Alicia Quirós Carretero

Learn More
This research describes a new Bayesian spatiotemporal model to analyse block-design BOLD fMRI studies. In the temporal dimension, we parameterise the hemodynamic response function's (HRF) shape with a potential increase of signal and a subsequent exponential decay. In the spatial dimension, we use Gaussian Markov random fields (GMRF) priors on activation(More)
This research describes a new Bayesian spatiotemporal model to analyse BOLD fMRI studies. In the temporal dimension, we describe the shape of the hemodynamic response function (HRF) with a transfer function model. The spatial continuity and local homogeneity of the evoked responses are modelled by a Gaussian Markov random field prior on the parameter(More)
a r t i c l e i n f o a b s t r a c t Article history: Available online xxxx Keywords: Bayesian analysis Dynamic linear models fMRI Resting-state This work shows an example of the application of Bayesian dynamic linear models in fMRI analysis. Estimating the error variances of such a model, we are able to obtain samples from the posterior distribution of(More)
Source separation is a common task in signal processing and is often analogous to factor analysis. In this work we look at a factor analysis model for source separation of multi-spectral image data where prior information about the sources and their dependencies is quantified as a multivariate Gaussian mixture model with an unknown number of factors.(More)
In this paper a fully Bayesian factor analysis model is developed that assumes a very general model for each factor, namely the Gaussian mixture. We discuss the cases where factors are both independent and dependent. In the statistical literature, factor analysis has been used principally as a dimension reduction technique, with little interest in a priori(More)
  • 1