Detection of EEG dynamic complex patterns in disorders of consciousness

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.

Authors

Institutions

Publication Details

Journal
Communications Biology
Published
2025-08-12
DOI
https://doi.org/10.1038/s42003-025-08666-9
Citations
2
Primary Topic
Traumatic Brain Injury Research
Type
article
Field-Weighted Citation Impact
2.71

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection of EEG dynamic complex patterns in disorders of consciousness

Jacobo Diego Sitt, Di Zang, Peng Gui, Xuehai Wu et al.
2 citations
Communications Biology
Traumatic Brain Injury Research
2.71
article

Detection of EEG dynamic complex patterns in disorders of consciousness

Jacobo Diego Sitt, Di Zang, Peng Gui, Xuehai Wu, Gabriel Della Bella, Pablo Barttfeld, Dragana Manasova, Diego M. Mateos, Stein Silva, Tristán Bekinschtein, Liping Wang, Fabrice Ferré, Ying Mao, Pedro W. Lamberti, Benjamine Sarton
article en
2 citations

Abstract

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.

Communications BiologyVol. 8(1)
Jagiellonian University (PL), Centre National de la Recherche Scientifique (FR), Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Inserm (FR), Universidad Nacional de Córdoba (AR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Chinese Academy of Sciences (CN), Université Paris Cité (FR), University of Cambridge (GB), Fudan University (CN), China-Japan Friendship Hospital (CN), Sorbonne Université (FR), Assistance Publique – Hôpitaux de Paris (FR), Center for Excellence in Brain Science and Intelligence Technology (CN), Achucarro Basque Center for Neuroscience (ES), Hôpital Purpan (FR), Cognitive Neuroimaging Lab (FR), Institut du Cerveau (FR), Shanghai Clinical Research Center (CN), Huashan Hospital (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Consejo Nacional de Investigaciones Científicas y Técnicas, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Agencia Nacional de Promoción Científica y Tecnológica, China-Japan Friendship Hospital, Youth Innovation Promotion Association
Openalex Percentile: Top 9%
Traumatic Brain Injury Research
2.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.