Disorders of consciousness (DoC), caused by severe brain injury, present major challenges for clinical diagnosis, treatment, and prognosis. The theory of brain criticality proposes that the healthy brain operates near a critical point between order and disorder, enabling optimal information processing and neural integration. However, whether the brains of DoC patients deviate from this critical state, and how such deviations relate to metabolic dysfunction and impaired consciousness, has remained largely unknown.

Now, a new study has found that patients with unresponsive wakefulness syndrome (UWS) show greater deviations from the brain's critical state than patients in a minimally conscious state (MCS).

The study was conducted by researchers from the Institute of Biophysics of the Chinese Academy of Sciences and Beijing Tiantan Hospital, Capital Medical University. By combining resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET), the researchers systematically investigated alterations in brain criticality in patients with DoC from the perspectives of neural dynamics and energy metabolism.

The study was published in Communications Biology on July 20.

To characterize brain criticality, the researchers used three complementary measures: the power-law scaling exponent of co-activation clusters, Ising model-based measures of neural dynamics, and phase synchronization.

Compared with patients in an MCS, those with UWS exhibited higher power-law scaling exponents and Ising energy, along with lower phase synchronization. The pattern indicates that the brains of UWS patients deviate further from the critical state and shift toward a more suppressed, subcritical regime.

In this subcritical state, neural activity becomes less capable of sustaining large-scale propagation and long-range communication, leading to reduced information integration and a diminished dynamic range.

Further analyses revealed that regional differences in criticality metrics between UWS and MCS patients were significantly correlated with regional differences in cerebral glucose metabolism, providing metabolic evidence for the observed abnormalities in brain dynamics.

The researchers further integrated three whole-brain criticality metrics into a support vector machine (SVM) classifier. The model achieved an accuracy of 92.31% in distinguishing UWS from MCS patients and 76.00% in predicting clinical outcomes, outperforming PET-based metabolic measures.

By characterizing DoC from the perspective of large-scale brain dynamics, this study provides new insights into the neural mechanisms underlying impaired consciousness and identifies brain criticality as a promising framework for developing objective tools for diagnosis and prognosis.

Study workflow (Image by LIU Ning's group)