Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

This article describes a method for selecting design parameters and a particular sequence of events in fMRI so as to maximize statistical power and psychological validity. Our approach uses a genetic algorithm (GA), a class of flexible search algorithms that optimize designs with respect to single or multiple measures of fitness. Two strengths of the GA framework are that (1) it operates with any sort of model, allowing for very specific parameterization of experimental conditions, including nonstandard trial types and experimentally observed scanner autocorrelation, and (2) it is flexible with respect to fitness criteria, allowing optimization over known or novel fitness measures. We describe how genetic algorithms may be applied to experimental design for fMRI, and we use the framework to explore the space of possible fMRI design parameters, with the goal of providing information about optimal design choices for several types of designs. In our simulations, we considered three fitness measures: contrast estimation efficiency, hemodynamic response estimation efficiency, and design counterbalancing. Although there are inherent trade-offs between these three fitness measures, GA optimization can produce designs that outperform random designs on all three criteria simultaneously.

Original publication

DOI

10.1016/s1053-8119(02)00046-0

Type

Journal article

Journal

NeuroImage

Publication Date

02/2003

Volume

18

Pages

293 - 309

Addresses

Department of Psychology, C/P Area, University of Michigan, 525 E. University, Ann Arbor, MI 48109-1109, USA. torw@umich.edu

Keywords

Cerebral Cortex, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Image Enhancement, Artifacts, Models, Statistical, Arousal, Attention, Algorithms, Fourier Analysis, Research Design, Computer Simulation