Condensation of eigenmodes in functional brain network and its correlation to chimera state

Abstract

Condensation has long been a closely studied problem in statistical physics but little attention has been paid to neural science. Here, we discuss this problem in brain networks and discover the condensation of a functional brain network whereby all its eigenmodes are condensed only into a few or even a single eigenmode of the structural brain network. We show that the condensation occurs due to the emergence of both chimera states and brain functions from the structure of the brain network. Furthermore, the condensation only appears in the regions of chimera and the condensed eigenmodes are only limited to the lower ones. Condensation is confirmed across different levels of brain subnetworks, including hemispheres, cognitive subnetworks, and isolated cognitive subnetworks, which are further supported by resting-state functional connectivity from empirical data. Our results indicate that condensation could be a potential mechanism for performing brain functions.

Chimera states have a close relationship with brain functionality but how to understand them directly from brain functional networks is challenging. Here, using the method of eigenmode analysis, the authors report a condensation phenomenon of all eigenmodes of brain functional networks and show that the chimera states represent the condensation on lower eigenmodes.