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Functional magnetic resonance imaging (fMRI) is an advanced technology for studying brain functions. Due to the complexity and high cost of fMRI experiments, high quality multi-objective fMRI designs are in great demand. We propose an efficient approach to find optimal experimental designs for event-related functional magnetic resonance imaging (ER-fMRI). We consider multiple objectives, including estimating the hemodynamic response function (HRF), detecting activation, circumventing psychological confounds and fulfilling customized requirements. Taking into account these goals, we formulate a family of multi-objective design criteria and develop a genetic-algorithm-based technique to search for optimal designs. Our proposed technique incorporates existing knowledge about the performance of fMRI designs, and its usefulness is shown through simulations. We also consider a nonlinear model to accommodate a wide spectrum of feasible HRF shapes, and propose an approach for obtaining maximin efficient designs. Our approach involves a reduction in the parameter space and an efficient search algorithm. The designs that we obtain are much more robust against mis-specified HRF shapes than designs widely used by researchers.
(Joint research with Ming-Hung (Jason) Kao, Dibyen Majumdar and John Stufken) |