The nature of fMRI studies impels the need for multi-objective designs that simultaneously accomplish statistical goals, circumvent psychological confounds, and fulfill customized requirements. Incorporating knowledge about fMRI designs, we propose an efficient algorithm to search for optimal multi-objective designs. This algorithm significantly outperforms previous search algorithms in terms of achieved efficiency, computation time and convergence rate. Furthermore, our design criterion allows fair, consistent design comparisons. This consistency is crucial to the success of the search algorithms. Moreover, the parametrization utilized in our underlying model allows parameters that are interpretable and better reflect the shape of the hemodynamic response function.

TR Number: 
Ming-Hung Kao, Abhyuday Mandal, Nicole Lazar, and John Stufken
Key Words: 
compound design criterion; counterbalancing; design efficiency; discretization interval; fMRI designs; genetic algorithms; normalization.

To request a copy of this report, please email us. We will send you a pdf copy if one is available.