dc.contributor.author | Uruñuela, Eneko | |
dc.contributor.author | Gonzalez-Castillo, Javier | |
dc.contributor.author | Zheng, Charles | |
dc.contributor.author | Bandettini, Peter | |
dc.contributor.author | Caballero-Gaudes, César | |
dc.date | 2024-11-07 | |
dc.date.accessioned | 2024-07-01T11:52:29Z | |
dc.date.available | 2024-07-01T11:52:29Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Eneko Uruñuela, Javier Gonzalez-Castillo, Charles Zheng, Peter Bandettini, César Caballero-Gaudes, Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection, Medical Image Analysis, Volume 91, 2024, 103010, ISSN 1361-8415, https://doi.org/10.1016/j.media.2023.103010 | es_ES |
dc.identifier.citation | Medical Image Analysis | |
dc.identifier.issn | 1361-8415 | |
dc.identifier.uri | http://hdl.handle.net/10810/68723 | |
dc.description | Available online 7 November 2023 | es_ES |
dc.description.abstract | Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigmfree analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: (1) their efficacy strongly depends on the appropriate selection of regularization parameters, (2) being univariate, they do not take advantage of the information present across the brain, and (3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce multivariate sparse paradigm free mapping (Mv-SPFM), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, Mv-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronalrelated BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, we present a formulation tailored for multi-echo fMRI acquisitions (MvME-SPFM), which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with the echo time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation | es_ES |
dc.description.sponsorship | This research was funded by the Spanish Ministry of Economy and
Competitiveness (RYC-2017-21845), the Basque Government, Spain
(BERC 2018–2021, PIB_2019_104, PRE_2020_2_0227), and the Spanish
Ministry of Science, Innovation and Universities (PID2019-105520GB-
100). This research was also possible thanks to the support of the
National Institute of Mental Health Intramural Research Program (ZIAMH002783,
ZICMH002968). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | ELSEVIER | 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/AEI/PID2019-105520GB-100 | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/PRE_2020_2_0227 | es_ES |
dc.relation | info:eu-repo/grantAgreement/GV/PIB_2019_104 | es_ES |
dc.rights | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.subject | Multi-echo fMRI | es_ES |
dc.subject | Hemodynamic deconvolution | es_ES |
dc.subject | Inverse problems | es_ES |
dc.subject | Stability selection | es_ES |
dc.title | Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2023 Published by Elsevier B.V. | es_ES |
dc.relation.publisherversion | https://www.sciencedirect.com/journal/medical-image-analysis | es_ES |
dc.identifier.doi | 10.1016/j.media.2023.103010 | |