Detection of EEG dynamic complex patterns in disorders of consciousness.
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Diagnosing Disorders of Consciousness (DoC) remains a critical challenge in cognitive neuroscience. In this study we introduce Electroencephalography (EEG)-based brain states as a real-time, bedside tool for assessing dynamic brain connectivity in DoC patients. We analyze EEG data from 237 acute and chronic DoC patients across three centers, identifying five recurrent functional connectivity patterns. The probability of these patterns correlated strongly with consciousness levels, with high-entropy patterns exclusive to healthy controls and low-entropy patterns prevalent in severe DoC, predicting individual recovery outcomes. Real-time testing validated reliable bedside detection of these patterns. Our findings demonstrate EEG's potential for monitoring dynamic brain connectivity, offering insights into the neural basis of consciousness and advancing diagnostic strategies for DoC.
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Acknowledgements: This research was supported by Agencia Nacional de Promoción Científica y Tecnológica, Argentina (Grants #2018-03614, CAT-I-00083) and Stic Amsud project (CONN-COMA, 2023). G.D.B. and P.B. were supported by the National Scientific and Technical Research Council (CONICET - Argentina). D.Z. was supported by National High Level Hospital Clinical Research Funding, and by Elite Medical Professionals Initiative of China-Japan Friendship Hospital (NO. ZRJY2025-QM20), and by Beijing Natural Science Foundation (7254417). P.G. was supported by the National Natural Science Foundation of China (82201352) and the Youth Innovation Promotion Association of Chinese Academy of Sciences (2022267). L.W. was supported by the CAS Project for Young Scientists in Basic Research (YSBR-071) and the Shanghai Municipal Science and Technology Major Project (2021SHZDZX). Y.M. and X.W. were funded by the Shanghai Municipal Science and Technology Major Project ([2018SHZDZX01)], ZJLab and the Shanghai Center for Brain Science and Brain-Inspired Technology. X.W. was also funded by the National Natural Science Foundation of China (82271224). L.W. is a SANS (Shanghai Academy of Natural Sciences) Exploration Scholar. We thank Rodrigo Echeveste, Srivas Chennu, Damian Cruse, Demian Engemann, Federico Raimondo and Anat Arzi for useful discussions, and anonymous reviewers for useful suggestions.
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2399-3642
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