dc.contributor.author | Caballero Gaudes, César | |
dc.contributor.author | Moia, Stefano | |
dc.contributor.author | Panwar, Puja | |
dc.contributor.author | Bandettini, Peter A. | |
dc.contributor.author | Gonzalez-Castillo, Javier | |
dc.date.accessioned | 2020-01-14T11:46:11Z | |
dc.date.available | 2020-01-14T11:46:11Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | César 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.issn | 1053-8119 | |
dc.identifier.uri | http://hdl.handle.net/10810/38274 | |
dc.description | Available online 13 August 2019. | es_ES |
dc.description.abstract | This 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.sponsorship | We 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.iso | eng | es_ES |
dc.publisher | NeuroImage | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/IJCI-2014-20821 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/ RYC-2017-21845 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/SEV-2015-0490 | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/MC/713673 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | BOLD fMRI | es_ES |
dc.subject | Multi-echo | es_ES |
dc.subject | Deconvolution | es_ES |
dc.subject | Single-trial | es_ES |
dc.title | A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2019 Elsevier Inc. All rights reserved. | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/journal/neuroimage | es_ES |
dc.identifier.doi | 10.1016/j.neuroimage.2019.116081 | |