Brain injury · 2026
Altered Temporal dynamics of resting-state EEG microstates as a neural signature of impaired consciousness: a high-density EEG analysis in chronic disorders of consciousness.
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Dong Xu, Wang Dan, Hou Jiajia, Wang Peipei, Jian Xue, Feng Tao
Аннотация
PRIMARY OBJECTIVE: To differentiate Unresponsive Wakefulness Syndrome (UWS) from Minimally Conscious State (MCS) using EEG microstates.
RESEARCH DESIGN: Retrospective analysis of high-density resting-state EEG.
METHODS AND PROCEDURES: We analyzed EEG from 20 UWS, 22 MCS patients, and 20 controls. Microstate topographies were clustered into four canonical classes. To prevent machine learning data leakage, a fixed, label-independent template basis derived solely from healthy controls was utilized for back-fitting across all individuals. After optimizing template back-fitting and systematically controlling for group-dependent spatial map distortions via class-specific spatial correlation (SC) analysis, robust temporal parameters were extracted. A strictly contained Leave-One-Subject-Out Cross-Validation (LOOCV) multivariate logistic regression model was then applied.
MAIN OUTCOMES AND RESULTS: Class D coverage progressively degraded (HC > MCS > UWS; p < 0.001) and correlated robustly with Coma Recovery Scale-Revised scores (ρ =0.84). Class C paradoxically increased in UWS. The multivariate LOOCV model accurately distinguished MCS from UWS (AUC = 0.97).
CONCLUSIONS: The selective collapse of Class D alongside Class C retention signifies severe frontoparietal decoupling. Cross-validated microstate dynamics serve as specific bedside biomarkers for prognostic stratification, empirically supporting the Global Neuronal Workspace and Mesocircuit hypotheses.
RESEARCH DESIGN: Retrospective analysis of high-density resting-state EEG.
METHODS AND PROCEDURES: We analyzed EEG from 20 UWS, 22 MCS patients, and 20 controls. Microstate topographies were clustered into four canonical classes. To prevent machine learning data leakage, a fixed, label-independent template basis derived solely from healthy controls was utilized for back-fitting across all individuals. After optimizing template back-fitting and systematically controlling for group-dependent spatial map distortions via class-specific spatial correlation (SC) analysis, robust temporal parameters were extracted. A strictly contained Leave-One-Subject-Out Cross-Validation (LOOCV) multivariate logistic regression model was then applied.
MAIN OUTCOMES AND RESULTS: Class D coverage progressively degraded (HC > MCS > UWS; p < 0.001) and correlated robustly with Coma Recovery Scale-Revised scores (ρ =0.84). Class C paradoxically increased in UWS. The multivariate LOOCV model accurately distinguished MCS from UWS (AUC = 0.97).
CONCLUSIONS: The selective collapse of Class D alongside Class C retention signifies severe frontoparietal decoupling. Cross-validated microstate dynamics serve as specific bedside biomarkers for prognostic stratification, empirically supporting the Global Neuronal Workspace and Mesocircuit hypotheses.