BRAINZOOM: High resolution reconstruction from multi-modal brain signals

How close can we zoom in to observe brain activity? Our understanding is limited by the resolution of imaging modalities that exhibit good spatial but poor temporal resolution, or vice-versa. In this paper, we propose BRAINZOOM, an efficient imaging algorithm that cross-leverages multi-modal brain signals. BRAINZOOM (a) constructs high resolution brain images from multi-modal signals, (b) is scalable, and (c) is flexible in that it can easily incorporate various priors on the brain activities, such as sparsity, low rank, or smoothness. We carefully formulate the problem to tackle nonlinearity in the measurements (via variable splitting) and auto-scale between different modal signals, and judiciously design an inexact alternating optimization-based algorithmic framework to handle the problem with provable convergence guarantees. Our experiments using a popular realistic brain signal simulator to generate fMRI and MEG demonstrate that high spatio-temporal resolution brain imaging is possible from these two modalities. The experiments also suggest that smoothness seems to be the best prior, among several we tried.