BrainSaber: Reconstruction-Based Information Decomposition for Functional Brain Atlas Construction

Brain atlases define the regional units through which we study brain function. Yet parcellation based on signal or representational similarity does not explicitly distinguish how locations share information or jointly predict activity elsewhere. We introduce BrainSaber, a whole-brain atlas framework built on our proposed reconstruction-based information decomposition RID. RID uses controlled masking of a frozen, pretrained fMRI autoencoder to compare reconstructions of common targets from individual and paired sources. It distinguishes redundant, unique, and synergistic predictive contributions through shared, source-specific, and joint-only reconstruction successes. BrainSaber combines these components with brain-wide predictive flow and spatial constraints to construct contiguous parcels across cortical and non-cortical structures. Regional analyses reveal contrasting associations of redundancy and synergy with conventional functional connectivity. Exploratory cortical and whole-brain evaluations show favorable parcel homogeneity and separability, while prediction across five datasets and cross-window and cross-session analyses support downstream utility and group-atlas repeatability. These findings motivate information composition as a complementary basis for functional parcellation, extending similarity-based grouping to account for individual and joint predictive contributions.