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Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work
dc.contributor.author | Uruñuela, Eneko | |
dc.contributor.author | Bolton, Thomas A.W. | |
dc.contributor.author | Van De Ville, Dimitri | |
dc.contributor.author | Caballero-Gaudes, César | |
dc.date.accessioned | 2024-03-27T10:52:36Z | |
dc.date.available | 2024-03-27T10:52:36Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Aperture Neuro | |
dc.identifier.issn | 2957-3963 | |
dc.identifier.uri | http://hdl.handle.net/10810/66486 | |
dc.description | Published online 28 August 2023 | es_ES |
dc.description.abstract | Deconvolution of the hemodynamic response is an important step to access short timescales of brain activity recorded by functional magnetic resonance imaging (fMRI). Albeit conventional deconvolution algorithms have been around for a long time (e.g., Wiener deconvolution), recent state-of-the-art methods based on sparsity-pursuing regularization are attracting increasing interest to in- vestigate brain dynamics and connectivity with fMRI. This technical note revisits the main concepts underlying two main methods, paradigm free mapping and total activation, in the most accessible way. Despite their apparent differences in the formulation, these methods are theoretically equivalent as they represent the synthesis and analysis sides of the same problem, respectively. We demonstrate this equivalence in practice with their best-available implementations using both simulations, with different signal-to- noise ratios, and experimental fMRI data acquired during a motor task and resting state. We evaluate the parameter settings that lead to equivalent results and showcase the potential of these algorithms compared to other common approaches. This note is useful for practitioners interested in gaining a better understanding of state-of-the-art hemodynamic deconvolution and aims to answer questions that practitioners often have regarding the differences between the two methods. | es_ES |
dc.description.sponsorship | We thank Stefano Moia and Vicente Ferrer for data availability and Younes Farouj for valuable comments on the manuscript. This research was funded by the Spanish Ministry of Economy and Competitiveness (RYC-2017-21845), the Basque Government (BERC 2018-2021, PIB_2019_104, PRE_2020_2_0227), and the Spanish Ministry of Science, Innovation and Universities (PID2019-105520GB-100), and the Swiss National Science Foundation (grant 205321_163376). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | OHBM | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/RYC-2017-21845 | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/BERC2018-2021 | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/PIB_2019_104 | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/PRE_2020_2_0227 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/PID2019-105520GB-100 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | fMRI deconvolution | es_ES |
dc.subject | paradigm free mapping | es_ES |
dc.subject | temporal regularization | es_ES |
dc.subject | total activation | es_ES |
dc.title | Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits authors to copy and redistribute the material in any medium or format, remix, transform and build upon material, for any purpose, even commercially. | es_ES |
dc.relation.publisherversion | https://apertureneuro.org/ | es_ES |
dc.identifier.doi | 10.52294/001c.87574 |