Adopting the Brain Imaging Data Structure in the Connectome Mapper. Tourbier, S., Pizzolato, M., Carboni, M., Pascucci, D., Rubega, M., Vuillemoz, S., Plomp, G., Michel, C. M., Thiran, J., & Hagmann, P. In Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018, Champéry, 2018. abstract bibtex Connectome Mapper is an open-source software pipeline that has been developed since 2012 to help researchers through the tedious process of organizing, processing and analyzing diffusion MRI data to perform global brain connectivity analysis. At this time, there had been no standard tools to describe data and its organization on storage devices, despite initiatives such as the eXtensible Markup Language (XML)-based Clinical Experiment Data Exchange schema (XCEDE) or the openfMRI convention, which had been poorly adopted. This was mainly caused by the adoption of tools not trivial to be used for non-informatics experts, together with the lack of file format specifications (XCEDE), and by the lack of explicit support for a number of important data types such as diffusion MRI (openfMRI). Consequently, the Connectome Mapper adopted its own standard for description and organization of anatomical and diffusion MRI, which requires reorganization of open datasets and limits the inter-operability with other software. Last year, a standard, known as the Brain Imaging Data Structure (BIDS), has emerged and been increasingly used. BIDS gives specifications on data description based on simple text-based file formats, a simple and comprehensive organization, and the use of NIfTI for images. Such standard is indeed essential to guarantee data understanding for people not implicated in the acquisition, easy data sharing and re-using within or between the labs, and application of automated analysis workflows for enhanced reproducibility and efficiency. In this work, we present a new version of the Connectome Mapper that adopts BIDS as standard for datasets.
@inproceedings{tourbier2018abim,
address = {Champéry},
title = {Adopting the {Brain} {Imaging} {Data} {Structure} in the Connectome Mapper},
abstract = {Connectome Mapper is an open-source software pipeline that has been developed since 2012 to help researchers through the tedious process of organizing, processing and analyzing diffusion MRI data to perform global brain connectivity analysis. At this time, there had been no standard tools to describe data and its organization on storage devices, despite initiatives such as the eXtensible Markup Language (XML)-based Clinical Experiment Data Exchange schema (XCEDE) or the openfMRI convention, which had been poorly adopted. This was mainly caused by the adoption of tools not trivial to be used for non-informatics experts, together with the lack of file format specifications (XCEDE), and by the lack of explicit support for a number of important data types such as diffusion MRI (openfMRI). Consequently, the Connectome Mapper adopted its own standard for description and organization of anatomical and diffusion MRI, which requires reorganization of open datasets and limits the inter-operability with other software. Last year, a standard, known as the Brain Imaging Data Structure (BIDS), has emerged and been increasingly used. BIDS gives specifications on data description based on simple text-based file formats, a simple and comprehensive organization, and the use of NIfTI for images. Such standard is indeed essential to guarantee data understanding for people not implicated in the acquisition, easy data sharing and re-using within or between the labs, and application of automated analysis workflows for enhanced reproducibility and efficiency. In this work, we present a new version of the Connectome Mapper that adopts BIDS as standard for datasets.},
booktitle = {Alpine Brain Imaging Meeting, Champéry, Switzerland, January 7-11, 2018},
author = {Tourbier, Sebastien and Pizzolato, Marco and Carboni, Margherita and Pascucci, David and Rubega, Maria and Vuillemoz, Serge and Plomp, Gijs and Michel, Christoph M. and Thiran, J.-P. and Hagmann, Patric},
year = {2018}
}
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