi: https://doi.org/10.1101/2022.01.31.478189
| Software | Version | Further info |
|---|---|---|
| dcm2niix | v1.0.20190902 | https://github.com/rordenlab/dcm2niix |
| Freesurfer | 6.0.0 | https://surfer.nmr.mgh.harvard.edu/ |
| FSl | 6.0 | https://fsl.fmrib.ox.ac.uk/fsl/fslwiki |
| AFNI | 20.3.03 | https://afni.nimh.nih.gov/download |
| MRtrix3 | 3.0.1 | https://www.mrtrix.org |
| ANTs | 2.3.4 | https://github.com/ANTsX/ANTs |
| workbench | 1.4.2 | https://www.humanconnectome.org/software/connectome-workbench |
| FIX | 1.06 | https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX |
| R | 3.6.3 | https://www.r-project.org |
| python | 3.7.6 | https://www.python.org/downloads/ |
The FIX package (FMRIB's ICA-based Xnoiseifier) requires FSL, R and one of MATLAB Runtime Component, full MATLAB or Octave. We recommend the use of the MATLAB Runtime Component. Additionally, it requires the following R libraries: 'kernlab','ROCR','class','party','e1071','randomForest'
| Package | Version |
|---|---|
| brainspace | 0.1.1 |
| certifi | 2020.6.20 |
| cycler | 0.10.0 |
| argparse | 0.1.1 |
| joblib | 0.16.0 |
| kiwisolver | 1.2.0 |
| matplotlib | 3.3.1 |
| nibabel | 3.1.1 |
| nilearn | 0.6.2 |
| numpy | 1.19.1 |
| packaging | 20.4 |
| pandas | 1.1.1 |
| Pillow | 7.2.0 |
| pyparsing | 2.4.7 |
| python-dateutil | 2.8.1 |
| pytz | 2020.1 |
| scikit-learn | 0.23.2 |
| scipy | 1.5.2 |
| six | 1.15.0 |
| threadpoolctl | 2.1.0 |
| vtk | 9.0.1 |
| Core library | version | Dependency library | version | Dependency library | version |
|---|---|---|---|---|---|
| plotly | 4.9.2.1 | tidyselect | 1.1.0 | parallel | 3.6.3 |
| viridis | 0.5.1 | coin | 1.3-1 | TH.data | 1.0-10 |
| viridisLite | 0.3.0 | purrr | 0.3.4 | Rcpp | 1.0.5 |
| tidyr | 1.1.2 | splines | 3.6.3 | jsonlite | 1.6.1 |
| ggplot2 | 3.3.2 | lattice | 0.20-41 | gridExtra | 2.3 |
| scales | 1.1.1 | colorspace | 1.4-1 | digest | 0.6.27 |
| randomForest | 4.6-14 | vctrs | 0.3.5 | dplyr | 1.0.2 |
| e1071 | 1.7-4 | generics | 0.0.2 | tools | 3.6.3 |
| party | 1.3-5 | htmltools | 0.4.0 | magrittr | 2.0.1 |
| strucchange | 1.5-2 | survival | 3.1-12 | lazyeval | 0.2.2 |
| sandwich | 2.5-1 | rlang | 0.4.9 | tibble | 3.0.4 |
| zoo | 1.8-7 | pillar | 1.4.3 | crayon | 1.3.4 |
| modeltools | 0.2-23 | glue | 1.4.2 | pkgconfig | 2.0.3 |
| mvtnorm | 1.1-1 | withr | 2.3.0 | MASS | 7.3-51.5 |
| class | 7.3-17 | matrixStats | 0.56.0 | ellipsis | 0.3.0 |
| ROCR | 1.0-11 | multcomp | 1.4-13 | libcoin | 1.0-6 |
| kernlab | 0.9-29 | lifecycle | 0.2.0 | Matrix | 1.2-18 |
| networkD3 | 0.4 | munsell | 0.5.0 | data.table | 1.12.8 |
| gtable | 0.3.0 | httr | 1.4.1 | ||
| htmlwidgets | 1.5.1 | R6 | 2.4.1 | ||
| codetools | 0.2-16 | compiler | 3.6.3 |