In recent years, there has been considerable research interest in the study of brain
connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies …

It is important in image restoration to remove noise while preserving meaningful details such
as blurred thin edges and low-contrast fine features. The existing edge-preserving …

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 …

Motivated by recent work on studying massive imaging data in various neuroimaging
studies, we propose a novel spatially varying coefficient model (SVCM) to capture the …

Functional magnetic resonance imaging (fMRI) investigations of a longitudinal nature, where
participants are scanned repeatedly over time and imaging data are obtained at more than …

One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection
of local activation in the human brain. However, lack of statistical power and inflated false …

It is common practice to spatially smooth fMRI data prior to statistical analysis and a number
of different smoothing techniques have been proposed (eg, Gaussian kernel filters …

Neuroimaging studies aim to analyse imaging data with complex spatial patterns in a large
number of locations (called voxels) on a two-dimensional surface or in a three-dimensional …

Electrophysiological and activity-dependent gene expression studies of birdsong have
contributed to the understanding of the neural representation of natural sounds. However …

We describe a Bayesian scheme to analyze images, which uses spatial priors encoded by a
diffusion kernel, based on a weighted graph Laplacian. This provides a general framework …