The aim of this thesis is to apply a particular category of machine learning and
pattern recognition algorithms, namely the kernel methods, to both functional and
anatomical magnetic resonance images (MRI). This work specifically focused on
supervised learning methods. Both methodological and practical aspects are described
in this thesis.
Kernel methods have the computational advantage for high dimensional data,
therefore they are idea for imaging data. The procedures can be broadly divided into
two components: the construction of the kernels and the actual kernel algorithms
themselves. Pre-processed functional or anatomical images can be computed into a
linear kernel or a non-linear kernel. We introduce both kernel regression and kernel
classification algorithms in two main categories: probabilistic methods and
non-probabilistic methods. For practical applications, kernel classification methods
were applied to decode the cognitive or sensory states of the subject from the fMRI
signal and were also applied to discriminate patients with neurological diseases from
normal people using anatomical MRI. Kernel regression methods were used to predict
the regressors in the design of fMRI experiments, and clinical ratings from the
anatomical scans
