A method for hierarchically decomposing a brain network into cores based on the strength of bidirectional connections helps identify regions likely essential for consciousness. Applied to a whole-brain mouse connectome, cores with strong bidirectional connections included the isocortex, thalamus, and claustrum—areas thought to support consciousness—and excluded the cerebellum, which is not considered relevant. Simpler methods that ignore bidirectionality failed to show this correspondence. The findings suggest that analyzing bidirectional connectivity offers a novel way to relate brain network structure to consciousness.
Integrated Information Theory (IIT) links the amount of integrated information (Φ) in the brain to the level of consciousness, proposing that Φ should be measured across the partition of a system where information loss from partitioning is minimized—the Minimum Information Partition (MIP). Exhaustively searching for the MIP is computationally infeasible for large systems. Previous work showed that if a measure of Φ is submodular, an optimization algorithm can find the MIP in polynomial time, but later versions of Φ are not submodular. This study empirically tested the algorithm on non-submodular Φ measures using simulated and real neural data, finding it identifies the MIP nearly perfectly, enabling practical Φ measurement in large systems.
Integrated Information Theory (IIT) links consciousness to the amount of integrated information (Φ) in the brain, measured across a system's Minimum Information Partition (MIP). Finding the MIP is computationally expensive for large systems. Previous work showed that if Φ satisfies submodularity, an optimization algorithm can find the MIP in polynomial time, but later versions of Φ are not submodular. This study tests the algorithm on non-submodular Φ measures using simulated and real neural data. The algorithm identifies the MIP with near-perfect accuracy even for non-submodular measures, enabling practical measurement of Φ in large systems.