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Physiologically informed Bayesian analysis of ASL fMRI data

Florence Forbes 1 Aina Frau-Pascual 2, 1 Thomas Vincent 1, 3 Jennifer Sloboda 1 Philippe Ciuciu 2 
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
Abstract : Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI) data provides a quantitative measure of blood perfusion, that can be correlated to neuronal activation. In contrast to BOLD measure, it is a direct measure of cerebral blood flow. However, ASL data has a lower SNR and resolution so that the recovery of the perfusion response of interest suffers from the contamination by a stronger hemodynamic component in the ASL signal. In this work we consider a model of both hemodynamic and perfusion components within the ASL signal. A physiological link between these two components is analyzed and used for a more accurate estimation of the perfusion response function in particular in the usual ASL low SNR conditions.
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Submitted on : Wednesday, January 21, 2015 - 10:58:06 AM
Last modification on : Saturday, June 25, 2022 - 9:09:20 PM


  • HAL Id : hal-01107613, version 1



Florence Forbes, Aina Frau-Pascual, Thomas Vincent, Jennifer Sloboda, Philippe Ciuciu. Physiologically informed Bayesian analysis of ASL fMRI data. Statistical Challenges in Neuroscience workshop, Sep 2014, Warwick, United Kingdom. ⟨hal-01107613⟩



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