  For many neurological disorders, prediction of disease state is an important
clinical aim. Neuroimaging provides detailed information about brain structure
and function from which such predictions may be statistically derived. A
multinomial logit model with Gaussian process priors is proposed to: (i)
predict disease state based on whole-brain neuroimaging data and (ii) analyze
the relative informativeness of different image modalities and brain regions.
Advanced Markov chain Monte Carlo methods are employed to perform posterior
inference over the model. This paper reports a statistical assessment of
multiple neuroimaging modalities applied to the discrimination of three
Parkinsonian neurological disorders from one another and healthy controls,
showing promising predictive performance of disease states when compared to
nonprobabilistic classifiers based on multiple modalities. The statistical
analysis also quantifies the relative importance of different neuroimaging
measures and brain regions in discriminating between these diseases and
suggests that for prediction there is little benefit in acquiring multiple
neuroimaging sequences. Finally, the predictive capability of different brain
regions is found to be in accordance with the regional pathology of the
diseases as reported in the clinical literature.
