Previous studies on event-related functional magnetic resonance imaging (ER-fMRI) experimental designs are primarily based on linear models, in which a known shape of the hemodynamic response function (HRF) is assumed. However, the HRF shape is usually uncertain at the design stage. To address this issue, we consider a nonlinear model to accommodate a wide spectrum of feasible HRF shapes, and propose an approach for obtaining maximin effcient designs. Our approach involves a reduction in the parameter space and an ecient search algorithm. The designs that we obtain are much more robust against mis-specifed HRF shapes than designs widely used by researchers.

TR Number: 
2011-01
Ming-Hung Kao, Dibyen Majumdar, Abhyuday Mandal, and John Stufken
Key Words: 
A-optimality; Genetic algorithms; Hemodynamic response function; Information matrix; Maximin effcient designs

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