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
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
3 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur

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.

Domaines: Sciences du Vivant [q-bio]; Statistiques [math.ST]

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⟩