micapipe
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micapipe from the Multimodal imaging and connectome analysis lab (http://mica-mni.github.io) at the Montreal Neurological Institute. Read The Docs documentation below
Multimodal connectome processing with the micapipe
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.
Themicapipe
utilizes 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, quantitative MRI and Diffusion weighted images.
![micapipe](https://github.com/MICA-MNI/micapipe/raw/master/img/micapipe_logo_black.jpg)
Documentation
You can find the documentation in micapipe.readthedocs.io
Container
You can find the latest version of the container in Docker
Reference
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, Ilana Leppert, Christine Tardif, Jean-Baptiste Poline, Luis Concha, Boris C. Bernhardt. (2022). Micapipe: a pipeline for multimodal neuroimaging and connectome analysis. NeuroImage, 2022, 119612, ISSN 1053-8119. doi: https://doi.org/10.1016/j.neuroimage.2022.119612
Workflow
Advantages
- 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).
Dependencies
Software | Version | Further info |
---|---|---|
dcm2niix | v1.0.20190902 | https://github.com/rordenlab/dcm2niix |
Freesurfer | 7.3.2 | https://surfer.nmr.mgh.harvard.edu/ |
FSl | 6.0.2 | 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 | 2.3.3 | https://github.com/ANTsX/ANTs |
workbench | 1.3.2 | 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 | 3.9.16 | https://www.python.org/downloads/ |
conda | 22.11.1 | https://docs.conda.io/en/latest/ |
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 0.9.24','ROCR 1.0.7','class 7.3.14','party 1.0.25','e1071 1.6.7','randomForest 4.6.12'
python
mandatory packages conda
Package | Version |
---|---|
nibabel | 4.0.2 |
numpy | 1.21.5 |
pandas | 1.4.4 |
vtk | 9.2.2 |
pyvirtualdisplay | 3.0 |
python
mandatory packages pip
Package | Version |
---|---|
argparse | 1.1 |
brainspace | 0.1.10 |
tedana | 0.0.12 |
pyhanko | 0.17.2 |
mapca | 0.0.3 |
xhtml2pdf | 0.2.9 |
oscrypto | 1.3.0 |
tzdata | 2022.7 |
arabic-reshaper | 3.0.0 |
cssselect2 | 0.7.0 |
pygeodesic | 0.1.8 |
seaborn | 0.11.2 |
R
libraries
library | version |
---|---|
scales | 1.1.1 |
randomForest | 4.6-14 |
e1071 | 1.7-4 |
party | 1.3-5 |
strucchange | 1.5-2 |
sandwich | 2.5-1 |
zoo | 1.8-7 |
modeltools | 0.2-23 |
mvtnorm | 1.1-1 |
class | 7.3-17 |
ROCR | 1.0-11 |
kernlab | 0.9-29 |
coin | 1.3-1 |
pkgconfig | 2.0.3 |
MASS | 7.3-51.5 |
libcoin | libcoin |
Matrix | 1.2-18 |