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

M. Filippone*, Andre Marquand, C. R. V. Blain, S. C. R. Williams, J. Mourao-Miranda, M. Girolami

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1883-1905
Number of pages23
JournalAnnals of Applied Statistics
Volume6
Issue number4
DOIs
Publication statusPublished - Dec 2012

Keywords

  • CRITERIA
  • high-dimensional data
  • Gaussian process
  • HIERARCHICAL-MODELS
  • Multi-modality multinomial logit model
  • CLASSIFICATION
  • DIFFUSION-TENSOR MRI
  • Parkinsonian diseases
  • hierarchical model
  • Markov chain Monte Carlo
  • prediction of disease state

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