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Subject-level Joint Parcellation-Detection-Estimation in fMRI

hal.structure.identifierEcole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications [ENSEEIHT]
dc.contributor.authorCHAARI, Lotfi
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorBADILLO, Solveig
hal.structure.identifierModelling and Inference of Complex and Structured Stochastic Systems [MISTIS ]
dc.contributor.authorVINCENT, Thomas
hal.structure.identifierNeuroimagerie cognitive - Psychologie cognitive expérimentale [UNICOG-U992]
hal.structure.identifierService NEUROSPIN [NEUROSPIN]
dc.contributor.authorDEHAENE-LAMBERTZ, Ghislaine
hal.structure.identifierModelling and Inference of Complex and Structured Stochastic Systems [MISTIS ]
dc.contributor.authorFORBES, Florence
hal.structure.identifierModelling brain structure, function and variability based on high-field MRI data [PARIETAL]
hal.structure.identifierService NEUROSPIN [NEUROSPIN]
dc.contributor.authorCIUCIU, Philippe
dc.date.accessioned2024-04-04T03:16:19Z
dc.date.available2024-04-04T03:16:19Z
dc.date.created2016-01-06
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194207
dc.description.abstractEnBrain parcellation is one of the most important issues in functional MRI (fMRI) data analysis. This parcellation allows establishing homogeneous territories that share the same functional properties. This paper presents a model-based approach to perform a subject-level parcellation into hemodynamic territories with similar hemodynamic features which are known to vary between brain regions. We specifically investigate the use of the Joint Parcellation-Detection-Estimation (JPDE) model initially proposed in [1] to separate brain regions that match different hemodynamic response function (HRF) profiles. A hierarchical Bayesian model is built and a variational expectation maximiza-tion (VEM) algorithm is deployed to perform inference. A more complete version of the JPDE model is detailed. Validation on synthetic data shows the robustness of this model to varying signal-to-noise ratio (SNR) as well as to different initializations. Our results also demonstrate that good parcellation performance is achieved even though the parcels do not involve the same amount of activation. On real fMRI data acquired in children during a language paradigm, we retrieved a parcellation along the superior temporal sulcus of the left hemisphere that matches the gradient of activation dynamics already reported in the literature.
dc.language.isoen
dc.subject.enfunctional MRI
dc.subject.enhemodynamics
dc.title.enSubject-level Joint Parcellation-Detection-Estimation in fMRI
dc.typeDocument de travail - Pré-publication
dc.subject.halSciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie
dc.subject.halInformatique [cs]/Imagerie médicale
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halSciences du Vivant [q-bio]/Neurosciences [q-bio.NC]/Sciences cognitives
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-01255465
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01255465v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=CHAARI,%20Lotfi&BADILLO,%20Solveig&VINCENT,%20Thomas&DEHAENE-LAMBERTZ,%20Ghislaine&FORBES,%20Florence&rft.genre=preprint


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