Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

Untitled Page 1 Page 2 Phillip I. Good Resampling Methods A Practical Guide to Data Analysis
Third Edition Birkhäuser Boston • Basel • Berlin Page 3 Phillip I. Good 205 West Utica Avenue …

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the
hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO 2). While …

We propose a novel iterative scheme for adaptive smoothing of functional MR images. The
method estimates a signal model at every voxel in the time-series, which is subsequently …

To spatially cluster resting state‐functional magnetic resonance imaging (rs‐fMRI) data into
potential networks, there are only a few general approaches that determine the number of …

Spatial smoothing using isotropic Gaussian kernels to remove noise reduces spatial
resolution and increases the partial volume effect of functional magnetic resonance images …

In this article, a generalized likelihood ratio test is proposed to assess the correlation
between multisubject functional MRI (fMRI) time series and bases of a signal subspace for …

Background High-resolution fMRI, useful for accurate brain map**, suffers from low
functional sensitivity at a reasonable acquisition time. Conventional smoothing techniques …

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed
to more accurately determine activation patterns in fMRI data. In its conventional formulation …

We present an iterative scheme for adaptive smoothing of functional magnetic resonance
images. We propose a novel similarity measure to estimate the weights of the smoothing …