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REV: Add high-level paper summary #175

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87 changes: 54 additions & 33 deletions paper/paper.md
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Expand Up @@ -75,6 +75,28 @@ bibliography: paper.bib
# aas-journal: Astrophysical Journal <- The name of the AAS journal.
---

# Summary

Connectome Mapper 3 (CMP3) is an open-source processing pipeline software, written in Python 3,
for multi-scale multi-modal connectome mapping of the human brain.
It provides researchers with a unique workflow, implemented in accordance with the BIDS App framework
[@GorgolewskiBIDSMethods:2017], that leverages a number of widely adopted software tools to map a graph
representation of the structural and functional connections between brain regions, the connectome,
at multiple scales.
The workflow can take any structural / diffusion / resting-state functional MRI dataset structured accordingly
to the BIDS standard [@GorgolewskiTheExperiments:2016], and is intended to be run via its Docker and
Singularity/Apptainer containerized versions.
To improve its accessibility, CMP3 comes with a graphical user interface, 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.
CMP3 has been successfully employed in a number of research papers, and is currently being extended to
electroencephalography, to offer a solid multi-modal framework to the community for the investigation of
brain network function and organization at specific scales, as well as a map that links different spatial
and temporal scales.
CMP3 is available from the [Python Package Index (PyPI)](https://pypi.org/project/connectomemapper/),
and the container images are available from [DockerHub](https://hub.docker.com/r/sebastientourbier/connectomemapper-bidsapp)
and [Sylabs Cloud](https://cloud.sylabs.io/library/connectomicslab/default/connectomemapper-bidsapp).

# Statement of Need

The field of Magnetic Resonance Imaging (MRI) Connectomics has rapidly expanded since its advent
Expand Down Expand Up @@ -126,9 +148,8 @@ Despite the recent emergence of electroencephalography (EEG) connectomics and th
exists to date.
Initiated during OHBM BrainHack 2020 ([https://github.com/ohbm/hackathon2020/issues/214](https://github.com/ohbm/hackathon2020/issues/214)),
CMP3 is being extended to EEG.
This manuscript summarizes CMP3 along with different design characteristics and principles.

# Summary
# Design and functionality overview

## A flexible and interoperable workflow for multi-modal human connectome mapping

Expand Down Expand Up @@ -215,35 +236,6 @@ All connectome files employ a common naming convention, based on the current
for more details).
\label{fig:parc}](Lausanne2018_parcellation_diagram.png)

## Outputs ready to be reused in the BIDS ecosystem

CMP3 outputs follow the BIDS Derivatives specifications wherever possible,
which facilitates the sharing of the derivatives in the BIDS App ecosystem,
and allows the user to easily retrieve any of the files generated by CMP3
with tools of the BIDS ecosystem such as pybids [@Yarkoni:2019].
It introduces a new BIDS entity ``atlas-<atlas_label>`` (See [proposal](https://github.com/bids-standard/bids-specification/pull/997))
that is used in combination with the ``res-<atlas_scale>`` entity to distinguish imaging and network data derived
from different parcellation atlases and scales (\autoref{fig:parc}).
While the BIDS-Derivatives extension to organize network data
(See [BEP017](https://docs.google.com/document/d/1ugBdUF6dhElXdj3u9vw0iWjE6f_Bibsro3ah7sRV0GA/edit#heading=h.mqkmyp254xh6))
is being developed, in which we are actively participating, structural and functional connectome files
derived from the different imaging modalities are saved in multiple formats following the convention shown in \autoref{fig:parc}.
All connectomes are saved by default as graph edge lists in ``.tsv`` files, that can
be directly analyzed using \href{https://networkx.org/documentation/stable/tutorial.html}{NetworkX} [@Hagberg:2008],
a Python library which offers many algorithms and tools to explore graphs and compute local and global network properties.
Connectivity matrices can be exported to MATLAB as MAT-files can be fed to the
\href{www.brain-connectivity-toolbox.net}{Brain Connectivity Toolbox} [@Rubinov:2010], which is a powerful
toolbox containing a large selection of network measures for the characterization of brain
connectivity datasets.
Finally, connectomes can be saved in GraphML format to interface with a lot of general purpose
software packages for graph analysis such as \href{www.cytoscape.org}{Cytoscape} [@Shannon:2003] [@Gustavsen:2019]
or \href{www.gephi.org}{Gephi} [@Bastian:2009].
Structuring outputs as BIDS Derivatives and saving them in a range of file formats
thus has a lot of advantages.
Not only does it ensure that the connectome files can be opened by the most popular
software packages used in this field to perform complex network analyses, but it
also eases the reuse of all outputs in the BIDS ecosystem.

## A focus on accessibility and versatility

CMP3 takes advantage of the Traits/TraitsUI framework
Expand Down Expand Up @@ -275,11 +267,40 @@ It offers them the possibility to tune and save all the parameters in configurat
can then be employed for running the BIDS App either with the Docker or Singularity software container engine directly,
or with the two [lightweight Docker and Singularity wrappers](https://connectome-mapper-3.readthedocs.io/en/latest/usage.html#with-the-wrappers).

## Outputs ready to be reused in the BIDS ecosystem

CMP3 outputs follow the BIDS Derivatives specifications wherever possible,
which facilitates the sharing of the derivatives in the BIDS App ecosystem,
and allows the user to easily retrieve any of the files generated by CMP3
with tools of the BIDS ecosystem such as pybids [@Yarkoni:2019].
It introduces a new BIDS entity ``atlas-<atlas_label>`` (See [proposal](https://github.com/bids-standard/bids-specification/pull/997))
that is used in combination with the ``res-<atlas_scale>`` entity to distinguish imaging and network data derived
from different parcellation atlases and scales (\autoref{fig:parc}).
While the BIDS-Derivatives extension to organize network data
(See [BEP017](https://docs.google.com/document/d/1ugBdUF6dhElXdj3u9vw0iWjE6f_Bibsro3ah7sRV0GA/edit#heading=h.mqkmyp254xh6))
is being developed, in which we are actively participating, structural and functional connectome files
derived from the different imaging modalities are saved in multiple formats following the convention shown in \autoref{fig:parc}.
All connectomes are saved by default as graph edge lists in ``.tsv`` files, that can
be directly analyzed using \href{https://networkx.org/documentation/stable/tutorial.html}{NetworkX} [@Hagberg:2008],
a Python library which offers many algorithms and tools to explore graphs and compute local and global network properties.
Connectivity matrices can be exported to MATLAB as MAT-files can be fed to the
\href{www.brain-connectivity-toolbox.net}{Brain Connectivity Toolbox} [@Rubinov:2010], which is a powerful
toolbox containing a large selection of network measures for the characterization of brain
connectivity datasets.
Finally, connectomes can be saved in GraphML format to interface with a lot of general purpose
software packages for graph analysis such as \href{www.cytoscape.org}{Cytoscape} [@Shannon:2003] [@Gustavsen:2019]
or \href{www.gephi.org}{Gephi} [@Bastian:2009].
Structuring outputs as BIDS Derivatives and saving them in a range of file formats
thus has a lot of advantages.
Not only does it ensure that the connectome files can be opened by the most popular
software packages used in this field to perform complex network analyses, but it
also eases the reuse of all outputs in the BIDS ecosystem.

## Developed with open science in mind

CMP3 is published under the terms of the open source 3-Clause Berkeley Software
Distribution (3-Clause BSD) license, which allows unlimited modification, redistribution
and commercial use in source and binary forms, as long as the copyright notice is retained and the
and commercial use in source and binary forms, as long as the copyright notice is retained, and the
license's disclaimers of warranty are maintained.
The source code for CMP3 is hosted at
[https://github.com/connectomicslab/connectomemapper3](https://github.com/connectomicslab/connectomemapper3),
Expand All @@ -304,7 +325,7 @@ More details about CMP3, the different processing steps and generated outputs to
the documentation ([connectome-mapper-3.readthedocs.io](connectome-mapper-3.readthedocs.io))
that is kept up to date with the current release and can be retrieved for older versions.

# Mention
# Community impact

CMP3 has been successfully employed in a number of methodological
[@Zheng2020GeometricConnectomeb] [@GlombNeuro:2020] [@GlombNet:2020] [@AkselrodHBM:2021]
Expand Down