How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging

• Published in: NeuroImage (Orlando, Fla.) Vol. 158; pp. 186 - 195
• Main Authors: Soch, Joram, Meyer, Achim Pascal, Haynes, John-Dylan, Allefeld, Carsten
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2017; Elsevier Limited
• Subjects: Algorithms; Bayes Theorem; Bayesian analysis; Bayesian model averaging; Brain Mapping - methods; correlated regressors; cross-validation; Data analysis; Data processing; Economic models; fMRI-based neuroimaging; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Linear Models; Magnetic Resonance Imaging - methods; mass-univariate GLM; Mathematical models; Models, Neurological; Models, Theoretical; Neuroimaging; nuisance variables; mass-univariate GLM; Bayesian model averaging; nuisance variables; correlated regressors; cross-validation; fMRI-based neuroimaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.06.056

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; http://dx.doi.org/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 model in each subject or the model which is best in a group of subjects. •Functional MRI (fMRI) data are analyzed using general linear models (GLMs).•Different GLMs lead to different conclusions regarding experimental effects.•In nested model comparison, the most complex GLM is not necessarily the best choice.•Bayesian model selection (BMS) can prevent not detecting established effects.•Bayesian model averaging (BMA) can be more sensitive than using the best GLM.