Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

Abstract

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.

Unravelling the impact of frontal lobe impairment for social dysfunction in myotonic dystrophy type 1

“…Analyses were performed using the T 1 -volume pipeline of the Clinica software platform ( www.clinica.run ) developed by the ARAMIS Lab ( www.aramislab.fr ). 44 This pipeline is a wrapper of different tools of the Statistical Parametric Mapping Software SPM12, London, UK http://www.fil.ion.ucl.ac.uk/spm/ ). 45 …”

“…Analyses were performed using the T 1 -freesurfer pipeline of Clinica. 44 This pipeline is a wrapper of different tools of the FreeSurfer software ( http://surfer.nmr.mgh.harvard.edu/ ). 47 This processing included segmentation of subcortical structures, extraction of cortical surfaces, cortical thickness (CT) estimation, spatial normalization onto the FreeSurfer surface template (FsAverage) and parcellation of cortical regions.…”

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

“…All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into Clinica (Routier et al, 2018) , an open-source software platform that we developed to 3 process data from neuroimaging studies, and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL . The tagged version v.0.0.1 corresponds to the version of the code used to obtain the results of the paper.…”

Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks

“…Details about the pre-processing steps are shown in the Supplementary Materials . In brief, after pre-processing the raw diffusion weighted imaging data to correct for motion, eddy currents and echo planar imaging distortions via the pipeline in Clinica (Routier et al, 2021 ), structural connectivity matrices were obtained by performing probabilistic tractography using the (Hernández et al, 2013 ) and (Hernandez-Fernandez et al, 2019 ) tool from FSL (Jenkinson et al, 2012 ). Since the raw connectivity matrix is not symmetric, the average of the upper and lower triangular was computed and was further log-transformed and standardized to ensure that the values follow a standard normal distribution (which will aid downstream modeling tasks).…”