Electroencephalographic Microstates During Sleep and Wake in Schizophrenia

Michael Murphy , Chenguang Jiang , Lei A. Wang , Nataliia Kozhemiako , Yining Wang
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Abstract

Background

Aberrant functional connectivity is a hallmark of schizophrenia. The precise nature and mechanism of dysconnectivity in schizophrenia remains unclear, but evidence suggests that dysconnectivity is different in wake versus sleep. Microstate analysis uses electroencephalography (EEG) to investigate large-scale patterns of coordinated brain activity by clustering EEG data into a small set of recurring spatial patterns, or microstates. We hypothesized that this technique would allow us to probe connectivity between brain networks at a fine temporal resolution and uncover previously unknown sleep-specific dysconnectivity.

Methods

We studied microstates during sleep in patients with schizophrenia by analyzing high-density EEG sleep data from 114 patients with schizophrenia and 79 control participants. We used a polarity-insensitive k-means analysis to extract a set of 6 microstate topographies.

Results

These 6 states included 4 widely reported canonical microstates. In patients and control participants, falling asleep was characterized by a shift from microstates A, B, and C to microstates D, E, and F. Microstate F was decreased in patients during wake, and microstate E was decreased in patients during sleep. The complexity of microstate transitions was greater in patients than control participants during wake, but this reversed during sleep.

Conclusions

Our findings reveal behavioral state–dependent patterns of cortical dysconnectivity in schizophrenia. Furthermore, these findings are largely unrelated to previous sleep-related EEG markers of schizophrenia such as decreased sleep spindles. Therefore, these findings are driven by previously undescribed sleep-related pathology in schizophrenia.

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精神分裂症患者睡眠和觉醒时的脑电微观状态
背景功能连接异常是精神分裂症的一个特征。精神分裂症患者功能连接障碍的确切性质和机制仍不清楚,但有证据表明,清醒时与睡眠时的功能连接障碍是不同的。微状态分析利用脑电图(EEG)将脑电图数据聚类为一小组重复出现的空间模式或微状态,从而研究大脑活动的大规模协调模式。我们假设这种技术将使我们能够以精细的时间分辨率探查大脑网络之间的连接性,并发现之前未知的睡眠特异性连接障碍。结果这6种状态包括4种广泛报道的典型微状态。在患者和对照组参与者中,入睡的特征是从微状态 A、B 和 C 到微状态 D、E 和 F 的转变。我们的研究结果揭示了精神分裂症患者大脑皮层连接障碍的行为状态依赖模式。此外,这些发现在很大程度上与以往与睡眠相关的精神分裂症脑电图标记(如睡眠棘波减少)无关。因此,这些发现是由以前未曾描述过的精神分裂症与睡眠相关的病理现象所驱动的。
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来源期刊
Biological psychiatry global open science
Biological psychiatry global open science Psychiatry and Mental Health
CiteScore
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审稿时长
91 days
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