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Zhian Liu

2 papers in the library · publishing 2020-2022

Papers

Brain connectivity changes of propofol-induced altered states of consciousness using High-Density EEG Source Estimation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference July 1, 2022 Zhian Liu, Lichengxi Si, Tianyu Wang et al.

By converting high-density electroencephalogram (EEG) signals recorded from the scalp into cortical signals using source estimation, researchers examined how propofol alters consciousness. In 20 healthy adults, they filtered alpha-band activity and calculated pairwise orthogonal power envelope connectivity (PEC) across 68 brain regions. A statistical method (LASSO) identified the fewest PECs needed to distinguish baseline from moderate sedation. Most of those PECs involved regions of the default mode network, and changes in thalamocortical and frontal-parietal connectivity matched those seen with direct neuroimaging. A classifier based on the selected PECs achieved over 70% accuracy in distinguishing the two states, suggesting this approach could aid future anesthesia depth monitoring.

Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness

International Journal of Neural Systems January 23, 2020 Wen Shi, Yamin Li, Zhian Liu et al.

During propofol-induced sedation, a distinct EEG microstate pattern—a posterior central maximum labeled microstate F—emerges and becomes prominent. Its coverage, occurrence, and power significantly increase in moderate sedation, and the transition from rest to sedation is accompanied by a significant rise in mean energy across all frequency bands in this microstate. The findings suggest that microstate F is closely linked to propofol-altered consciousness and may derive from the canonical anterior-posterior microstate C. The work also advances methods for analyzing microstates in the frequency domain using multivariate empirical mode decomposition and Hilbert-Huang transform.