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Yun Zhang

3 papers in the library · 766 citations · publishing 2019-2026

Papers

Efficacy of Esketamine Nasal Spray Plus Oral Antidepressant Treatment for Relapse Prevention in Patients With Treatment-Resistant Depression

JAMA Psychiatry June 5, 2019 Ella Daly, Madhukar H. Trivedi, Adam Janik et al. 766 citations

For adults with treatment-resistant depression who achieved stable remission or response after 16 weeks of esketamine nasal spray plus an oral antidepressant, continuing esketamine plus the antidepressant delayed relapse significantly more than switching to placebo plus the antidepressant. Among those in stable remission, 26.7% relapsed on esketamine versus 45.3% on placebo, a 51% reduction in relapse risk. Among stable responders, 25.8% relapsed on esketamine versus 57.6% on placebo, a 70% reduction. Common side effects of esketamine included transient taste disturbance, vertigo, dissociation, drowsiness, and dizziness.

Frequency-specific microstate correlates of ciprofol-induced alterations of consciousness.

Frontiers in Neuroscience January 1, 2026 Fei Yan, Yansong Li, Ni Xiong et al.

Ciprofol, a GABAergic intravenous anesthetic, induces a frequency-specific reorganization of brain network dynamics measurable via EEG microstates. During loss of consciousness, delta and alpha power increased; alpha-band microstates showed reduced duration and increased occurrence, while microstate D in delta-theta bands decreased. Recovery of consciousness was marked by elevated beta-gamma activity and altered microstates A, C, and E. Machine learning classifiers distinguished conscious states with highest accuracy (0.971) when combining features from all frequency bands, compared to broadband features alone (0.754). The findings suggest that frequency-resolved microstate metrics may serve as sensitive markers of anesthetic-induced brain state transitions.

Differentiating propofol-induced altered states of consciousness using features of EEG microstates

Biomedical Signal Processing and Control February 1, 2021 Haidong Wang, Yubo Wang, Yun Zhang et al.

During propofol-induced unconsciousness, the brain's electrical activity patterns—EEG microstates—change in ways that can distinguish between resting, light, and deep anesthesia. Analyzing 60-channel EEG from 31 male subjects, the study identified 7 common and 2 anesthesia-specific microstate templates. Features such as the occurrence and duration of certain microstates decreased as consciousness suppression deepened. Machine learning models using these features classified the three consciousness levels with a mean accuracy of 85.6%. The findings suggest that microstate analysis may help reveal the neural mechanisms underlying propofol anesthesia and could provide useful markers for quantifying levels of consciousness.