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Unsupervised learning approaches, such as Sparse Partial Least Squares (SPLS), may provide useful insights into the brain mechanisms by finding relationships between two sets of variables (i.e. Views) from the same subjects. The algorithm outputs two sets of paired weight vectors, where each pair expresses an "effect" between both views. However, each effect can be described by a different...
In this paper we compare two different methods for dealing with so-called nuisance variables (NV) when training models to predict clinical/psychometric scales from neuroimaging data. In the first approach, the NV are used to adjust the imaging data by 'regressing out' their contribution to the image features. In the second approach, the NV are included as additional predictors in the model with a...
Machine learning has obvious applications to the diagnosis of disease, and for many neurological conditions features extracted from brain images allow classifiers based on neuroimaging biomarkers to provide a useful complement to more traditional diagnostic methods based on symptoms and psychological testing. However the labels used in the training of such systems frequently depend on standard clinical...
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...
The Free-Form Deformation (FFD) algorithm is a widely-used approach for non-rigid registration. Modifications have previously been proposed to ensure topology preservation and invertibility within this framework. However, in practice, none of these yield the inverse transformation itself, and one loses the parsimonious B-spline parameterisation.
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...
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