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dc.contributor.authorCaballero-Gaudes, César
dc.contributor.authorMoia, Stefano
dc.contributor.authorPanwar, Puja
dc.contributor.authorBandettini, Peter A.
dc.contributor.authorGonzalez-Castillo, Javier
dc.date.accessioned2020-01-14T11:46:11Z
dc.date.available2020-01-14T11:46:11Z
dc.date.issued2019
dc.identifier.citationCésar Caballero-Gaudes, Stefano Moia, Puja Panwar, Peter A. Bandettini, Javier Gonzalez-Castillo, A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping, NeuroImage, Volume 202, 2019, 116081, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2019.116081.es_ES
dc.identifier.issn1053-8119
dc.identifier.urihttp://hdl.handle.net/10810/38274
dc.descriptionAvailable online 13 August 2019.es_ES
dc.description.abstractThis work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of ⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.es_ES
dc.description.sponsorshipWe thank Prof. Penny A. Gowland for helpful discussion regarding the contents of this manuscript, as well as the anonymous reviewers for their valuable comments and feedback. This research was possible thanks to the support of the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Fellowship (IJCI-2014-20821) and Ramon y Cajal Fellowship (RYC-2017-21845), the Spanish State Research Agency through the BCBL ”Severo Ochoa” excellence accreditation (SEV-2015-490), the Basque Government through the BERC 2018- 2021 program, the National Institute of Mental Health Intramural Research Program (NIH clinical protocol number NCT00001360, protocol ID 93-M-0170, Annual report ZIAMH002783-16), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713673, and a fellowship from La Caixa Foundation (ID 100010434) (fellowship code LCF/BQ/IN17/11620063). Portions of this study used the highperformance computational capabilities of the NIH High Performance Cluster (Biowulf) at the National Institutes Health, Bethesda, MD (http://hpc.nih.gov).es_ES
dc.language.isoenges_ES
dc.publisherNeuroImagees_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/IJCI-2014-20821es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/ RYC-2017-21845es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/SEV-2015-0490es_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/MC/713673es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectBOLD fMRIes_ES
dc.subjectMulti-echoes_ES
dc.subjectDeconvolutiones_ES
dc.subjectSingle-triales_ES
dc.titleA deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mappinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2019 Elsevier Inc. All rights reserved.es_ES
dc.relation.publisherversionhttps://www.sciencedirect.com/journal/neuroimagees_ES
dc.identifier.doi10.1016/j.neuroimage.2019.116081


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