mica-mni/micapipe

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.
The micapipe 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.

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