Bayesian structure learning for functional neuroimaging

Mijung Park, Oluwasanmi Koyejo, Joydeep Ghosh, Russell A. Poldrack, Jonathan W. Pillow

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

Predictive modeling of functional neuroimaging data has become an important tool for analyzing cognitive structures in the brain. Brain images are high-dimensional and exhibit large correlations, and imaging experiments provide a limited number of samples. Therefore, capturing the inherent statistical properties of the imaging data is critical for robust inference. Previous methods tackle this problem by exploiting either spatial sparsity or smoothness, which does not fully exploit the structure in the data. Here we develop a flexible, hierarchical model designed to simultaneously capture spatial block sparsity and smoothness in neuroimaging data. We exploit a function domain representation for the high-dimensional small-sample data and develop efficient inference, parameter estimation, and prediction procedures. Empirical results with simulated and real neuroimaging data suggest that simultaneously capturing the block sparsity and smoothness properties can significantly improve structure recovery and predictive modeling performance.

Original languageEnglish (US)
Pages (from-to)489-497
Number of pages9
JournalJournal of Machine Learning Research
Volume31
StatePublished - Jan 1 2013
Externally publishedYes
Event16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013 - Scottsdale, United States
Duration: Apr 29 2013May 1 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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    Park, M., Koyejo, O., Ghosh, J., Poldrack, R. A., & Pillow, J. W. (2013). Bayesian structure learning for functional neuroimaging. Journal of Machine Learning Research, 31, 489-497.