Liu Tiantian, Zhang Jian, Dong Xiaonan, Li Zhucheng, Shi Xiaorui, Tong Yizhou, Yang Ruobing, Wu Jinglong, Wang Changming, Yan Tianyi
School of Life Science, Beijing Institute of Technology, Beijing, China.
Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.
Front Psychiatry. 2019 Aug 16;10:553. doi: 10.3389/fpsyt.2019.00553. eCollection 2019.
Schizophrenia patients always show cognitive impairment, which is proved to be related to hypo-connectivity or hyper-connectivity. Further, individuals with an ultra-high risk for psychosis also show abnormal functional connectivity-related cognitive impairment, especially in the alpha rhythm. Thus, the identification of functional networks is essential to our understanding of the disorder. We investigated the resting-state functional connectivity of the alpha rhythm measured by electroencephalography (EEG) to reveal the relation between functional network and clinical symptoms. The participants included 28 patients with first-episode schizophrenia (FES), 28 individuals with ultra-high risk for psychosis (UHR), and 28 healthy controls (HC). After the professional clinical symptoms evaluation, all the participants were instructed to keep eyes closed for 3-min resting-state EEG recording. The 3-min EEG data were segmented into artefact-free epochs (the length was 3 s), and the functional connectivity of the alpha phase was estimated using the phase lag index (PLI), which measures the phase differences of EEG signals. The FES and UHR groups displayed increased resting-state PLI connectivity compared with the HC group [F(2,74) = 10.804, p < 0.001]. Significant increases in the global efficiency, the local efficiency, and the path length were found in the FES and UHR groups compared with those of the HC group. FES and UHR showed an increased degree of connectivity compared with HC. The degree of the left occipital lobe area was higher in the UHR group than in the FES group. The hypothesis of disconnection is confirmed. Furthermore, differences between the UHR and FES group were found, which is valuable for producing clinical significance before the onset of schizophrenia.
精神分裂症患者总是表现出认知障碍，事实证明这与连接不足或连接过度有关。此外，有超高精神病风险的个体也表现出与功能连接相关的异常认知障碍，尤其是在阿尔法节律方面。因此，识别功能网络对于我们理解这种疾病至关重要。我们研究了通过脑电图（EEG）测量的阿尔法节律的静息态功能连接，以揭示功能网络与临床症状之间的关系。参与者包括28名首发精神分裂症（FES）患者、28名有超高精神病风险（UHR）的个体和28名健康对照（HC）。在进行专业的临床症状评估后，所有参与者被要求闭眼进行3分钟的静息态脑电图记录。将3分钟的脑电图数据分割为无伪迹的时段（长度为3秒），并使用相位滞后指数（PLI）估计阿尔法相位的功能连接，该指数用于测量脑电图信号的相位差。与HC组相比，FES组和UHR组的静息态PLI连接性增加[F(2,74) = 10.804，p < 0.001]。与HC组相比，FES组和UHR组的全局效率、局部效率和路径长度均显著增加。FES组和UHR组与HC组相比，连接程度增加。UHR组左枕叶区域的程度高于FES组。断开连接的假设得到证实。此外，还发现了UHR组和FES组之间的差异，这对于在精神分裂症发病前产生临床意义具有重要价值。
