Abstract
New computer algorithms for finding D-optimal designs of stimulus sequence for functional magnetic resonance imaging (MRI) experiments are proposed. Although functional MRI data are commonly analysed by linear models, the construction of a functional MRI design matrix is much more complicated than in conventional experimental design problems. Inspired by the widely used exchange algorithm technique, our proposed approach implements a greedy search strategy over the vast functional MRI design space for a D-optimal design. Compared with a recently proposed genetic algorithm, our algorithms are superior in terms of computing time and achieved design efficiency in both single-objective and multiobjective problems. In addition, the algorithms proposed are sufficiently flexible to incorporate a constraint that requires the exact number of appearances of each type of stimulus in a design. This realistic design issue is unfortunately not well handled by existing methods.
Original language | English (US) |
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Pages (from-to) | 73-91 |
Number of pages | 19 |
Journal | Journal of the Royal Statistical Society. Series C: Applied Statistics |
Volume | 66 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2017 |
Keywords
- Exchange algorithm
- Functional magnetic resonance imaging experiments
- Genetic algorithm
- Greedy search
- Multiple objectives
- Optimal experimental design
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty