Human disease identification from the scanned body parts helps medical practitioners make
the right decision in lesser time. Image segmentation plays a vital role in automated …

We present a fully Bayesian approach to modeling in functional magnetic resonance
imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic …

Data mining in brain imaging is proving to be an effective methodology for disease
prognosis and prevention. This, together with the rapid accumulation of massive …

Postacquisition denoising of magnetic resonance (MR) images is of importance for clinical
diagnosis and computerized analysis, such as tissue classification and segmentation. It has …

This book explores various state-of-the-art aspects behind the statistical analysis of
neuroimaging data. It examines the development of novel statistical approaches to model …

In this thesis we introduce and analyze the influence model, a particular but tractable
mathematical representation of random, dynamical interactions on networks. Specifically, an …

This paper deals with the estimation of the blood oxygen level-dependent response to a
stimulus, as measured in functional magnetic resonance imaging (fMRI) data. A precise …

This paper addresses the problem of estimating the Potts parameter β jointly with the
unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) …

In this work we treat fMRI data analysis as a spatiotemporal system identification problem
and address issues of model formulation, estimation, and model comparison. We present a …

Resting-state functional MRI is a powerful technique for mapping the functional organization
of the human brain. However, for many types of connectivity analysis, high-resolution …