DesignD-Optimal Event-Related Functional Magnetic Resonance Imaging Experiments

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.

On designing experiments for a dynamic response modeled by regression splines

“…Mixed‐effect models have been applied on a wide variety of experimental design studies. For examples, Goos and Jones used this model for designing split‐plot experiments; Laird and Ware used it to study the repeated measurement problem; and Liu and Frank, Kao et al, and Saleh et al applied it on functional magnetic resonance imaging experiments. However, the experimental response to be considered in this paper is much more complicated than those in previous studies.…”