Pinpointing consciousness in animal brains using a mouse 'brain map'
). To be specific, in this study, we first define a “main” complex as a subnetwork that has the local maximum of bidirectional connection strength. We evaluate the strength of bidirectional connections by a measure that takes a large value when nodes are connected by strong bidirectional edges. To reveal the network structure, we also define complexes as a weaker notion of a main complex. Complexes are less strongly bidirectionally connected than a main complex and form a nested structure.
If we search for complexes by brute force, computation time grows exponentially with the number of nodes, because we need to take account of all possible subnetworks. To reduce computation time, we can use an algorithm proposed in our previous study . This algorithm, called hierarchical partitioning for complex search , enables the identification of complexes simply by hierarchically dividing the entire network.
As a step in investigating the relationship between bidirectionally connected subnetworks—complexes—and consciousness, we applied the proposed method to a meso-scale, whole-brain mouse connectome and identified the complexes. This connectome includes not only the cortical regions but also the subcortical, brainstem, and cerebellar regions, and has high spatial resolution. These characteristics make it suitable for discussing the relationship between brain regions and consciousness.
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