  The dynamic core hypothesis posits that consciousness is correlated with
simultaneously integrated and differentiated assemblies of transiently
synchronized brain regions. We represented time-dependent functional
interactions using dynamic brain networks, and assessed the integrityof the
dynamic core by means of the flexibility and largest multilayer module of these
networks. As a first step, we constrained parameter selection using a newly
developed benchmark for module detection in heterogeneous temporal networks.
Next, we applied a multilayer modularity maximization algorithm to dynamic
brain networks computed from functional magnetic resonance imaging (fMRI) data
acquired during deep sleep and under propofol anesthesia. We found that
unconsciousness reconfigured network flexibility and reduced the size of the
largest spatiotemporal module, which we identified with the dynamic core. Our
results present a first characterization of modular brain network dynamics
during states of unconsciousness measured with fMRI, adding support to the
dynamic core hypothesis of human consciousness.
