Optimization of experimental design in fMRI : a general framework using a genetic algorithm
Wager, Tor D. and Nichols, Thomas E.. (2003) Optimization of experimental design in fMRI : a general framework using a genetic algorithm. NeuroImage, Vol.18 (No.2). pp. 293-309. ISSN 10538119Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/S1053-8119(02)00046-0
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.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Magnetic resonance imaging -- Data processing, Experimental design, Genetic algorithms, Magnetic resonance imaging -- Statistical methods|
|Journal or Publication Title:||NeuroImage|
|Date:||30 January 2003|
|Page Range:||pp. 293-309|
|Access rights to Published version:||Restricted or Subscription Access|
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