TY - JOUR
T1 - Clinica
T2 - An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies
AU - Routier, Alexandre
AU - Burgos, Ninon
AU - Díaz, Mauricio
AU - Bacci, Michael
AU - Bottani, Simona
AU - El-Rifai, Omar
AU - Fontanella, Sabrina
AU - Gori, Pietro
AU - Guillon, Jérémy
AU - Guyot, Alexis
AU - Hassanaly, Ravi
AU - Jacquemont, Thomas
AU - Lu, Pascal
AU - Marcoux, Arnaud
AU - Moreau, Tristan
AU - Samper-González, Jorge
AU - Teichmann, Marc
AU - Thibeau-Sutre, Elina
AU - Vaillant, Ghislain
AU - Wen, Junhao
AU - Wild, Adam
AU - Habert, Marie Odile
AU - Durrleman, Stanley
AU - Colliot, Olivier
N1 - Publisher Copyright:
© Copyright © 2021 Routier, Burgos, Díaz, Bacci, Bottani, El-Rifai, Fontanella, Gori, Guillon, Guyot, Hassanaly, Jacquemont, Lu, Marcoux, Moreau, Samper-González, Teichmann, Thibeau-Sutre, Vaillant, Wen, Wild, Habert, Durrleman and Colliot.
PY - 2021/8/13
Y1 - 2021/8/13
N2 - We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.
AB - We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.
KW - data processing and analysis
KW - machine learning
KW - multimodal neuroimaging data
KW - neuroimaging
KW - pipeline
KW - software
U2 - 10.3389/fninf.2021.689675
DO - 10.3389/fninf.2021.689675
M3 - Article
AN - SCOPUS:85114337512
SN - 1662-5196
VL - 15
JO - Frontiers in Neuroinformatics
JF - Frontiers in Neuroinformatics
M1 - 689675
ER -