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micapipe from the Multimodal imaging and connectome analysis lab (https://mica-mni.github.io) at the Montreal Neurological Institute. Readthedoc documentation below

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micapipe logo

Multimodal connectome processing with the micapipe

License: GPL v3 Documentation Status CircleCI Codacy Badge GitHub stars GitHub issues version

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 and Diffusion weighted images.

Documentation

You can find the documentation in micapipe.readthedocs.io

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 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'

python packages

Package Version
brainspace 0.1.1
certifi 2020.6.20
cycler 0.10.0
enigmatoolbox 0.0.1
joblib 0.16.0
kiwisolver 1.2.0
matplotlib 3.3.1
nibabel 3.1.1
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

R libraries

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
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

micapipe usage examples

Optional Flags

Will keep the temporal directory, and the session name is defined as "ses-01"

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -ses 01 -nocleanup -proc_structural

First stage basic processing

Volumetric processing

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -proc_structural

Freesurfer recon-all processing

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -proc_freesurfer

Runs both modules of first stage: volumetric and freesurfer

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -proc_structural -proc_freesurfer

Second stage processing (requires stage 1 completed)

Post structural volumetric processing

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -post_structural

DWI-Diffusion weighted images processing with MRtrix3

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -proc_dwi

Third stage processing (requires stage 1 and 2 completed)

Resting state Funtional MRI processing and functional connectome

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -proc_rsfmri

Structural connectome generation

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -SC

Microstructural profiles and covariance analysis

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -MPC

Geodesic distance analysis

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -GD

Further analysis (requires stages 1,2 and 3 completed)

Cortical morphology analysis

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -post_morph

Run all processing stages

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -all

Quality check (under development)

This module can be used at any stage of processing

mica-pipe  -sub <subject_id> -out <outputDirectory> -bids <BIDS-directory> -QC

Acknowledgements

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micapipe from the Multimodal imaging and connectome analysis lab (https://mica-mni.github.io) at the Montreal Neurological Institute. Readthedoc documentation below

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