Schizophrenia is a severe mental illness featured with complex etiology, long course, severe functional impairment, and poor prognosis; it usually occurs in early adulthood with a lifetime prevalence of approximately 1% (1, 2). This disease has been considered as one of the leading causes of disability worldwide (2, 3) and has brought a great burden to the patients, their families, as well as the health care systems (4–6). Evidence has suggested that prolonged periods of untreated psychosis can lead to adverse functional outcomes (7, 8). Therefore, a further understanding of schizophrenia in the early stage could provide information for early detection and intervention, which is crucial in reducing the risk for deterioration associated with the chronic and recurring process of the disease.

The pathophysiological hypothesis of schizophrenia has suggested that the occurrence of the disease might be due to poor connections across and within brain networks (9). Electroencephalography (EEG) is a method based on an integrative, complex, and in vivo model of brain functions, which can be used to analyze the neural synchrony associated with the pathophysiology of schizophrenia (10–12). The resting-state EEG has a high temporal resolution so that it can capture the rapid-changing dynamics of neural networks. The multichannel resting-state EEG signal can be parsed into a limited number of distinct quasi-stable states (13), which are defined by topographies of electric potentials recorded in a multichannel array over the scalp. A certain topographic map will remain stable for 80–120 ms before rapidly transitioning to another one. The quasi-stable period of this single topographic map is called EEG microstate. Four major microstate classes (i.e., class A–D) have been identified to explain a total of 65–84% of the variance of EEG data (14); they have shown high reliability and consistency across studies (15). EEG microstates were considered to be the most fundamental pattern of human neuronal functions (16) and were regarded as cornerstones of mental states in EEG data (17).

The abnormality of EEG microstates in patients with schizophrenia has been observed and reported in multiple previous studies (18–22), most of which involved patients with chronic conditions (19, 23, 24) or a mixed sample with no restriction on the course of disease (25–28). For schizophrenia, the early stage represents the transition from the premorbid to the morbid state (7); a clear diagnosis and early intervention during this period can help avoid the interference of long course and numerous attacks of the disease. Therefore, the involvement of first-episode patients with psychosis becomes necessary in studies on early-stage characteristics of schizophrenia. One study (29) found that patients with first-episode psychosis showed similar microstate dynamics as patients with chronic schizophrenia; both samples showed increased microstate C, decreased microstate D, and shortened duration of microstate B, as compared with healthy controls (HCs). However, another study (30) found an increased duration of microstate B in patients with first-episode psychosis. One possible explanation for this disparity might be the difference in medication between these studies.

Evidence suggested that some antipsychotic drugs can affect EEG microstates in both healthy individuals and patients with psychosis. The increase of microstate D has been reported in healthy individuals after the use of antipsychotic drugs (31); and in those with first-episode psychosis, some antipsychotic medications have a regulatory effect on microstate A and B, reducing the contribution of microstate A and increasing the contribution of microstate B (30). It has also been reported that antipsychotics could regulate the abnormal microstate dynamics with good efficacy (29). For instance, patients with schizophrenia showed decreased occurrence and increased duration of microstate C after the use of antipsychotic medications (22). Furthermore, antipsychotic drugs can increase the duration of microstates in a non-specific manner (31), e.g., sulpiride (32) and haloperidol (31) can increase the average duration of all microstate classes. Therefore, medication should be taken into account in studies on EEG microstates.

Koenig et al. (33) recruited drug-naïve, first-episode schizophrenia (FE-SCH) for the first time to eliminate the impact of medication. This study found that compared with HCs, the microstate class D was decreased and the occurrence of microstate A was increased. Lehmann et al. (34) subsequently repeated their study and, in addition to confirming the former results, found a decrease in the duration of microstate B and an increase in the incidence of microstate C. As the results of previous studies have been inconsistent so far. The primary aim of the current study was to replicate these finding using an independent dataset to investigate the characteristics of EEG microstates in FE-SCH and to explore the relationship between the EEG microstates and psychopathological symptoms of the patients.

The EEG activity was acquired using a 64 Brain Amp cap (Brain Products GmbH, Munich, Germany), with electrodes positioned according to the 10–20 International System and reference to linked mastoids. The vertical electrooculogram (VEOG) were recorded from the electrode beneath the right eye. The sampling frequency of the signal is 1,000 Hz and all impedances of the electrodes were kept below 10 kΩ. The data were recorded in an electrically shielded, sound-attenuated and lit room. The participants were asked to sit quietly and accommodated in the environment for about 3 min. The resting-state EEG data were collected with their eyes closed for 3–7 min.

It was an independent laboratory replication study. Through exploring the abnormalities of EEG microstates in FE-SCH, we found that, compared with HCs, the occurrence, duration, and contribution of microstate class C significantly increased, and the occurrence and contribution of microstate class D significantly decreased in patients with FE-SCH. For the relationship between clinical symptoms and EEG microstates, we found that the score of PANSS positive symptoms was negatively correlated with the occurrence of microstate class D.

Our study found increased microstate class C and decreased microstate class D in patients with FE-SCH, which is consistent with some previous studies (20, 29, 34). Such abnormalities of microstate class C and D have been identified in patients with chronic schizophrenia (19, 36), individuals with ultra-high risk for psychosis (37, 38), and even siblings of patients with schizophrenia (29). Two meta-analyses (28, 29) published in recent years showed similar results that the occurrence of microstate class C increased and the occurrence of microstate class D decreased in patients with schizophrenia. Furthermore, some researchers have proposed the dynamics of resting-state EEG microstates, especially for microstate classes C and D, as the potential endophenotype of schizophrenia (28, 29).

Studies combining functional Magnetic Resonance Imaging (fMRI) and EEG showed that the characteristics of microstates overlapped with those of resting-state networks identified using fMRI (39–41), indicating that microstates might be closely related to resting-state functional networks. It has been found that microstate class C is closely related to the default mode network (39), which consists of the medial prefrontal, parietal, and temporal cortices (42). Default mode network is the neural basis of ego (43); it was found to be activated during internally oriented mental processes and to play an important role in self-referential thoughts and episodic memory extraction (14, 44). The microstate class C might be generated at the bilateral medial temporal gyrus and the lateral parietal lobe (36), which were found to be associated with self-experience in fMRI studies (45, 46). It was also considered that microstate class C was associated with the activation of the default mode network (14, 39), the abnormal activation of which has been repeatedly demonstrated in schizophrenia (47, 48). Therefore, the increase of microstate class C might explain the abnormalities in self-focus and the self-experience in schizophrenia.

Microstate class D was found to be associated with the dorsal attention network (39). The source of microstate class D was located in the frontoparietal area (49), which was associated with the successful execution of attention-related tasks (33) and was extensively interconnected with both default mode and dorsal attention networks (50). Attention deficit has been regarded as one of the prominent symptoms of schizophrenia. Numerous studies revealed that patients with schizophrenia had obvious deficits in information processing under conditions of high processing load and distraction (33, 51, 52). In fact, patients with schizophrenia demonstrated frontal lobe dysfunction (53), as well as reduced interaction and functional integration between the prefrontal cortex and other cortical and subcortical brain structures (9), which might result in the imbalance of neural activities of different sites. Therefore, the decrease of microstate class D might suggest the impaired function of the frontoparietal and dorsal attention networks in schizophrenia.

Some studies on event-related potential found that microstates were associated with specific functions of information processing (54, 55). Microstate class C and D were found to be associated with higher and lower attention levels, respectively. Therefore, we may infer that increased microstate class C and decreased microstate class D in patients with FE-SCH in this study might indicate an abnormal state of attention distribution. These microstate abnormalities have been explained in a meta-analysis as an imbalance between processes involving saliency and processes that integrate contextual information (28). Moreover, Kikuchi et al. (22) found that successful antipsychotic treatment not only mitigated attention and executive control impairments in schizophrenia, but also regulated the patterns of microstate class C and D.

No difference was found in microstates class A and B between the two groups. Although some studies found abnormalities of these two microstate classes (33, 34), conclusion cannot be reached with the existing literature due to the heterogeneity of studies. Two meta-analyses (28, 29) showed consistent results that there was no significant difference in the parameters of microstate A and B between patients with schizophrenia and HCs. Therefore, these two microstate classes might be of limited significance in the identification of FE-SCH.

Regarding the relationship between clinical symptoms and EEG microstates, we found that the score of PANSS positive symptoms was negatively correlated with the occurrence of microstate class D. To date, there have been consistent findings about the relationship between microstate class D and positive psychotic symptoms (20, 21, 28). A study found that the duration of microstate class D was negatively correlated with the score of paranoid-hallucinatory syndromes (33). Compared with HCs, the duration of microstate class D was shorter in those with schizophrenia (20), and this effect was particularly significant in patients with acute experience of hallucinations (21). Apart from microstate class D, microstate classes C and A were also demonstrated to be associated with positive symptoms in several studies. A study involving individuals with high risk for schizophrenia found a positive association between microstate class C and positive psychotic symptoms (38), but this result has not been reproduced in studies of FE-SCH. As for microstate class A, a study found that it was positively associated with the score of PANSS positive symptoms in patients with first-episode psychosis (30), but in another study, a positive association was present with negative symptoms (26).

Microstate class D was found to be associated with the reduction of positive symptoms. A study found that the indication of its occurrence was similar to the adjustment of strategy when erroneous processing occurred (21, 56). The positive symptoms might be related to the attribution of internally generated speech error to external source, and decreased occurrence of microstate class D might be associated with decreased ability to correct such errors. In addition, decreased occurrence of microstate class D might also be related to decreased activation of dorsal attention network in patients, leading to focal attention impairments and the co-activation of functionally incompatible networks (21), thereby resulting in positive symptoms.

Some limitations need to be mentioned in this study. First, this is a cross-sectional study, which precluded us to assess the severity and progression of the disease. Second, our study still shares the common shortcoming of a small sample size with many previous studies. Due to possible sampling error and disease heterogeneity, a small sample might produce insignificance or even opposite effects, thereby leading to inaccurate conclusions. Therefore, the sample needs to be expanded in future works. Third, this study only included the four main microstate classes, which could not cover the overall variance; thus, more microstate classes need to be included in future works for a better explanation of the total variance. However, the analysis of the four major microstate classes allows us to compare our findings with previous evidence directly. The four microstate classes explained 79% of the overall variance, indicating that our method is appropriate.

In summary, our findings suggested that drug-naïve, first-episode patients with schizophrenia might be associated with increased microstate class C and decreased microstate class D. We also found a negative correlation between microstate class D and positive symptoms. These results are in line with previous studies. Furthermore, the deviant microstate class C and D might reflect the underlying pathogenesis of schizophrenia, which could provide useful information in the identification of patients with schizophrenia and the development of intervention strategies.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by the Second Xiangya Hospital. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

QS collected and analyzed the data. QS and LZ wrote the original draft. LT critically revised the manuscript and confirmed the final version of it to submit. All authors have reviewed and approved the final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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