EEG-Based Brain Functional Connectivity in First-Episode Schizophrenia Patients, Ultra-High-Risk Individuals, and Healthy Controls During P50 Suppression

Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Biomarker discovery using machine learning in the psychosis spectrum

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

Novel N100 area reliably captures aberrant sensory processing and is associated with neurocognition in early psychosis

BACKGROUND

Despite findings from translational and genetic studies in the event-related potential (ERP) literature, the validity and reliability of P50 suppression as a schizophrenia spectrum endophenotype has been questioned. Here, we aimed to examine sensory registration and gating measures derived from P50 and N100 amplitude, as well as N100 area-a novel approach proposed herein-in early psychosis versus health.

METHODS

Individuals at clinical high risk for psychosis (CHR; n = 77), first-episode psychosis (FE; n = 52), and healthy controls (HC; n = 65) were assessed in a paired-click auditory ERP paradigm. Eight CHR converted to psychosis (CHRC) and 39 did not (CHR-NC) by 24 months, while 30 CHR were lost to follow-. Group differences, test-retest reliability, and associations with neurocognitive function were assessed in nine ERP measures.

RESULTS

Significant differences were observed in N100 S1 amplitude, S1 area, and area difference between HC and FE, as well as in N100 S1 area between HC and CHR, among the total population. Furthermore, significant differences were found in N100 S1 area between HC and CHR-NC (Cliff's delta, Δ = 0.32), as well as in N100 area difference between HC and CHR-C (Δ = 0.55). Both N100 S1 area and area difference demonstrated moderate to acceptable reliability (intraclass correlation coefficients: 0.61-0.78). Processing speed negatively correlated with both N100 S1 area and area difference, while executive function negatively correlated with N100 S1 area alone in CHR and FE.

CONCLUSION

Among the ERP measures studied, N100 area measures may serve as a reliable biomarker of aberrant sensory processing and neurocognition in early psychosis.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Association of P50 with social function, but not with cognition in patients with first-episode schizophrenia

Functional deficits including cognitive impairment and social dysfunction are the core symptoms of schizophrenia (SCZ), and sensory gating (SG) deficits may be involved in the pathological mechanism of functional deficits in SCZ. This study was to investigate the relationship between defective P50 inhibition and functional deficits in first-episode drug naïve (FEDN) SCZ patients. A total of 95 FEDN SCZ patients and 53 healthy controls (HC) were recruited. The Chinese version of UCSD Performance-Based Skills (UPSA), MATRICS Consensus Cognitive Battery (MCCB), and EEG system were used to assess the social function, cognitive performance, and P50 inhibition, respectively. The MCCB total score and eight domain scores were significantly lower in patients with FEDN SCZ than those in HC (all p < 0.05). The UPSA total score and financial skills scores were also significantly lower in SCZ patients than that in the HC (all p < 0.05). Compared with HC, patients with FEDF SCZ had a higher P50 ratio (all p < 0.05). There was no correlation between P50 components and MCCB scores in patients with FEDF SCZ. However, there was only a correlation between the P50 ratio and UPSA financial skills, communication skills, or total score in patients (all p < 0.05). Defective P50 inhibition in FEDN SCZ patients may be associated with social dysfunction but not cognitive impairment, suggesting that the social dysfunction and cognitive impairment of patients with FEDN SCZ may have different pathogenic mechanisms.

Use of the Chinese version of the MATRICS Consensus Cognitive Battery to assess cognitive functioning in individuals with high risk for psychosis, first-episode schizophrenia and chronic schizophrenia: a systematic review and meta-analysis

More than one hundred studies have used the mainland Chinese version of the MATRICS Consensus Cognitive Battery (MCCB) to assess cognition in schizophrenia, but the results of these studies, the quality of the reports, and the strength of the evidence provided in the reports have not been systematically assessed. We identified 114 studies from English-language and Chinese-language databases that used the Chinese MCCB to assess cognition in combined samples of 7394 healthy controls (HC), 392 individuals with clinical high risk for psychosis (CHR-P), 4922 with first-episode schizophrenia (FES), 1549 with chronic schizophrenia (CS), and 2925 with schizophrenia of unspecified duration. The mean difference (MD) of the composite MCCB T-score (-13.72) and T-scores of each of the seven cognitive domains assessed by MCCB (-14.27 to -7.92) were significantly lower in individuals with schizophrenia than in controls. Meta-analysis identified significantly greater cognitive impairment in FES and CS than in CHR-P in six of the seven domains and significantly greater impairment in CS than FES in the reasoning and problem-solving domain (i.e., executive functioning). The only significant covariate of overall cognitive functioning in individuals with schizophrenia was a negative association with the severity of psychotic symptoms. These results confirm the construct validity of the mainland Chinese version of MCCB. However, there were significant limitations in the strength of the evidence provided about CHR-P (small pooled sample sizes) and the social cognition domain (inconsistency of results across studies), and the quality of many reports (particularly those published in Chinese) was rated 'poor' due to failure to report sample size calculations, matching procedures or methods of handling missing data. Moreover, almost all studies were cross-sectional studies limited to persons under 60 with at least nine years of education, so longitudinal studies of under-educated, older individuals with schizophrenia are needed.

Sex difference in association between cognitive and P50 deficits in patients with chronic schizophrenia

A large number of studies have reported that sensory gating disorders represented by P50 inhibition may be involved in the pathophysiological process of schizophrenia. However, few studies have explored the relationship between sensory gating disorders and cognitive dysfunction in patients with schizophrenia. This study aimed to explore sex differences in the relationship between cognitive and P50 deficits in patients with chronic schizophrenia, which has not been reported. A total of 183 chronic schizophrenia patients (128 males and 55 females) and 166 healthy controls (76 males and 90 females) participated in this study. The MATRICS Consensus Cognitive Battery (MCCB) was measured for cognitive function and P50 components for the sensory gating in all participants. The Positive and Negative Syndrome Scales (PANSS) was used to assess the psychopathological symptoms in patients. Female patients performed significantly better than male patients in several cognitive domains of MCCB (all p < 0.01). There were no significant differences in P50 components between male and female patients (all p > 0.05). Further analysis showed that in female patients, latency of S2 was negatively correlated with reasoning and problem-solving domain of MCCB (p < 0.05), and P50 ratio was negatively correlated with social cognition domain of MCCB (p < 0.05). In male patients, there was no any correlation between P50 and cognitive domains of MCCB. Our results suggest that there is a sex difference in the association between P50 deficiency and cognitive impairment in Chinese Han patients with schizophrenia.

Sensory gating, neurocognition, social cognition and real-life functioning: a 2-year follow-up of early psychosis

BACKGROUND

Diminished sensory gating (SG) is a robust finding in psychotic disorders, but studies of early psychosis (EP) are rare. It is unknown whether SG deficit leads to poor neurocognitive, social, and/or real-world functioning. This study aimed to explore the longitudinal relationships between SG and these variables.

METHODS

Seventy-nine EP patients and 88 healthy controls (HCs) were recruited at baseline. Thirty-three and 20 EP patients completed 12-month and 24-month follow-up, respectively. SG was measured using the auditory dual-click (S1 & S2) paradigm and quantified as P50 ratio (S2/S1) and difference (S1-S2). Cognition, real-life functioning, and symptoms were assessed using the MATRICS Consensus Cognitive Battery, Global Functioning: Social (GFS) and Role (GFR), Multnomah Community Ability Scale (MCAS), Awareness of Social Inference Test (TASIT), and the Positive and Negative Syndrome Scale (PANSS). Analysis of variance (ANOVA), chi-square, mixed model, correlation and regression analyses were used for group comparisons and relationships among variables controlling for potential confounding variables.

RESULTS

In EP patients, P50 ratio (p < 0.05) and difference (p < 0.001) at 24-month showed significant differences compared with that at baseline. At baseline, P50 indices (ratio, S1-S2 difference, S1) were independently associated with GFR in HCs (all p < 0.05); in EP patients, S2 amplitude was independently associated with GFS (p = 0.037). At 12-month and 24-month, P50 indices (ratio, S1, S2) was independently associated with MCAS (all p < 0.05). S1-S2 difference was a trending predictor of future function (GFS or MCAS).

CONCLUSIONS

SG showed progressive reduction in EP patients. P50 indices were related to real-life functioning.

P50 inhibition defects, psychopathology and gray matter volume in patients with first-episode drug-naive schizophrenia

BACKGROUND

Sensory gating deficits and gray matter volume (GMV) abnormalities have been found to be associated with the pathogenesis and psychopathology of patients with schizophrenia (SCZ). However, no studies have investigated their interrelationship in first-episode treatment-naive (FETN) SCZ patients.

METHODS

We recruited 52 FETN SCZ patients and 57 healthy controls. The Positive and Negative Syndrome Scale (PANSS) was used to measure the psychopathology of the patients. We collected magnetic resonance imaging and P50 inhibition data from all participants.

RESULTS

Compared to healthy controls, patients had shorter S1 and S2 latencies but larger S2 amplitudes and P50 ratio (Bonferroni adjusted all p < 0.01). In patients, S2 latency was independently associated with PANSS total score, negative symptoms and general psychopathology (t = 2.26-2.58, both P < 0.05), whereas S1 (t = 2.44, P < 0.05) and S2 latencies (t = 2.13, P < 0.05) were associated with PANSS cognitive factor. Moreover, GMV in the left inferior temporal gyrus, left lingual gyrus and right superior occipital gyrus, and bilateral dorsolateral superior frontal gyrus were each associated with the P50 components (all p < 0.05). In addition, GMV associated with S2 latency was negatively correlated with PANSS general psychopathology (t = -2.46, p < 0.05) and total score (t = -2.34, p < 0.05).

CONCLUSIONS

Our findings indicate that FETN SCZ patients exhibit deficits in P50 inhibition and GMV of brain regions associated with these deficits may be associated with their psychopathological symptoms, suggesting that brain structures associated with P50 components may be important biomarkers of SCZ psychopathology. Future studies could use a prospective longitudinal design to investigate the potential causal relationship of brain structures associated with P50 components in the psychopathological symptoms of SCZ patients.

Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.

Neurophysiology in psychosis: The quest for disease biomarkers

Psychotic disorders affect 3% of the population at some stage in life, are a leading cause of disability, and impose a great economic burden on society. Major breakthroughs in the genetics of psychosis have not yet been matched by an understanding of its neurobiology. Biomarkers of perception and cognition obtained through non-invasive neurophysiological tools, especially EEG, offer a unique opportunity to gain mechanistic insights. Techniques for measuring neurophysiological markers are inexpensive and ubiquitous, thus having the potential as an accessible tool for patient stratification towards early treatments leading to better outcomes. In this paper, we review the literature on neurophysiological markers for psychosis and their relevant disease mechanisms, mainly covering event-related potentials including P50/N100 sensory gating, mismatch negativity, and the N100 and P300 waveforms. While several neurophysiological deficits are well established in patients with psychosis, more research is needed to study neurophysiological markers in their unaffected relatives and individuals at clinical high risk. We need to harness EEG to investigate markers of disease risk as key steps to elucidate the aetiology of psychosis and facilitate earlier detection and treatment.

Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity

Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.

Dynamic brain functional network based on EEG microstate during sensory gating in schizophrenia

OBJECTIVE

Cognitive impairment is one of the core symptoms of schizophrenia, with an emphasis on dysfunctional information processing. Sensory gating deficits have consistently been reported in schizophrenia, but the underlying physiological mechanism is not well-understood. We report the discovery and characterization of P50 dynamic brain connections based on microstate analysis.

APPROACH

We identify five main microstates associated with the P50 response and the difference between the first and second click presentation (S1-S2-P50) in first-episode schizophrenia patients (FESZ), ultra-high-risk individuals (UHR) and healthy controls (HC). The we used the signal segments composed of consecutive time points with the same microstate label to construct brain functional networks.

MAIN RESULTS

The microstate with a prefrontal extreme location during the response to the S1 of P50 are statistically different in duration, occurrence and coverage among the FESZ, UHR and HC groups. In addition, a microstate with anterior-posterior orientation was found to be associated with S1-S2-P50 and its coverage was found to differ among the FESZ, UHR and HC groups. Source location of microstates showed that activated brain regions were mainly concentrated in the right temporal lobe. Furthermore, the connectivities between brain regions involved in P50 processing of HC were widely different from those of FESZ and UHR.

SIGNIFICANCE

Our results indicate that P50 suppression deficits in schizophrenia may be due to both aberrant baseline sensory perception and adaptation to repeated stimulus. Our findings provide new insight into the mechanisms of P50 suppression in the early stage of schizophrenia.

Sex differences in P50 inhibition defects with psychopathology and cognition in patients with first-episode schizophrenia

BACKGROUND

A large number of studies have shown that the pathophysiology of schizophrenia may be involved in sensory gating that appears to be P50 inhibition. However, few studies have investigated the relationship between clinical symptoms, cognitive impairment and sensory gating disorders in patients with first-episode schizophrenia. The purpose of this study was to explore the sex differences in the relationship between clinical symptoms, cognitive impairment and P50 inhibition defects in patients with first-episode schizophrenia, which has not been reported.

METHODS

130 patients with first-episode schizophrenia (53 males and 77 females) and 189 healthy controls (87 males and 102 females) participated in the study. Positive and Negative Syndrome Scale (PANSS) was used to evaluate the patients' psychopathological symptoms, and the 64-channel electroencephalogram (EEG) system was used to record the P50 inhibition.

RESULTS

Male patients had higher PANSS negative symptom, general psychopathology, cognitive factor and total scores than female patients (all p < 0.01). The S1 amplitude was smaller in male than female patients (all p < 0.05). Multiple regression analysis showed that in male patients, S1 latency was contributor to negative symptoms, while S1 latency, S2 latency, age, and smoking status were contributors to cognitive factor (all p < 0.05). In female patients, no P50 component was found to be an independent contributor to PANSS scores (all p > 0.05).

CONCLUSIONS

Our results indicate that there is a sex difference in the relationship between clinical symptoms, cognitive impairment and P50 inhibition defects in Chinese Han patients with first-episode schizophrenia.

Schizophrenia Classification Using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level

Disrupted functional and structural connectivity measures have been used to distinguish schizophrenia patients from healthy controls. Classification methods based on functional connectivity derived from EEG signals are limited by the volume conduction problem. Recorded time series at scalp electrodes capture a mixture of common sources signals, resulting in spurious connections. We have transformed sensor level resting state EEG times series to source level EEG signals utilizing a source reconstruction method. Functional connectivity networks were calculated by computing phase lag values between brain regions at both the sensor and source level. Brain complex network analysis was used to extract features and the best features were selected by a feature selection method. A logistic regression classifier was used to distinguish schizophrenia patients from healthy controls at five different frequency bands. The best classifier performance was based on connectivity measures derived from the source space and the theta band.The transformation of scalp EEG signals to source signals combined with functional connectivity analysis may provide superior features for machine learning applications.

P50 inhibition defects with psychopathology and cognitive impairment in patients with first-episode drug naïve schizophrenia

BACKGROUND

Many studies have announced that P50 inhibition defects represent sensory gating deficits in schizophrenia, but studies seldom have searched the correlation between P50 inhibition defects and the psychopathology or cognitive impairment of patients with first-episode, drug naïve (FEDN) of schizophrenia. In this study, we investigated the auditory sensory gating deficits in a large number of Han patients with FEDN schizophrenia and their correlation with clinical symptoms and cognitive impairment.

METHODS

A total of 130 patients with FEDN schizophrenia and 189 healthy controls were recruited in this study. Positive and Negative Syndrome Scale (PANSS) and its five-factor model were used to score the psychopathology of the patients, and P50 inhibition was recorded using the 64-channel electroencephalography (EEG) system.

RESULTS

Patients exhibited significantly longer S1 and S2 latency, lower S1 and S2 amplitudes and lower P50 difference than healthy controls (all p < 0.05). Significant correlations existed between S1 latency and PANSS negative symptoms or cognitive factor, P50 ratio and general psychopathology, P50 ratio and PANSS total score, P50 difference and general psychopathology, and P50 difference and PANSS total score (all p < 0.05). Multiple regression analysis revealed that S1 latency, sex, age, and education were contributors to negative symptom score (all p < 0.05). S1 latency, S2 latency, sex, age, and smoking status were contributors to cognitive factor (all p < 0.05).

CONCLUSIONS

Our results show that patients with FEDN schizophrenia have P50 inhibition defects, which may be related to their psychopathological symptoms and cognitive impairment.

Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls

The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients' quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.

EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia. Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis. Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands. Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.