Altered electrophysiological meta-state dynamics in disorders of consciousness

This is the authors' abstract. We don't add key points for this paper.

• We studied EEG brain states in disorders of consciousness and healthy controls using two independent datasets of resting-state and task-based data. • Brain states are remarkably similar after injury compared to those of healthy controls. • Brain state dynamics are more unstable in higher frequencies in DoC. • Anticorrelation between active states is lower in DoC patients in higher frequencies. • Using combined learning classification, we show that brain state dynamics could serve as biomarkers to assess consciousness levels in DoC. This multi-centric study aimed to explore differences in brain activity patterns in patients with disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Using high-density electroencephalographic (EEG) recordings from 368 DoC patients, 39 who emerged from MCS (eMCS), and 73 healthy controls, we examined instantaneous functional connectivity-based meta-states acting as attractors in a dynamical system, extracted by means of community detection algorithms and recurrence analysis. We analyzed data from two patient cohorts and included resting-state and auditory processing tasks in four frequency bands (delta, theta, alpha, beta) and from three perspectives, namely: (i) discrete activation of dominant states, (ii) a dynamical system composed of attractor states and (iii) the correlation and anticorrelation patterns of the active states. Findings revealed that while the overall structure of brain connectivity remained stable after injury, patients with DoC and those who emerged showed notable differences in the speed and consistency of their brain states activated. Specifically, in higher frequencies, UWS patients exhibited faster, and less stable dynamics, shorter dwell times and decreased meta-state anticorrelation compared to those in MCS and eMCS. Moreover, a four-way combined learning classification analyses showed that the measures were able to distinguish the UWS and MCS subgroups. These brain state dynamics could serve as valuable markers for assessing consciousness levels. Our results highlight the potential of using high-temporal resolution dynamic brain activity patterns to improve the understanding of altered consciousness and their application to clinical settings.