Block-Champagne: A Novel Bayesian Framework for Imaging Extended E/MEG Source

Estimating the extents of E/MEG source activities is crucial for exploring brain dynamics at high spatiotemporal resolution. In this study, we introduce a novel ESI method - Block-Champagne, a Bayesian framework designed to accurately estimate both the locations and extents of extended sources. Our approach leverages a block-sparsity constraint that models each voxel and its neighbors as a single block to account for local homogeneity. The blocks, inherently overlapping in the original source domain, can be adaptively combined to reconstruct sources with arbitrary spatial extents. Furthermore, prior constraints from other neuroimaging modalities with additional spatial information, such as fMRI, can be incorporated to model interactions between distinct sources to further enhance source reconstruction accuracy. The performance of Block-Champagne is quantitatively evaluated through a series of simulation experiments, which demonstrate its overall superiority under various complex scenarios (i.e., SNR, extent size, number of sources, intra-source correlation, & number of EEG channels) compared to benchmark algorithms (including LORETA, EBI-Convex, ts-Cham, L21-Sissy, & BESTIES). Validation results using deep brain stimulation EEG and epilepsy data confirm the practical feasibility of Block-Champagne. Moreover, findings from face processing multimodal data indicate that incorporating relevant and accurate priors significantly enhances source reconstruction accuracy. In conclusion, our study reveals the superiority of the proposed Block-Champagne in accurate reconstruction of extended source, positioning Block-Champagne as a highly promising tool for realistic applications where source locations and extents are of equivalent importance.