Print Close

Connectome Mapper 3: a software pipeline for multi-scale connectome mapping of multimodal MR data

Presented During:

Software Demonstrations

Poster No:

1892

Submission Type:

Abstract Submission

Authors:

Sebastien Tourbier¹, Yasser Alemàn-Gòmez¹, Emeline Mullier¹, Alessandra Griffa², Meritxell Bach Cuadra³, Patric Hagmann¹

Institutions:

¹Connectomics Lab, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Vaud, ²Medical Image Processing Lab (MIPLAB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Vaud, ³Centre D'Imagerie BioMédicale (CIBM), University of Lausanne (UNIL), Lausanne, Vaud

First Author:

Sebastien Tourbier
Connectomics Lab, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL)
Lausanne, Vaud

Co-Author(s):

Yasser Alemàn-Gòmez
Connectomics Lab, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL)
Lausanne, Vaud

Emeline Mullier
Connectomics Lab, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL)
Lausanne, Vaud

Alessandra Griffa
Medical Image Processing Lab (MIPLAB), Ecole Polytechnique Fédérale de Lausanne (EPFL)
Lausanne, Vaud

Meritxell Bach Cuadra
Centre D'Imagerie BioMédicale (CIBM), University of Lausanne (UNIL)
Lausanne, Vaud

Patric Hagmann
Connectomics Lab, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL)
Lausanne, Vaud

Introduction:

Connectome Mapper (CMP) is an open-source software pipeline with a Graphical User Interface (GUI) written in Python. It was historically designed to help researchers in all the organization and processing of raw structural MRI (sMRI) and diffusion MRI (dMRI) data to obtain a hierarchical multi-scale brain parcellation (Cammoun 2012) and its corresponding structural connectomes (Daducci 2012). While the first two versions were designed with ease-of-use, modularity, configurability, re-executability and transparency in mind, they have shown to be limited in terms of interoperability, reusability, portability, and reproducibility. Following recent advances in the standardization of neuroimaging data organization (Gorgolewski 2016) and processing (Gorgolewski 2017), we present the third version of CMP (CMP3). It has massively evolved in terms of the underlying code, the processing tools and the scope of functionality provided, being extended to the processing of resting-state fMRI (rfMRI) data.

Methods:

CMP3 is developed following modern software practices and implements a full subject-level analysis workflow necessary to estimate a structural brain parcellation at five different macroscopic scales from sMRI with corresponding multivariate and multi-scale structural and functional connectomes from dMRI and rfMRI respectively. The workflow is encapsulated in a Docker container image and released as a BIDS App (Gorgolewski 2017), a framework which promotes interoperability, reusability, portability, and reproducibility. The processing workflow of CMP3 takes as input BIDS datasets (Gorgolewski 2016) and has a modular architecture composed of processing pipelines, each dedicated to one modality and composed of different stages that interface with state-of-the-art tools (Figure 1). Each pipeline is described by a configuration file and represented in Nipype (Gorgolewski 2011), which facilitates its execution and the recordings of data and workflow provenance. This allows CMP3 workflow to be dynamically built and configured depending on the availability of sMRI, dMRI, rfMRI data and the parameters set in the pipeline configuration files. Pipeline outputs are structured according to the BIDS derivatives RC1 specifications.

Results:

CMP3 is distributed under an open-source license, hosted on GitHub at https://github.com/connectomicslab/connectomemapper3 where issues and new feature requests are transparently discussed, and versions are released through continuous integration testing. For best user experience, CMP3 has a GUI that eases and supports all the steps involved in the configuration of the pipelines, the configuration of the BIDS App run and its execution, and the inspection of the stage outputs (Figure 2). While the configuration files can be easily created from the GUI, the execution of the BIDS App can also be scripted to guarantee a consistent and homogeneous processing on a collection of datasets at the same time. Adopting this procedure, CMP3 has been successfully run on the range of supported diffusion schemes using the HCP test-retest (reorganized according to BIDS) and four in-house datasets.

Conclusions:

The Connectome Mapper 3 provides a unique software pipeline solution for researchers to easily, reliably and transparently create a hierarchical multi-scale connectome representation of the structural and functional brain systems, on any dataset structured according to the BIDS standard. Thanks to its modular architecture, new pipelines and stages can be added with relatively little effort to account for additional imaging modalities and algorithms. At the time of writing, a new pipeline dedicated to the processing of EEG data is being implemented for instance. We believe that CMP3 provide a solid multi-modal and multi-scale framework for the investigation of the anatomo-functional organization of brain networks.

Neuroinformatics and Data Sharing:

Workflows ¹

Informatics Other ²

Keywords:

Data analysis

Data Registration

FUNCTIONAL MRI

Informatics

STRUCTURAL MRI

Sub-Cortical

Thalamus

Tractography

Workflows

Other - BIDS App, Connectome

^1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

Yes

Please indicate below if your study was a "resting state" or "task-activation” study.

Other

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Not applicable

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI

Structural MRI

Diffusion MRI

Provide references using author date format

Avants B.B. (2008), ‘Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain’, Med Image Anal., vol. 1, pp. 26-41
Cammoun L. (2012), ‘Mapping the human connectome at multiple scales with diffusion spectrum MRI’, J Neurosci Methods, vol. 203, no. 2, pp. 386-97
Cox R.W. (1996), ‘AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages’, Computers and Biomedical Research, vol. 29, pp. 162-173.
Daducci A. (2012), ‘The Connectome Mapper: an open-source processing pipeline to map connectomes with MRI’, PLoS ONE, vol. 7, no. 12, e48121
Fischl B. (2012), ‘FreeSurfer’, Neuroimage, vol. 62, no. 2, pp. 774-781
Garifallidis E. (2014), ‘Dipy, a library for the analysis of diffusion MRI data’, Front. Neuroinform., vol. 8, no. 8
Gorgolewski K.J. (2011), 'Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python', Front Neuroinform, vol. 5, pp. 13–13.
Gorgolewski K.J. (2016), 'The brain imaging data structure, a format for organizing and describing outputs of neuroimaging, Scientific Data, vol. 3, no. 160044
Gorgolewski K.J. (2017), 'BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods', PLoS computational biology vol. 13, no. 3, e1005209
Jenkinson B. (2012), ‘FSL’, Neuroimage. vol. 62, no. 2, pp. 782-90
Tournier J.-D. (2019), ‘MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation’, NeuroImage, vol. 202, pp. 116–37