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There has been a great deal of recent work making use of multivariate classification techniques such as support vector machines to classify brain images obtained from MRI or PET as healthy or suffering from neurodegenerative disease. In the case of Alzheimer's disease, the results are as accurate as standard clinical tests and could potentially be used in a diagnostic setting. However these techniques...
This paper describes a novel approach to estimate the consistency of global functional connectivity. We apply kernel regression methods, kernel ridge regression (KRR) and support vector regression (SVR), to predict the time-series from a target voxel using voxels in the rest of the brain as features. A correlation coefficient, obtained by cross-validation, was used to define the consistency of global...
This paper introduces a multivariate Bayesian (MVB) scheme to decode or recognise brain states from neuroimages. It resolves the ill-posed many-to-one mapping, from voxel values or data features to a target variable, using a parametric empirical or hierarchical Bayesian model. This model is inverted using standard variational techniques, in this case expectation maximisation, to furnish the model...
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