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 (MO) fMRI designs are in great demand; they help to render precise statistical inference, and are keys to the success of fMRI experiments. Here, we propose an efficient approach for obtaining MO fMRI designs. In contrast to existing methods, the proposed approach does not require users to specify weights for the different objectives, and can easily handle constraints to fulfill customized requirements. Moreover, the underlying statistical models that we consider are more general. We can thus obtain designs for cases where brief, long or varying stimulus durations are utilized. The usefulness of our approach is illustrated using various experimental settings. We also show the importance of taking the stimulus duration into account at the design stage.

TR Number: 
Ming-Hung Kao, Abhyuday Mandal, and John Stufken
Key Words: 
Genetic algorithms, Hemodynamic response function, Multi-objective optimization, Design efficiency

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