Abstract

Background: The brain is continuously active, even in resting state conditions, in order to prepare the system for the arrival of new internal or external stimuli. Microstate resting-state analysis is relevant for translational purposes due to the consistency of its findings and the brevity and simplicity of its registration. In brief, it consists of the analysis of four transient electrical topographies that are related to four resting-state networks: A-phonological, B-visual, C-salience and D-attentional. For statistical purposes, each microstate can be described in detail according to its occurrence (1/s), duration (ms), coverage (%) and global explained variance (GEV). Previously, microstate resting-state abnormalities have been reported in schizophrenia (SZ; Koeing et al., 1998) and 22q11 deletion syndrome subjects (Tomescu et al., 2015), which is thought to be a genetic risk group for SZ. Specifically, decreased microstate D (attentional networks) and increased microstate C (saliency) parameters, have been widely replicated in SZ microstate studies. However, little is known about this topic in ultra high-risk (UHR) syndrome (Fusar Poli et al., 2013) and its relationship with clinical symptoms. Methods: A four-minute EEG recording microstate resting-state analysis was performed in 23 UHR and 29 age and sex matched controls (CNT). UHR group was also assessed using the Structured Interview for Prodromal Syndromes (SIPS) for clinical symptoms and the MATRICS Consensus Battery (MCCB) for cognitive symptoms. UHR subjects were recruited from the first UHR cohort of Chile (Gaspar et al., 2018). 78.26% of UHR subjects were taking atypical antipsychotics. EEG recordings were pre-processed and analyzed in an open source microstate EEGLab toolbox for MATLAB (Poulsen et al., 2018). Finally, an unpaired t-test was performed to evaluate differences of each microstate statistic (duration [ms], coverage [%], occurrence [1/s] and GEV) between both groups. Pearson’s correlation was used to detect interactions between microstate statistics and SIPS/MCCCB scores. Spearman’s correlation analysis was performed to discard any antipsychotic influence (measured as chlorpromazine equivalents [mg]) in microstate results. All subjects included in the analysis signed an informed consent and the research was approved by the local ethics committee. Results: Obtained microstate topographies were similar to those found in previous studies (A, B, C, D; Lehmann et al., 2005). UHR subjects presented a decreased microstate D duration (ms) (p < 0.001), coverage (%) (p = 0.005) and GEV (p = 0.001). Also, increased microstate B coverage (%) (p < 0.003) and GEV (p < 0.001) were found. Microstate C abnormalities were not found in this study. No correlations were shown between microstate statistics and antipsychotic treatment. Additionally, microstate D coverage (%) was negatively correlated with total negative symptoms score in UHR subjects (p = 0.027; r = −0.460), and microstate D duration (ms) was negatively correlated with the speed of processing MCCB domain (p = 0.034; r = −0.445) and Total MCCB score (p = 0.018; r = −0.522). Microstate B coverage (%), however, was positively correlated with the problem-solving MCCB domain (p = 0.036; r = 0.439). Conclusions: Microstate D abnormalities previously seen in SZ were replicated in this UHR study, suggesting abnormal resting-state connectivity in core attentional areas in psychotic disorders. Negative correlations between resting-state abnormalities, negative symptoms, and Total MCCB score support this statement. Thus, this finding could represent an early SZ trait marker of potential clinical utility. On the other hand, increased coverage (%) and GEV of microstate B, related to visual networks, represents a more inconsistent finding (only noted in one previous SZ study [Kikuchi et al., 2007]), though no less interesting given its correlation with cognitive problem-solving functions. Although microstate C has also been widely reported in SZ studies, it did not show significant differences in this study. This could potentially be explained by ethnic differences or later neurodevelopmental emergence.