Welcome to the UPF Digital Repository

Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity

Show simple item record

dc.contributor.author Pallarés, Vicente
dc.contributor.author Insabato, Andrea
dc.contributor.author Sanjuán, Ana
dc.contributor.author Kühn, Simone
dc.contributor.author Mantini, Dante
dc.contributor.author Deco, Gustavo
dc.contributor.author Gilson, Matthieu
dc.date.accessioned 2019-06-14T07:37:42Z
dc.date.available 2019-06-14T07:37:42Z
dc.date.issued 2018
dc.identifier.citation Pallarés V, Insabato A, Sanjuán A, Kühn S, Mantini D, Deco G, Gilson M. Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity. Neuroimage. 2018 Sep;178:238-54. DOI: 10.1016/j.neuroimage.2018.04.070
dc.identifier.issn 1053-8119
dc.identifier.uri http://hdl.handle.net/10230/41752
dc.description.abstract The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities (e.g., subjects and conditions such as tasks performed by them). In particular, condition-specific signatures should not be contaminated by subjectrelated information, since they aim to generalize over subjects. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic.
dc.description.sponsorship This work was supported by the European Union's Horizon 2020 research and innovation programme/Human Brain Project (grant FP7-FET-ICT-604102 to MG and GD; H2020-720270 HBP SGA1 to GD) and the Marie Sklodowska-Curie Action (grant H2020-MSCA-656547 to MG). A.I. is supported by the Spanish Ministry of Economy and Competitiveness Flag-ERA APCIN project CHAMPMouse (PCIN-2015-127). GD also acknowledges funding from the ERC Advanced Grant DYSTRUCTURE (#295129), the Spanish Research Project PSI2016-75688-P and the Catalan Research Group Support 2017 SGR 1545. DM was supported by the KU Leuven Special Research Fund (grant C16/15/070). SK has been funded by a Heisenberg grant from the German Science Foundation (DFG KU 3322/1-1), the European Union (ERC-2016-StG-Self-Control-677804) and a Fellowship from the Jacobs Foundation (JRF 2016-2018). This work has in part been funded by the German Science Foundation (SFB 936/C7).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Neuroimage. 2018 Sep;178:238-54.
dc.rights © 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
dc.title Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://dx.doi.org/10.1016/j.neuroimage.2018.04.070
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/720270
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/FET-ICT-604102
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/656547
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2016-75688-P
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/677804
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account

Statistics

Compliant to Partaking