Measuring the Dynamic Balance of Integration and Segregation Underlying Consciousness, Anesthesia, and Sleep in Humans

Overview

Journal Nat Commun

Specialty Biology

Date 2024 Oct 25

PMID 39448600

Authors

Hyunwoo Jang

George A Mashour

Anthony G Hudetz

Zirui Huang

Affiliations

Soon will be listed here.

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.

Citing Articles

Consciousness and Energy Processing in Neural Systems.

Pepperell R Brain Sci. 2024; 14(11).

PMID: 39595875 PMC: 11591782. DOI: 10.3390/brainsci14111112.

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

Jang H, Mashour G, Hudetz A, Huang Z Nat Commun. 2024; 15(1):9164.

PMID: 39448600 PMC: 11502666. DOI: 10.1038/s41467-024-53299-x.

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