Connectome Mapper 3
Latest released version: v3.0.2
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. Source code is hosted on GitHub.
THIS SOFTWARE IS FOR RESEARCH PURPOSES ONLY AND SHALL NOT BE USED FOR ANY CLINICAL USE. THIS SOFTWARE HAS NOT BEEN REVIEWED OR APPROVED BY THE FOOD AND DRUG ADMINISTRATION OR EQUIVALENT AUTHORITY, AND IS FOR NON-CLINICAL, IRB-APPROVED RESEARCH USE ONLY. IN NO EVENT SHALL DATA OR IMAGES GENERATED THROUGH THE USE OF THE SOFTWARE BE USED IN THE PROVISION OF PATIENT CARE.
Connectome Mapper 3 is an open-source Python3 image processing pipeline software,
with a Graphical User Interface (GUI), that implements full anatomical, diffusion and
resting-state MRI processing pipelines, from raw Diffusion / T1 / T2 / BOLD data
to multi-resolution connection matrices based on a new version of the Lausanne
parcellation atlas, aka
Connectome Mapper 3 pipelines use a combination of tools from
well-known software packages, including FSL, FreeSurfer, ANTs,
MRtrix3, Dipy and AFNI, empowered by the Nipype dataflow library.
These pipelines are designed to provide the best software implementation
for each state of processing at the time of conception, and can be
easily 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 enhanced accessibility and reduce the risk of misconfiguration,
Connectome Mapper 3 comes with an interactive GUI, aka
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_singularity) that will
generate and run the appropriate command.
New in v3.0.2 🌍🌳✨
In support to the Organisation for Human Brain Mapping (OHBM) Sustainability and Environmental Action (OHBM-SEA) group, CMP3 enables you now to be aware about the adverse impact of your processing on the environment!
With the new
--track_carbon_footprint option of the
connectomemapper3_singularity BIDS App python wrappers, and
"Track carbon footprint" option of the BIDS Interface Window of
cmpbidsappmanager, 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
Then, to visualize, interpret and track the evolution of the emitted CO2 emissions, you can use the visualization tool of
carbonboard that takes as input the
$ carbonboard --filepath="<bids_dir>/code/emissions.csv" --port=xxxx
Please check https://ohbm-environment.org to learn more about OHBM-SEA!
This software is distributed under the open-source license Modified BSD. See license for more details.
All trademarks referenced herein are property of their respective holders.
If your are using the Connectome Mapper 3 in your work, please acknowledge this software. See Citing for more details.
Eager to contribute?
Connectome Mapper 3 is open-source and all kind of contributions (bug reporting, documentation, code,…) are welcome! See Contributing to Connectome Mapper for more details.
- Connectome Mapper 3 and the BIDS standard
- Commandline Usage
- Graphical User Interface
- Outputs of Connectome Mapper 3
- Packages and modules
- cmp package
- Pipelines and stages modules
- GUI modules
- cmtklib package
- cmp package
- Adopting Datalad for collaboration
- Running on a cluster (HPC)
Work supported by the SNF Sinergia Grant 170873 (http://p3.snf.ch/Project-170873).