Murphy Michael, Jiang Chenguang, Wang Lei A, Kozhemiako Nataliia, Wang Yining, Wang Jun, Pan Jen Q, Purcell Shaun M
Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, Massachusetts.
Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, Jiangsu, China.
Biol Psychiatry Glob Open Sci. 2024 Aug 9;4(6):100371. doi: 10.1016/j.bpsgos.2024.100371. eCollection 2024 Nov.
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.
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 -means analysis to extract a set of 6 microstate topographies.
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.
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.
异常功能连接是精神分裂症的一个标志。精神分裂症中连接障碍的确切性质和机制仍不清楚，但有证据表明清醒和睡眠状态下的连接障碍有所不同。微状态分析利用脑电图（EEG），通过将EEG数据聚类成一小套反复出现的空间模式（即微状态）来研究大脑活动协调的大规模模式。我们假设这项技术将使我们能够在精细的时间分辨率下探究脑网络之间的连接性，并揭示此前未知的睡眠特异性连接障碍。
我们通过分析114例精神分裂症患者和79名对照参与者的高密度EEG睡眠数据，研究了精神分裂症患者睡眠期间的微状态。我们使用了一种不敏感极性的均值分析来提取一组6种微状态地形图。
这6种状态包括4种广泛报道的典型微状态。在患者和对照参与者中，入睡的特征是从微状态A、B和C转变为微状态D、E和F。在清醒状态下，患者的微状态F减少，在睡眠状态下，患者的微状态E减少。在清醒状态下，患者微状态转换的复杂性高于对照参与者，但在睡眠期间这种情况发生了逆转。
我们的研究结果揭示了精神分裂症中与行为状态相关的皮质连接障碍模式。此外，这些结果在很大程度上与先前精神分裂症的睡眠相关EEG标记物（如睡眠纺锤波减少）无关。因此，这些结果是由精神分裂症中先前未描述的睡眠相关病理所驱动的。
