4 Scopus citations


We propose a novel, efficient approach for obtaining high-quality experimental designs for event-related functional magnetic resonance imaging (ER-fMRI), a popular brain mapping technique. Our proposed approach combines a greedy hill-climbing algorithm and a cyclic permutation method. When searching for optimal ER-fMRI designs, the proposed approach focuses only on a promising restricted class of designs with equal frequency of occurrence across stimulus types. The computational time is significantly reduced. We demonstrate that our proposed approach is very efficient compared with a recently proposed genetic algorithm approach. We also apply our approach in obtaining designs that are robust against misspecification of error correlations.

Original languageEnglish (US)
Pages (from-to)2391-2407
Number of pages17
JournalJournal of Statistical Computation and Simulation
Issue number11
StatePublished - Nov 2014


  • A-optimality
  • D-optimality
  • autoregressive process
  • cyclic permutation
  • genetic algorithms
  • hill-climbing technique
  • maximin designs

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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