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Signal propagation along the structural connectome of the brain induceschanges in the patterns of activity. These activity patterns define globalbrain states and contain information in accordance with their expectedprobability of occurrence. The structural connectome, in conjunction with thedynamics, determines the set of possible brain states and constrains thetransition between accessible states. Yet, precisely how these structuralconstraints on state-transitions relate to their information content remainsunexplored. To address this gap in knowledge, we defined the informationcontent as a function of the activation distribution, where statistically rarevalues of activation correspond to high information content. With thisnumerical definition in hand, we studied the spatiotemporal distribution ofinformation content in fMRI data from the Human Connectome Project duringdifferent tasks, and report four key findings. First, information contentstrongly depends on the cognitive task. Second, while information content showssimilarities to other measures of brain activity, it is distinct from bothNeurosynth maps and task contrast maps generated by a general linear modelapplied to the fMRI data. Third, the brain's structural wiring constrains thecost to control its state, where the cost to transition into high informationcontent states is larger than that to transition into low information contentstates. Finally, all state transitions - especially those to high informationcontent states - are less costly than expected from random network null models,thereby indicating the brain's marked efficiency. Taken together, our findingsestablish an explanatory link between the information contained in a brainstate and the energetic cost of attaining that state, thereby laying importantgroundwork for our understanding of large-scale cognitive computations.

The information content of brain states is explained by structural constraints on state energetics