Predicting brain activity using a Bayesian spatial model

Gordana Derado, F. Dubois Bowman, Lijun Zhang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.

Original languageEnglish (US)
Pages (from-to)382-397
Number of pages16
JournalStatistical Methods in Medical Research
Volume22
Issue number4
DOIs
StatePublished - Aug 2013

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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