Hemodynamically informed parcellation of cerebral FMRI data

Aina Frau-Pascual 1, 2 Thomas Vincent 1 Florence Forbes 1 Philippe Ciuciu 3, 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : Standard detection of evoked brain activity in functional MRI (fMRI) relies on a fixed and known shape of the impulse response of the neurovascular coupling, namely the hemodynamic response function (HRF). To cope with this issue, the joint detection-estimation (JDE) framework has been proposed. This formalism enables to estimate a HRF per region but for doing so, it assumes a prior brain partition (or parcellation) regarding hemodynamic territories. This partition has to be accurate enough to recover accurate HRF shapes but has also to overcome the detection-estimation issue: the lack of hemodynamics information in the non-active positions. An hemodynamically-based parcellation method is proposed, consisting first of a feature extraction step, followed by a Gaussian Mixture-based parcellation, which considers the injection of the activation levels in the parcellation process, in order to overcome the detection-estimation issue and find the underlying hemodynamics.
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Aina Frau-Pascual, Thomas Vincent, Florence Forbes, Philippe Ciuciu. Hemodynamically informed parcellation of cerebral FMRI data. ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2014, Florence, Italy. pp.2079-2083, ⟨10.1109/ICASSP.2014.6853965⟩. ⟨hal-01100186v2⟩

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