RT Journal Article
SR Electronic
T1 How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 095778
DO 10.1101/095778
A1 Joram Soch
A1 Achim Pascal Meyer
A1 John-Dylan Haynes
A1 Carsten Allefeld
YR 2017
UL http://biorxiv.org/content/early/2017/03/02/095778.abstract
AB In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: “How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection”, NeuroImage, vol. 141, pp. 469-489; DOI: 10.1016/j.neuroimage.2016.07.047), we have introduced cross-validated Bayesian model selection (cvBMS) to infer the best model for a group of subjects and use it to guide second-level analysis. While this is the optimal approach given that the same GLM has to be used for all subjects, there is a much more efficient procedure when model selection only addresses nuisance variables and regressors of interest are included in all candidate models. In this work, we propose cross-validated Bayesian model averaging (cvBMA) to improve parameter estimates for these regressors of interest by combining information from all models using their posterior probabilities. This is particularly useful as different models can lead to different conclusions regarding experimental effects and the most complex model is not necessarily the best choice. We find that cvBMS can prevent not detecting established effects and that cvBMA can be more sensitive to experimental effects than just using even the best individual model in each subject.