Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities

Filippone, M., Marquand, A.F., Blain, C.R.V., Williams, S.C.R., Mourão-Miranda, J. and Girolami, M. (2012) Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities. Annals of Applied Statistics, 6(4), pp. 1883-1905. (doi: 10.1214/12-AOAS562)

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Abstract

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Filippone, Dr Maurizio and Girolami, Prof Mark
Authors: Filippone, M., Marquand, A.F., Blain, C.R.V., Williams, S.C.R., Mourão-Miranda, J., and Girolami, M.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Annals of Applied Statistics
Publisher:Institute of Mathematical Statistics
ISSN:1932-6157
Copyright Holders:Copyright © 2012 Institute of Mathematical Statistics
First Published:First published in Annals of Applied Statistics 6(4):1883-1905
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
450691The integration of probabilistic prediction and mechanistic modelling within a computational and systems biology contextMark GirolamiEngineering & Physical Sciences Research Council (EPSRC)EP/E052029/1COM - COMPUTING SCIENCE