Optimization of experimental design in fMRI: a general framework using a genetic algorithm.

@article{citeulike:1150814, abstract = {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.}, address = {Department of Psychology, C/P Area, University of Michigan, 525 E. University, Ann Arbor, MI 48109-1109, USA. torw@umich.edu}, author = {Wager, T. D. and Nichols, T. E. }, citeulike-article-id = {1150814}, issn = {1053-8119}, journal = {Neuroimage}, keywords = {fmri\_methodology, hemodynamic\_response, optimal\_sequencing, optimal\_stimulus\_presentation}, month = {February}, number = {2}, pages = {293--309}, posted-at = {2007-05-11 14:22:25}, priority = {5}, title = {Optimization of experimental design in fMRI: a general framework using a genetic algorithm.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/12595184}, volume = {18}, year = {2003} }

TY - JOUR ID - citeulike:1150814 L3 - citeulike-article-id:1150814 TI - Optimization of experimental design in fMRI: a general framework using a genetic algorithm. JF - Neuroimage VL - 18 IS - 2 SP - 293 EP - 309 AD - Department of Psychology, C/P Area, University of Michigan, 525 E. University, Ann Arbor, MI 48109-1109, USA. torw@umich.edu SN - 1053-8119 N2 - 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. KW - fmri_methodology KW - hemodynamic_response KW - optimal_sequencing KW - optimal_stimulus_presentation AU - Wager, TD AU - Nichols, TE PY - 2003/02// UR - http://view.ncbi.nlm.nih.gov/pubmed/12595184 ER -