Abstract
Previous studies on event-related functional magnetic resonance imaging 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 efficient approaches for obtaining maximin and maximin-efficient designs. Our approaches involve a reduction in the parameter space and a search algorithm that helps to efficiently search over a restricted class of designs for good designs. The obtained designs are compared with traditional designs widely used in practice. We also demonstrate the usefulness of our approaches via a motivating example.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1940-1959 |
| Number of pages | 20 |
| Journal | Annals of Applied Statistics |
| Volume | 7 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2013 |
| Externally published | Yes |
Keywords
- A-optimality
- Cyclic permutation
- Design efficiency
- Genetic algorithms
- Hemodynamic response function
- Information matrix
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
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