Abstract

The human brain controls various cognitive functions via the functional coordination of multiple brain regions in an efficient and robust way. However, the relationship between consciousness state and the control mode of brain networks is poorly explored. Using multi-channel EEG, the present study aimed to characterize the abnormal control architecture of functional brain networks in the patients with disorders of consciousness (DOC). Resting state EEG data were collected from 40 DOC patients with different consciousness levels and 24 healthy subjects. Functional brain networks were constructed in five different EEG frequency bands and the broadband in the source level. Subsequently, a control architecture framework based on the minimum dominating set was applied to investigate the of control mode of functional brain networks for the subjects with different conscious states. Results showed that regardless of the consciousness levels, the functional networks of human brain operate in a distributed and overlapping control architecture different from that of random networks. Compared to the healthy controls, the patients have a higher control cost manifested by more minimum dominating nodes and increased degree of distributed control, especially in the alpha band. The ability to withstand network attack for the control architecture is positive correlated with the consciousness levels. The distributed of control increased correlation levels with Coma Recovery Scale-Revised score and improved separation between unresponsive wakefulness syndrome and minimal consciousness state. These findings may benefit our understanding of consciousness and provide potential biomarkers for the assessment of consciousness levels.

Highlights

  • Human brain can be viewed as a large complex network in terms of the interactions of its neural elements[1, 2]

  • Network metrics can reduce the misdiagnosis rate of disorder of consciousness (DOC) patients and serve as markers to evaluate the neural responses of treatments for patients [18,19,20,21]

  • Our results showed that control architecture has a very significant correlation with network topology metrics, further confirming the close relationship between consciousness levels and network integration ability

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Summary

Introduction

Human brain can be viewed as a large complex network in terms of the interactions of its neural elements[1, 2]. Different brain regions can be seen as vertices, and edges of a network can be divided into structural and functional connections between regions [3]. Patients with severe brain injury may develop disorder of consciousness (DOC), accompanied by significant changes in brain networks which are important for understanding consciousness and assessing the level of consciousness [8,9,10,11,12]. Network metrics can reduce the misdiagnosis rate of DOC patients and serve as markers to evaluate the neural responses of treatments for patients [18,19,20,21]. Weakened or disrupted connectivity in DOC brain networks is significantly correlated with consciousness [22,23,24]. The advantages of low cost, portability, simple operation and high temporal resolution make electroencephalography (EEG) more suitable for bedside monitoring or diagnosis of patients

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