Open Access
December 2012 Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities
M. Filippone, A. F. Marquand, C. R. V. Blain, S. C. R. Williams, J. Mourão-Miranda, M. Girolami
Ann. Appl. Stat. 6(4): 1883-1905 (December 2012). DOI: 10.1214/12-AOAS562


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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M. Filippone. A. F. Marquand. C. R. V. Blain. S. C. R. Williams. J. Mourão-Miranda. M. Girolami. "Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities." Ann. Appl. Stat. 6 (4) 1883 - 1905, December 2012.


Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62103
MathSciNet: MR3058687
Digital Object Identifier: 10.1214/12-AOAS562

Keywords: Gaussian process , hierarchical model , High-dimensional data , Markov chain Monte Carlo , Multi-modality multinomial logit model , Parkinsonian diseases , prediction of disease state

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 4 • December 2012
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