Schizophrenia patients and 22q11.2 deletion syndrome adolescents at risk express the same deviant patterns of resting state EEG microstates: A candidate endophenotype of schizophrenia

• Main Authors: Miralena I. Tomescu, Tonia A. Rihs, Maya Roinishvili, F. Isik Karahanoglu, Maude Schneider, Sarah Menghetti, Dimitri Van De Ville, Andreas Brand, Eka Chkonia, Stephan Eliez, Michael H. Herzog, Christoph M. Michel, Céline Cappe
• Format: Article
• Language: English
• Published: Elsevier 2015-09-01
• Series: Schizophrenia Research: Cognition
• Subjects: Schizophrenia; 22q11.2 deletion syndrome; Endophenotype; Microstates; Resting-state
• Online Access: http://www.sciencedirect.com/science/article/pii/S2215001315000153

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.