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Journal of Neurorestoratology 2024, 12(1) https://doi.org/10.1016/j.jnrt.2024.100095
Original Research | Open Access | Issue | Published: 23 January 2024

Decoding consciousness from different time-scale spatiotemporal dynamics in resting-state electroencephalogram

Show Author's Information Chunyun Zhanga,  Li Bied,  Shuai Hand,  Dexiao Zhaoa,  Peidong Lib,  Xinjun Wangb,  Bin Jianga( ),  Yongkun Guob,c( )
Department of Neurosurgery, Qilu Hospital of Shandong University(Qingdao), Qingdao 266000, Shandong, China
Department of Neurosurgery, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan, China
Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou 450000, Henan, China
Department of Neurosurgery, First Hospital of Jilin University, Changchun 130021, Jilin, China
Keywords:
Machine learning, Vegetative state, Minimally conscious state, Consciousness disorders, Spatiotemporal analysis
Cite this article:
Zhang C, Bie L, Han S, et al. Decoding consciousness from different time-scale spatiotemporal dynamics in resting-state electroencephalogram. Journal of Neurorestoratology, 2024, 12(1). https://doi.org/10.1016/j.jnrt.2024.100095
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Abstract Full text About this article
Introduction

Functional connectivity across large-scale networks is crucial for the regulation of conscious states. Nonetheless, our understanding of potential alterations in the temporal dynamics of dynamic functional connectivity (dFC) among patients with disorders of consciousness (DOC) remains limited. The present study aimed to examine different time-scale spatiotemporal dynamics of electroencephalogram oscillation amplitudes recorded in different consciousness states.

Methods

Resting-state electroencephalograms were collected from a cohort of 90 patients with DOC. The sliding window approach was used to create dFC matrices, which were subsequently subjected to k-means clustering to identify distinct states. Finally, we performed state analysis and developed a decoding model to predict consciousness.

Results

There was significantly lower dFC within the forebrain network in patients with unresponsive wakefulness syndrome than in those with a minimally conscious state. Moreover, there were significant differences in temporal properties, mean dwell time, and the number of transitions in the high-frequency band at different time scales between the unresponsive wakefulness syndrome and minimally conscious state groups. Using the multi-band and multi-range temporal dynamics of dFC approach, satisfactory classification accuracy (approximately 83.3 %) was achieved.

Conclusion

Loss of consciousness is accompanied by an imbalance of complex dynamics within the brain. Both transitions between states at short and medium time scales in high-frequency bands and the forebrain are important in consciousness recovery. Together, our findings contribute to a better understanding of brain network alterations in patients with DOC.


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About this article

Decoding consciousness from different time-scale spatiotemporal dynamics in resting-state electroencephalogram

Show Author's information Chunyun Zhanga, Li Bied, Shuai Hand, Dexiao Zhaoa, Peidong Lib, Xinjun Wangb, Bin Jianga( ), Yongkun Guob,c( )
Department of Neurosurgery, Qilu Hospital of Shandong University(Qingdao), Qingdao 266000, Shandong, China
Department of Neurosurgery, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan, China
Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou 450000, Henan, China
Department of Neurosurgery, First Hospital of Jilin University, Changchun 130021, Jilin, China

Abstract

Introduction

Functional connectivity across large-scale networks is crucial for the regulation of conscious states. Nonetheless, our understanding of potential alterations in the temporal dynamics of dynamic functional connectivity (dFC) among patients with disorders of consciousness (DOC) remains limited. The present study aimed to examine different time-scale spatiotemporal dynamics of electroencephalogram oscillation amplitudes recorded in different consciousness states.

Methods

Resting-state electroencephalograms were collected from a cohort of 90 patients with DOC. The sliding window approach was used to create dFC matrices, which were subsequently subjected to k-means clustering to identify distinct states. Finally, we performed state analysis and developed a decoding model to predict consciousness.

Results

There was significantly lower dFC within the forebrain network in patients with unresponsive wakefulness syndrome than in those with a minimally conscious state. Moreover, there were significant differences in temporal properties, mean dwell time, and the number of transitions in the high-frequency band at different time scales between the unresponsive wakefulness syndrome and minimally conscious state groups. Using the multi-band and multi-range temporal dynamics of dFC approach, satisfactory classification accuracy (approximately 83.3 %) was achieved.

Conclusion

Loss of consciousness is accompanied by an imbalance of complex dynamics within the brain. Both transitions between states at short and medium time scales in high-frequency bands and the forebrain are important in consciousness recovery. Together, our findings contribute to a better understanding of brain network alterations in patients with DOC.

Keywords: Machine learning, Vegetative state, Minimally conscious state, Consciousness disorders, Spatiotemporal analysis

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Publication history

Received: 12 August 2023
Revised: 08 January 2024
Accepted: 10 January 2024
Published: 23 January 2024
Issue date: March 2024

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© 2024 The Author(s).

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Acknowledgements

All authors would like to thank the patients who participated in the study.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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