Tomescu Miralena I, Rihs Tonia A, Roinishvili Maya, Karahanoglu F Isik, Schneider Maude, Menghetti Sarah, Van De Ville Dimitri, Brand Andreas, Chkonia Eka, Eliez Stephan, Herzog Michael H, Michel Christoph M, Cappe Céline
Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience, Geneva, Switzerland.
Institute of Cognitive Neurosciences, Agricultural University of Georgia, Tbilisi, Georgia.
Schizophr Res Cogn. 2015 May 27;2(3):159-165. doi: 10.1016/j.scog.2015.04.005. eCollection 2015 Sep.
Schizophrenia is a complex psychiatric disorder and many of the factors contributing to its pathogenesis are poorly understood. In addition, identifying reliable neurophysiological markers would improve diagnosis and early identification of this disease. The 22q11.2 deletion syndrome (22q11DS) is one major risk factor for schizophrenia. Here, we show further evidence that deviant temporal dynamics of EEG microstates are a potential neurophysiological marker by showing that the resting state patterns of 22q11DS are similar to those found in schizophrenia patients. The EEG microstates are recurrent topographic distributions of the ongoing scalp potential fields with temporal stability of around 80 ms that are mapping the fast reconfiguration of resting state networks. Five minutes of high-density EEG recordings was analysed from 27 adult chronic schizophrenia patients, 27 adult controls, 30 adolescents with 22q11DS, and 28 adolescent controls. In both patient groups we found increased class C, but decreased class D presence and high transition probabilities towards the class C microstates. Moreover, these aberrant temporal dynamics in the two patient groups were also expressed by perturbations of the long-range dependency of the EEG microstates. These findings point to a deficient function of the salience and attention resting state networks in schizophrenia and 22q11DS as class C and class D microstates were previously associated with these networks, respectively. These findings elucidate similarities between individuals at risk and schizophrenia patients and support the notion that abnormal temporal patterns of EEG microstates might constitute a marker for developing schizophrenia.
精神分裂症是一种复杂的精神疾病，许多导致其发病机制的因素仍未被充分了解。此外，识别可靠的神经生理标志物将有助于改善该疾病的诊断和早期识别。22q11.2缺失综合征（22q11DS）是精神分裂症的一个主要风险因素。在此，我们通过表明22q11DS的静息状态模式与精神分裂症患者的模式相似，进一步证明脑电图微状态的异常时间动态是一种潜在的神经生理标志物。脑电图微状态是正在进行的头皮电位场的反复出现的地形分布，时间稳定性约为80毫秒，反映了静息状态网络的快速重新配置。对27名成年慢性精神分裂症患者、27名成年对照、30名患有22q11DS的青少年以及28名青少年对照进行了5分钟的高密度脑电图记录分析。在两个患者组中，我们都发现C类微状态增加，但D类微状态出现减少，且向C类微状态的转换概率很高。此外，这两个患者组中的这些异常时间动态也通过脑电图微状态的长程依赖性扰动表现出来。这些发现表明，精神分裂症和22q11DS中突显和注意力静息状态网络功能存在缺陷，因为C类和D类微状态先前分别与这些网络相关。这些发现阐明了有风险个体与精神分裂症患者之间的相似性，并支持脑电图微状态的异常时间模式可能构成精神分裂症发病标志物的观点。
