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This study concerns optimal designs for functional magnetic resonance imaging (fMRI) experiments when the model matrix of the statistical model depends on both the selected stimulus sequence (fMRI design), and the subject's uncertain feedback (e.g. answer) to each mental stimulus (e.g. question) presented to her/him. While practically important, this design issue is challenging. This mainly is because that the information matrix cannot be fully determined at the design stage, making it difficult to evaluate the quality of the selected designs. To tackle this challenging issue, we propose an easy-to-use optimality criterion for evaluating the quality of designs, and an efficient approach for obtaining designs optimizing this criterion. Compared with a previously proposed method, our approach requires a much less computing time to achieve designs with high statistical efficiencies. [ABSTRACT FROM AUTHOR]

Event-related functional magnetic resonance imaging (ER-fMRI) is a leading technology for studying brain activity in response to mental stimuli. Due to the popularity and high cost of this pioneering technology, efficient experimental designs are in great demand. However, the complex nature of ER-fMRI makes it difficult to obtain such designs; it requires careful consideration regarding both statistical and practical issues as well as major computational efforts. In this article, we obtain efficient designs for ER-fMRI. In contrast to previous studies, we take into account a common practice where subjects undergo multiple scanning sessions in an experiment. To the best of our knowledge, this important reality has never been studied systematically for design selection. We compare several approaches to obtain efficient designs and propose a novel algorithm for this problem. Our simulation results indicate that, using our algorithm, highly efficient designs can be obtained. [ABSTRACT FROM AUTHOR]

Abstract: In this article, 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. Although our approach also works for other linear combinations of parameters, we primarily focus on the case when the interest lies either in the individual stimulus effects or in pairwise contrasts between stimulus types. Under either of these popular cases, our algorithm outperforms the previous approaches. We also find designs yielding higher estimation efficiencies than m-sequences. When the underlying model is with white noise and a constant nuisance parameter, the stimulus frequencies of the designs we obtained are in good agreement with the optimal stimulus frequencies derived by Liu and Frank, 2004, NeuroImage 21: 387-400. In addition, our approach is built upon a rigorous model formulation. [Copyright &y& Elsevier]