Spontaneous transient brain states in EEG source space in disorders of consciousness
Yang Bai,
Jianghong He,
Xiaoyu Xia,
Yong Wang,
Yi Yang,
Haibo Di,
Xiaoli Li,
Ulf Ziemann
Affiliations
Yang Bai
International Vegetative State and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China; Department of Neurology and Stroke, University of Tübingen, Hoppe-Seyler-Str. 3, Tübingen 72076, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
Jianghong He
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
Xiaoyu Xia
Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
Yong Wang
State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
Yi Yang
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
Haibo Di
International Vegetative State and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
Xiaoli Li
State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Corresponding author.
Ulf Ziemann
Department of Neurology and Stroke, University of Tübingen, Hoppe-Seyler-Str. 3, Tübingen 72076, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Corresponding author at: Department of Neurology and Stroke, University of Tübingen, Hoppe-Seyler-Str. 3, Tübingen 72076, Germany.
Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment.
