Deriving functional network topology from in vivo two-photon calcium imaging: state-dependent graph features in mouse mesoscale motor cortical network

Abstract

Mesoscale neuronal networks represent an intermediate organizational level linking single-neuron activity to large-scale brain networks. Here, we used in vivo two-photon calcium imaging and graph-theoretical analysis to characterize functional network topology in the primary motor cortex across behavioral states. Motion networks exhibited the largest functional connectivity architectures, whereas anesthesia networks showed reduced network scales together with stronger modular segregation and more pronounced small-world topology. Network sign further shaped topology, with negative associations associated with reduced modularity and weakened small-world structure. Hub analyses revealed additional state-dependent differences: anesthesia networks exhibited stronger hub connectivity despite reduced neuronal activity, whereas motion networks showed higher hub activity with weaker connectivity structure. These findings demonstrate that mesoscale neuronal networks exhibit structured and state-dependent organization and provide a framework for studying cortical network dynamics in normal brain function and brain disorders.