Constrained multiobjective designs for functional magnetic resonance imaging experiments via a modified non-dominated sorting genetic algorithm

Ming-Hung Kao, Abhyuday Mandal, John Stufken

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Functional magnetic resonance imaging (MRI) is an advanced technology for studying brain functions. Owing to the complexity and high cost of functional MRI experiments, high quality multiobjective functional MRI designs are in great demand; they help to render precise statistical inference and are keys to the success of functional MRI experiments. Here, we propose an efficient approach for obtaining multiobjective functional MRI designs. In contrast with existing methods, the approach proposed does not require users to specify weights for the different objectives and can easily handle constraints to fulfil customized requirements. Moreover, the underlying statistical models that we consider are more general. We can thus obtain designs for cases where brief, long or varying stimulus durations are utilized. The usefulness of our approach is illustrated by using various experimental settings.

Original languageEnglish (US)
Pages (from-to)515-534
Number of pages20
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume61
Issue number4
DOIs
StatePublished - Aug 2012

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Functional Magnetic Resonance Imaging
Sorting algorithm
Genetic Algorithm
Experiment
Statistical Inference
Statistical Model
Design
Genetic algorithm
Functional magnetic resonance imaging
Sorting
Requirements
Costs

Keywords

  • Design efficiency
  • Genetic algorithms
  • Haemodynamic response function
  • Multiobjective optimization

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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