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Design D ‐optimal event‐related functional magnetic resonance imaging experiments
Author(s) -
Saleh Moein,
Kao MingHung,
Pan Rong
Publication year - 2017
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12151
Subject(s) - computer science , greedy algorithm , functional design , optimal design , algorithm , magnetic resonance imaging , design of experiments , mathematical optimization , mathematics , machine learning , medicine , statistics , software engineering , radiology
Summary New computer algorithms for finding D ‐optimal designs of stimulus sequence for functional magnetic resonance imaging (MRI) experiments are proposed. Although functional MRI data are commonly analysed by linear models, the construction of a functional MRI design matrix is much more complicated than in conventional experimental design problems. Inspired by the widely used exchange algorithm technique, our proposed approach implements a greedy search strategy over the vast functional MRI design space for a D ‐optimal design. Compared with a recently proposed genetic algorithm, our algorithms are superior in terms of computing time and achieved design efficiency in both single‐objective and multiobjective problems. In addition, the algorithms proposed are sufficiently flexible to incorporate a constraint that requires the exact number of appearances of each type of stimulus in a design. This realistic design issue is unfortunately not well handled by existing methods.