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

      research-article
      1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 2 , 3 , 4 , 5 , 6 , 8 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , 9 , 10 , 11 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 2 , 3 , 4 , 5 , 6 , *
      Frontiers in Neuroinformatics
      Frontiers Media S.A.
      neuroimaging, software, pipeline, data processing and analysis, machine learning, multimodal neuroimaging data

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          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.

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          Most cited references66

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          An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

          In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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            Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

            An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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              FSL.

              FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                13 August 2021
                2021
                : 15
                : 689675
                Affiliations
                [1] 1Inria, Aramis Project-Team , Paris, France
                [2] 2Sorbonne Université , Paris, France
                [3] 3Institut du Cerveau – Paris Brain Institute – ICM , Paris, France
                [4] 4Inserm , Paris, France
                [5] 5CNRS , Paris, France
                [6] 6AP-HP, Hôpital de la Pitié-Salpêtrière , Paris, France
                [7] 7Inria, Service d'Expérimentation et de Développement , Paris, France
                [8] 8Department of Neurology, Institute for Memory and Alzheimer's Disease, Pitié-Salpêtrière Hospital, AP-HP , Paris, France
                [9] 9Sorbonne Université, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB) , Paris, France
                [10] 10AP-HP, Hôpital Pitié-Salpêtrière, Médecine Nucléaire , Paris, France
                [11] 11Centre d'Acquisition et Traitement des Images , Paris, France
                Author notes

                Edited by: Antonio Fernández-Caballero, University of Castilla-La Mancha, Spain

                Reviewed by: Hugo Alexandre Ferreira, University of Lisbon, Portugal; Nathan Churchill, St. Michael's Hospital, Canada

                *Correspondence: Olivier Colliot olivier.colliot@ 123456sorbonne-universite.fr
                Article
                10.3389/fninf.2021.689675
                8415107
                34483871
                979170b3-b069-4845-95c1-8eee64b9c9d0
                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.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 April 2021
                : 19 July 2021
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 67, Pages: 16, Words: 12125
                Categories
                Neuroscience
                Original Research

                Neurosciences
                neuroimaging,software,pipeline,data processing and analysis,machine learning,multimodal neuroimaging data

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