Journal
ANNALS OF APPLIED STATISTICS
Volume 6, Issue 4, Pages 1883-1905Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/12-AOAS562
Keywords
Multi-modality multinomial logit model; Gaussian process; hierarchical model; high-dimensional data; Markov chain Monte Carlo; Parkinsonian diseases; prediction of disease state
Categories
Funding
- KCL Centre of Excellence in Medical Engineering
- Wellcome Trust
- EPSRC [WT088641/Z/09/Z, EP/E052029/1, EP/H024875/1]
- Wellcome Trust [WT086565/Z/08/Z]
- EPSRC [EP/E052029/1, EP/H024875/2] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H024875/2, EP/E052029/1] Funding Source: researchfish
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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.
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