Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics

This study investigated the possible benefit of subject specific optimization of preprocessing strategies in functional magnetic resonance imaging (fMRI) experiments. The optimization was performed using the data-driven performance metrics developed recently [Neuroimage 15 (2002), 747]. We applied numerous preprocessing strategies and a multivariate statistical analysis to each of the 20 subjects in our two example fMRI data sets. We found that the optimal preprocessing strategy varied, in general, from subject to subject. For example, in one data set, optimum smoothing levels varied from 16 mm (4 subjects), 10 mm (5 subjects), to no smoothing at all (1 subject). This strongly suggests that group-specific preprocessing schemes may not give optimum results. For both studies, optimizing the preprocessing for each subject resulted in an increased number of suprathresholded voxels in within-subject analyses. Furthermore, we demonstrated that we were able to aggregate the optimized data with a random effects group analysis, resulting in improved sensitivity in one study and the detection of interesting, previously undetected results in the other.

Authors

• Marnie E. Shaw
• Stephen C. Strother
• Maria Gavrilescu
• Katherine Podzebenko
• Anthony Waites
• John Watson
• Jon Anderson
• Graeme Jackson
• Gary Egan

Keywords

• fMRI
• Preprocessing
• Reproducibility
• Generalizability
• Optimization