Abstract
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 language | English (US) |
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Pages (from-to) | 2391-2407 |
Number of pages | 17 |
Journal | Journal of Statistical Computation and Simulation |
Volume | 84 |
Issue number | 11 |
DOIs | |
State | Published - Nov 2014 |
Keywords
- 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