A Bayesian probit model with spatially varying coefficients for brain decoding using fMRI data

Fengqing Zhang*, Wenxin Jiang, Patrick Wong, Ji Ping Wang

*Corresponding author for this work

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Recent advances in human neuroimaging have shown that it is possible to accurately decode how the brain perceives information based only on non-invasive functional magnetic resonance imaging measurements of brain activity. Two commonly used statistical approaches, namely, univariate analysis and multivariate pattern analysis often lead to distinct patterns of selected voxels. One current debate in brain decoding concerns whether the brain's representation of sound categories is localized or distributed. We hypothesize that the distributed pattern of voxels selected by most multivariate pattern analysis models can be an artifact due to the spatial correlation among voxels. Here, we propose a Bayesian spatially varying coefficient model, where the spatial correlation is modeled through the variance-covariance matrix of the model coefficients. Combined with a proposed region selection strategy, we demonstrate that our approach is effective in identifying the truly localized patterns of the voxels while maintaining robustness to discover truly distributed pattern. In addition, we show that localized or clustered patterns can be artificially identified as distributed if without proper usage of the spatial correlation information in fMRI data.

Original languageEnglish (US)
Pages (from-to)4380-4397
Number of pages18
JournalStatistics in Medicine
Volume35
Issue number24
DOIs
StatePublished - Oct 30 2016

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Keywords

  • brain decoding
  • classification
  • fMRI
  • multivariate pattern analysis
  • variable selection

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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