Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework

Lotfi Chaari 1 Florence Forbes 2 Thomas Vincent 2 Philippe Ciuciu 3
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
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 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 demonstrate the JPDE performance over standard detection estimation schemes and suggest it as a new brain exploration tool.
Mots-clés : fMRI VEM JDE parcellation
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Lotfi Chaari, Florence Forbes, Thomas Vincent, Philippe Ciuciu. Hemodynamic-informed parcellation of fMRI data in a Joint Detection Estimation framework. MICCAI 2012 - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2012, Nice, France. pp.180-188, ⟨10.1007/978-3-642-33454-2_23⟩. ⟨hal-00859388⟩

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