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In this work, the Bayesian framework is used for the analysis of fMRI data. The novelty of the proposed approach is the introduction of a spatio-temporal model used to estimate the variance of the noise across the images and the voxels. The proposed approach is based on a spatio-temporal version of generalized linear model (GLM). To estimate the regression parameters of the GLM as well as the variance...
In this work we present a Bayesian approach for the estimation of the regression parameters in the analysis of fMRI data when the noise is non - stationary. The proposed approach is based on the variational Bayesian (VB) methodology and the generalized linear model (GLM). The VB methodology permits the use of prior distributions over the parameters of the noise. This results to a very elegant approach...
The aim of this work is to propose a new approach for the determination of the design matrix in fMRI experiments. The design matrix embodies all available knowledge about experimentally controlled factors and potential confounds. This knowledge is expressed through the regressors of the design matrix. However, in a particular fMRI time series some of those regressors may not be present. In order to...
We present an automated supervised method which assists in the diagnosis of Alzheimerpsilas disease (AD) using fMRI data. The method consists of five stages: a) preprocessing of fMRI data to remove motion and spatial noise artifacts, b) modeling of the data using generalized linear models (GLM), c) feature extraction, d) feature selection and e) classification using majority and weighted voting schemes.
In this work we present a supervised method to assist the diagnosis of Alzheimer's disease (AD) based on functional magnetic resonance images (fMRI). The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling the way in which the BOLD response depends on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification...
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