Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep in humans.

A metric called the integration-segregation difference (ISD), derived from fMRI data, captures two key brain network properties: efficiency (integration) and clustering (segregation). During anesthesia with propofol, brain networks shift profoundly toward segregation as consciousness is lost. A common sequence of disintegration and reintegration occurs in unimodal and transmodal networks during loss and return of responsiveness. Machine learning models using these measures accurately identify awake versus unresponsive states. Metastability is more closely linked to integration, while complexity is linked to segregation. Similar patterns appear in sleep. The ISD reliably indexes states of consciousness.

Study at a glance

Abstract

Consciousness requires a dynamic balance of integration and segregation in brain networks. We report an fMRI-based metric, the integration-segregation difference (ISD), which captures two key network properties: network efficiency (integration) and clustering (segregation). With this metric, we quantify brain state transitions from conscious wakefulness to unresponsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift towards the segregation of brain networks during anesthesia. A common unimodal-transmodal sequence of disintegration and reintegration occurs in brain networks during, respectively, loss and return of responsiveness. Machine learning models using integration and segregation data accurately identify awake vs. unresponsive states and their transitions. Metastability (dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (diversity of neural activity) is more closely linked with segregation. A parallel analysis of sleep states produces similar findings. Our results demonstrate that the ISD reliably indexes states of consciousness.