Coupled generator decomposition for fusion of electro- and magnetoencephalography data
This provides a new data fusion method for multimodal neuroimaging researchers, though it appears incremental as it builds on existing sparse principal component analysis techniques.
The researchers tackled the problem of identifying common neural features across EEG and MEG data while accounting for modality- and subject-specific variability, introducing a coupled generator decomposition framework that generalizes sparse principal component analysis. Their results revealed altered fusiform face area activation around 170ms for scrambled versus real faces, with the model showing comparable performance to conventional methods but with faster execution.
Data fusion modeling can identify common features across diverse data sources while accounting for source-specific variability. Here we introduce the concept of a \textit{coupled generator decomposition} and demonstrate how it generalizes sparse principal component analysis (SPCA) for data fusion. Leveraging data from a multisubject, multimodal (electro- and magnetoencephalography (EEG and MEG)) neuroimaging experiment, we demonstrate the efficacy of the framework in identifying common features in response to face perception stimuli, while accommodating modality- and subject-specific variability. Through split-half cross-validation of EEG/MEG trials, we investigate the optimal model order and regularization strengths for models of varying complexity, comparing these to a group-level model assuming shared brain responses to stimuli. Our findings reveal altered $\sim170ms$ fusiform face area activation for scrambled faces, as opposed to real faces, particularly evident in the multimodal, multisubject model. Model parameters were inferred using stochastic optimization in PyTorch, demonstrating comparable performance to conventional quadratic programming inference for SPCA but with considerably faster execution. We provide an easily accessible toolbox for coupled generator decomposition that includes data fusion for SPCA, archetypal analysis and directional archetypal analysis. Overall, our approach offers a promising new avenue for data fusion.