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. 2018 Dec 10;12:94. doi: 10.3389/fninf.2018.00094

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Copyright © 2018 Marcoux, Burgos, Bertrand, Teichmann, Routier, Wen, Samper-González, Bottani, Durrleman, Habert, Colliot and for the Alzheimer's Disease Neuroimaging Initiative.

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.

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Diagram of the Clinica modules involved in the processing and analysis of the data presented in this paper. The raw dataset is first converted into a BIDS compliant format (e.g., using the adni-to-bids converter). The t1-freesurfer-cross-sectional pipeline (wrapping the recon-all command of FreeSurfer) provides the cortical surfaces and the parcellation necessary to run the proposed pet-surface pipeline. The PET uptake projected onto the cortical surface can be used to perform individual classification (machine learning module) or group comparison (statistics-surface pipeline). All these modules store their outputs in a ClinicA Processed Structure (CAPS) hierarchy. All the names in italics refer to actual command lines of Clinica.