Activation likelihood estimation meta-analysis revisited, NeuroImage, vol.59, issue.3, pp.2349-2361, 2012. ,
DOI : 10.1016/j.neuroimage.2011.09.017
Functional grouping and cortical???subcortical interactions in emotion: A meta-analysis of neuroimaging studies, NeuroImage, vol.42, issue.2, pp.998-1031, 2008. ,
DOI : 10.1016/j.neuroimage.2008.03.059
A parametric approach to voxel-based meta-analysis, NeuroImage, vol.46, issue.1, pp.115-122, 2009. ,
DOI : 10.1016/j.neuroimage.2009.01.031
Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies, NeuroImage, vol.45, issue.3, pp.810-823, 2009. ,
DOI : 10.1016/j.neuroimage.2008.12.039
org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain, Front. Neuroinform, vol.9, issue.8, 2015. ,
URL : https://hal.archives-ouvertes.fr/inserm-01134573
Data sharing in neuroimaging research, Frontiers in Neuroinformatics, vol.6, pp.9-9, 2012. ,
DOI : 10.3389/fninf.2012.00009
URL : http://doi.org/10.3389/fninf.2012.00009
Guidelines for reporting an fMRI study, NeuroImage, vol.40, issue.2, pp.409-414, 2008. ,
DOI : 10.1016/j.neuroimage.2007.11.048
URL : http://doi.org/10.1016/j.neuroimage.2007.11.048
A checklist for fMRI acquisition methods reporting in the literature. The Winnower https, 2015. ,
Best Practices in Data Analysis and Sharing in Neuroimaging using MRI, p.54262, 1101. ,
DOI : 10.1101/054262
Better living through transparency: Improving the reproducibility of fMRI results through comprehensive methods reporting, Cognitive, Affective, & Behavioral Neuroscience, vol.8, issue.3, pp.660-666, 2013. ,
DOI : 10.3758/s13415-013-0188-0
Power failure: why small sample size undermines the reliability of neuroscience, Nature Reviews Neuroscience, vol.80, issue.5, pp.365-376, 2013. ,
DOI : 10.1038/nrn3475
BrainMap: The Social Evolution of a Human Brain Mapping Database, Neuroinformatics, vol.3, issue.1, pp.65-78, 2005. ,
DOI : 10.1385/NI:3:1:065
Large-scale automated synthesis of human functional neuroimaging data, Nature Methods, vol.98, issue.8, pp.665-670, 2011. ,
DOI : 10.1073/pnas.1102693108
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146590
Available at http://neurosynth.org/. Accessed on 15th, 2016. ,
Statistical parametric mapping: the analysis of functional brain images: the analysis of functional brain images, 2011. ,
SPM?Statistical Parametric Mapping, Accessed on 15, 2016. ,
FSL, NeuroImage, vol.62, issue.2, pp.782-790, 2012. ,
DOI : 10.1016/j.neuroimage.2011.09.015
URL : https://hal.archives-ouvertes.fr/inserm-01149484
Available at http://fsl.fmrib.ox.ac.uk/fsl. Accessed on 15th, 2016. ,
AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages, Computers and Biomedical Research, vol.29, issue.3, pp.162-173, 1996. ,
DOI : 10.1006/cbmr.1996.0014
Available at http://afni.nimh.nih.gov/. Accessed on, 2005. ,
SPM plot units, 31 Neuroimaging Statistics Tips & Tools, Accessed on 15, 2016. ,
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python, Frontiers in Neuroinformatics, vol.5, p.13, 2011. ,
DOI : 10.3389/fninf.2011.00013
URL : http://doi.org/10.3389/fninf.2011.00013
The LONI Pipeline Processing Environment, NeuroImage, vol.19, issue.3, pp.1033-1048, 2003. ,
DOI : 10.1016/S1053-8119(03)00185-X
CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research, Frontiers in Neuroinformatics, vol.8, p.54, 2014. ,
DOI : 10.1007/S10723-010-9146-Z
URL : https://hal.archives-ouvertes.fr/hal-01016487
XCEDE: An Extensible Schema for Biomedical Data, Neuroinformatics, vol.8, issue.4, pp.19-32, 2012. ,
DOI : 10.1007/s12021-011-9119-9
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836560
Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid, Frontiers in Neuroinformatics, vol.3, p.30, 2009. ,
DOI : 10.3389/neuro.11.030.2009
URL : http://doi.org/10.3389/neuro.11.030.2009
A General XML Schema and SPM Toolbox for Storage of Neuro-Imaging Results and Anatomical Labels, Neuroinformatics, vol.4, issue.2, pp.199-211, 2006. ,
DOI : 10.1385/NI:4:2:199
Informatics Research Network (BIRN) Available at http://www.nitrc.org/frs/shownotes.php? release_id=551. Accessed on 15th, 2016. ,
Towards structured sharing of raw and derived neuroimaging data across existing resources, NeuroImage, vol.82, pp.647-661, 2013. ,
DOI : 10.1016/j.neuroimage.2013.05.094
39. glatard/cbrain-plugins-nidm. GitHub Available at https://github.com/glatard/cbrain-plugins-nidm, nidash.org. Accessed on 15th, 2016. ,
Toward open sharing of task-based fMRI data: the OpenfMRI project, Frontiers in Neuroinformatics, vol.7, pp.12-12, 2013. ,
DOI : 10.3389/fninf.2013.00012
Accessed on 15th incf-nidash/nidmresults-paper. GitHub Available at https://github.com/incf-nidash/nidmresults-paper, Accessed on 15, 2016. ,
Making big data open: data sharing in neuroimaging, Nature Neuroscience, vol.6, issue.11, pp.1510-1517, 2014. ,
DOI : 10.1016/j.neuroimage.2008.04.186
The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience, Frontiers in Neuroinformatics, vol.5, pp.17-17, 2011. ,
DOI : 10.3389/fninf.2011.00017
Available at http://www.cognitiveatlas.org, 2011. ,
The Cognitive Paradigm Ontology: Design and Application, Neuroinformatics, vol.7, issue.11, pp.57-66, 2012. ,
DOI : 10.1007/s12021-011-9126-x
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3682219
Available at http://www.cogpo.org/. Accessed on 15th, 2016. ,
Available at https://github.com/incf-nidash/ nidm/pull/233. Accessed on, 2014. ,
The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration, Nature Biotechnology, vol.10, issue.11, pp.1251-1255, 2007. ,
DOI : 10.1038/nbt1346
The wonderweb library of fundational ontologies and the dolce ontology. wonderweb deliverable d18, final report (vr. 1.0 The WonderWeb Library of Fundational Ontologies and the DOLCE ontology, pp.31-43, 2003. ,
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Scientific Data, vol.8, p.160044, 2016. ,
DOI : 10.3389/fnins.2013.00009
URL : https://hal.archives-ouvertes.fr/inserm-01345616
Towards an ontology for sharing medical images and regions of interest in neuroimaging, Journal of Biomedical Informatics, vol.41, issue.5, pp.766-778, 2008. ,
DOI : 10.1016/j.jbi.2008.03.002
URL : https://hal.archives-ouvertes.fr/inserm-00344293
Building a Web of Linked Data Resources to Advance Neuroscience Research, p.53934, 1101. ,
Reproducibility of neuroimaging analyses across operating systems, Frontiers in Neuroinformatics, vol.9, p.12, 2015. ,
DOI : 10.3389/fninf.2015.00012
URL : https://hal.archives-ouvertes.fr/hal-01207394
The secret lives of experiments: Methods reporting in the fMRI literature, NeuroImage, vol.63, issue.1, pp.289-300, 2012. ,
DOI : 10.1016/j.neuroimage.2012.07.004
org: an online framework for neuroscience knowledge, Front. Neuroinform, vol.7, pp.18-18, 2013. ,
RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods, Neuron, vol.90, issue.3, pp.434-436, 2016. ,
DOI : 10.1016/j.neuron.2016.04.030
Available at http://www.semanticdesktop.org/ontologies, 2007. ,
FSL: New tools for functional and structural brain image analysis, NeuroImage, vol.13, issue.6, p.249, 2001. ,
DOI : 10.1016/S1053-8119(01)91592-7
General multilevel linear modeling for group analysis in FMRI, NeuroImage, vol.20, issue.2, pp.1052-1063, 2003. ,
DOI : 10.1016/S1053-8119(03)00435-X
Multilevel linear modelling for FMRI group analysis using Bayesian inference, NeuroImage, vol.21, issue.4, pp.1732-1747, 2004. ,
DOI : 10.1016/j.neuroimage.2003.12.023
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.337.1448
Available at https://codemirror.net. Data Citations, 2011. ,
K. was supported by the Function Biomedical Informatics Re-search Network (NIH 1 [U24 U24 RR021992]), the BIRN Coordinating Center (https://www.nitrc. org/projects, was partially supported by NIH grants]) and the Conte Center on Brain Programming in Adolescent Vulnerabilities [1P50MH096889-01A1]. G.C. and R.R. were supported by the NIMH and NINDS Intramural Research Programs (ZICMH002888) of the NIH/HHS, USA. K.J.G. was sponsored by the Laura and John Arnold Foundation. K.G.H. was supported by the Morphometry Biomedical Informatics Research Network (MBIRN, NIH U24 RR021382), the BIRN Coordinating Center (NIH U24 RR025736-01). S.D. and T.G. were supported by the Irving Ludmer Family Foundation and the Ludmer Centre for Neuroinformatics and Mental Health ,