Connectomemapper3

Connectome Mapper 3

This neuroimaging processing pipeline software is developed by the Connectomics Lab at the University Hospital of Lausanne (CHUV) for use within the SNF Sinergia Project 170873, as well as for open-source software distribution.

Description

Connectome Mapper 3 is an open-source Python3 image processing pipeline software, with a Graphical User Interface, that implements full anatomical, diffusion, resting-state MRI, and EEG processing pipelines, from raw Diffusion / T1 / BOLD / preprocessed EEG to multi-resolution connection matrices, based on a new version of the Lausanne parcellation atlas, aka Lausanne2018.

Connectome Mapper 3 pipelines use a combination of tools from well-known software packages, including FSL, FreeSurfer, ANTs, MRtrix3, Dipy, AFNI, MNE, MNE_Connectivity, and Cartool (PyCartool) orchestrated by the Nipype dataflow library. These pipelines were designed to provide the best software implementation for each state of processing at the time conceptualization, and can be updated as newer and better neuroimaging software become available.

To enhance reproducibility and replicatibility, the processing pipelines with all dependencies are encapsulated in a Docker image container, which handles datasets organized following the BIDS standard and is distributed as a BIDS App @ Docker Hub. For execution on high-performance computing cluster, a Singularity image is also made freely available @ Sylabs Cloud.

To reduce the risk of misconfiguration and improve accessibility, Connectome Mapper 3 comes with an interactive GUI, aka cmpbidsappmanager, which supports the user in all the steps involved in the configuration of the pipelines, the configuration and execution of the BIDS App, and the control of the output quality. In addition, to facilitate the use by users not familiar with Docker and Singularity containers, Connectome Mapper 3 provides two Python commandline wrappers (connectomemapper3_docker and connectomemapper3_singularity) that will generate and run the appropriate command.

Since v3.1.0, CMP3 provides full support to EEG. Please check this notebook for a demonstration of the newly implemented pipeline, using the “VEPCON” dataset, available at https://openneuro.org/datasets/ds003505/versions/1.1.1.

How to install the python wrappers and the GUI?

You need to have first either Docker or Singularity engine and miniconda installed. We refer to the dedicated documentation page for more instruction details.

Then, download the appropriate environment.yml / environment_macosx.yml and create a conda environment py39cmp-gui with the following command:

$ conda env create -f /path/to/environment[_macosx].yml

Once the environment is created, activate it and install Connectome Mapper 3 with PyPI as follows:

$ conda activate py39cmp-gui
(py39cmp-gui)$ pip install connectomemapper

You are ready to use Connectome Mapper 3!

Resources

• JOSS paper: https://joss.theoj.org/papers/10.21105/joss.04248
• Documentation: https://connectome-mapper-3.readthedocs.io
• Mailing list: https://groups.google.com/forum/#!forum/cmtk-users
• Source: https://github.com/connectomicslab/connectomemapper3
• Bug reports: https://github.com/connectomicslab/connectomemapper3/issues

Carbon footprint estimation of BIDS App run 🌍🌳✨

In support to the Organisation for Human Brain Mapping (OHBM) Sustainability and Environmental Action (OHBM-SEA) group, CMP3 enables you since v3.0.3 to be more aware about the adverse impact of your processing on the environment!

With the new --track_carbon_footprint option of the connectomemapper3_docker and connectomemapper3_singularity BIDS App python wrappers, and the new "Track carbon footprint" option of the cmpbidsappmanager BIDS Interface Window, you can estimate the carbon footprint incurred by the execution of the BIDS App. Estimations are conducted using codecarbon to estimate the amount of carbon dioxide (CO2) produced to execute the code by the computing resources and save the results in <bids_dir>/code/emissions.csv.

Then, to visualize, interpret and track the evolution of the emitted CO2 emissions, you can use the visualization tool of codecarbon aka carbonboard that takes as input the .csv created::

$ carbonboard --filepath="<bids_dir>/code/emissions.csv" --port=xxxx

Please check https://ohbm-environment.org to learn more about OHBM-SEA!

Usage

Having the py39cmp-gui conda environment previously installed activated, the BIDS App can easily be run using connectomemapper3_docker, the python wrapper for Docker, as follows:

    usage: connectomemapper3_docker [-h]
                            [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
                            [--session_label SESSION_LABEL [SESSION_LABEL ...]]
                            [--anat_pipeline_config ANAT_PIPELINE_CONFIG]
                            [--dwi_pipeline_config DWI_PIPELINE_CONFIG]
                            [--func_pipeline_config FUNC_PIPELINE_CONFIG]
                            [--eeg_pipeline_config EEG_PIPELINE_CONFIG]
                            [--number_of_threads NUMBER_OF_THREADS]
                            [--number_of_participants_processed_in_parallel NUMBER_OF_PARTICIPANTS_PROCESSED_IN_PARALLEL]
                            [--mrtrix_random_seed MRTRIX_RANDOM_SEED]
                            [--ants_random_seed ANTS_RANDOM_SEED]
                            [--ants_number_of_threads ANTS_NUMBER_OF_THREADS]
                            [--fs_license FS_LICENSE] [--coverage]
                            [--notrack] [-v] [--track_carbon_footprint]
                            [--docker_image DOCKER_IMAGE]
                            [--config_dir CONFIG_DIR]
                            bids_dir output_dir {participant,group}

    Entrypoint script of the Connectome Mapper BIDS-App version v3.1.0 via Docker.
    
    positional arguments:
      bids_dir              The directory with the input dataset formatted
                            according to the BIDS standard.
      output_dir            The directory where the output files should be stored.
                            If you are running group level analysis this folder
                            should be prepopulated with the results of the
                            participant level analysis.
      {participant,group}   Level of the analysis that will be performed. Multiple
                            participant level analyses can be run independently
                            (in parallel) using the same output_dir.
    
    optional arguments:
      -h, --help            s