Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework
CHAARI, Lotfi
Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications [ENSEEIHT]
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Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications [ENSEEIHT]
CHAARI, Lotfi
Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications [ENSEEIHT]
Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications [ENSEEIHT]
DEHAENE-LAMBERTZ, Ghislaine
Modelling brain structure, function and variability based on high-field MRI data [PARIETAL]
Modelling brain structure, function and variability based on high-field MRI data [PARIETAL]
CIUCIU, Philippe
Modelling brain structure, function and variability based on high-field MRI data [PARIETAL]
< Reduce
Modelling brain structure, function and variability based on high-field MRI data [PARIETAL]
Language
en
Document de travail - Pré-publication
English Abstract
Identifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by ...Read more >
Identifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called Hemodynamic Response Function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contributions commit to pre-specified spatial supports for the hemodynamic territories by defining these supports either as individual voxels or a priori fixed brain parcels. In this paper, we introduce a Joint Parcellation-Detection-Estimation (JPDE) procedure that incorporates an adaptive parcel identification step based upon local hemodynamic properties. Efficient inference of both evoked activity, HRF shapes and supports is then achieved using variational approximations. Validation on synthetic and real fMRI data demonstrates the JPDE performance over standard detection estimation schemes and suggests it as a new brain exploration tool.Read less <
English Keywords
hemodynamic
fMRI
parcellation
brain
Pyhrf
joint detection-estimation (JDE)
Origin
Hal imported