Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis? A prospective multicenter study

An electroencephalographic signature predicts craving for methamphetamine

Craving is central to methamphetamine use disorder (MUD) and both characterizes the disease and predicts relapse. However, there is currently a lack of robust and reliable biomarkers for monitoring craving and diagnosing MUD. Here, we seek to identify a neurobiological signature of craving based on individual-level functional connectivity pattern differences between healthy control and MUD subjects. We train high-density electroencephalography (EEG)-based models using data recorded during the resting state and then calculate imaginary coherence features between the band-limited time series across different brain regions of interest. Our prediction model demonstrates that eyes-open beta functional connectivity networks have significant predictive value for craving at the individual level and can also identify individuals with MUD. These findings advance the neurobiological understanding of craving through an EEG-tailored computational model of the brain connectome. Dissecting neurophysiological features provides a clinical avenue for personalized treatment of MUD.

Neuronal oscillations: early biomarkers of psychiatric disease?

Our understanding of the environmental and genetic factors contributing to the wide spectrum of neuropsychiatric disorders has significantly increased in recent years. Impairment of neuronal network activity during early development has been suggested as a contributor to the emergence of neuropsychiatric pathologies later in life. Still, the neurobiological substrates underlying these disorders remain yet to be fully understood and the lack of biomarkers for early diagnosis has impeded research into curative treatment options. Here, we briefly review current knowledge on potential biomarkers for emerging neuropsychiatric disease. Moreover, we summarize recent findings on aberrant activity patterns in the context of psychiatric disease, with a particular focus on their potential as early biomarkers of neuropathologies, an essential step towards pre-symptomatic diagnosis and, thus, early intervention.

Tensor factorization approach for ERP-based assessment of schizotypy in a novel auditory oddball task on perceived family stress

Objective. Schizotypy, a potential phenotype for schizophrenia, is a personality trait that depicts psychosis-like signs in the normal range of psychosis continuum. Family communication may affect the social functioning of people with schizotypy. Greater family stress, such as irritability, criticism and less praise, is perceived at a higher level of schizotypy. This study aims to determine the differences between people with high and low levels of schizotypy using electroencephalography (EEG) during criticism, praise and neutral comments. EEGs were recorded from 29 participants in the general community who varied from low schizotypy to high schizotypy (HS) during a novel emotional auditory oddball task.Approach. We consider the difference in event-related potential parameters, namely the amplitude and latency of P300 subcomponents (P3a and P3b), between pairs of target words (standard, positive, negative and neutral). A model based on tensor factorization is then proposed to detect these components from the EEG using the CANDECOMP/PARAFAC decomposition technique. Finally, we employ the mutual information estimation method to select influential features for classification.Main results.The highest classification accuracy, sensitivity, and specificity of 93.1%, 94.73%, and 90% are obtained via leave-one-out cross validation.Significance. This is the first attempt to investigate the identification of individuals with psychometrically-defined HS from brain responses that are specifically associated with perceiving family stress and schizotypy. By measuring these brain responses to social stress, we achieve the goal of improving the accuracy in detection of early episodes of psychosis.

Diagnostic Value of the Electroencephalogram and Cerebrospinal Fluid in Viral Encephalitis

BACKGROUND

Electroencephalogram (EEG) and cerebrospinal fluid (CSF) are widely used in the clinical diagnosis of viral encephalitis (VE), but their value in the diagnosis of VE and the detection rate of abnormal indicators need to be further supported by more clinical data.

METHODS

In this study, routine laboratory testing, biochemical examinations of cerebrospinal fluid (CSF) and EEG characteristics were performed in patients with VE to guide the diagnosis and treatment of VE in clinical settings. A total of 330 patients with VE were enrolled in the Department of Neurology of the Third Affiliated Hospital of Zunyi Medical University from January 1, 2015 to January 30, 2020. EEG, routine testing and assessment of biochemical indicators of CSF were performed within 10 days after admission, and the results were analyzed by paired χ 2 test to compare the diagnostic value of EEG and CSF for VE.

RESULTS

In 330 cases of VE, 283 cases (85.76%) had abnormal EEG, and 189 cases (57.27%) had abnormal CSF indicators. The incidence of EEG abnormalities was higher than that of CSF indicators, and the difference was statistically significant ( P <0.05).

CONCLUSIONS

Both the EEG and CSF analysis are valuable indicators in the diagnosis of VE patients. Compared with the CSF examination, the EEG examination had a better diagnostic efficacy for the diagnosis of VE. In addition, a normal EEG or a normal CSF level cannot exclude VE, and it is still necessary to develop new diagnostic indicators to cover all viral encephalitides.

Machine learning based prediction and the influence of complement - Coagulation pathway proteins on clinical outcome: Results from the NEURAPRO trial

BACKGROUND

Functional outcomes are important measures in the overall clinical course of psychosis and individuals at clinical high-risk (CHR), however, prediction of functional outcome remains difficult based on clinical information alone. In the first part of this study, we evaluated whether a combination of biological and clinical variables could predict future functional outcome in CHR individuals. The complement and coagulation pathways have previously been identified as being of relevance to the pathophysiology of psychosis and have been found to contribute to the prediction of clinical outcome in CHR participants. Hence, in the second part we extended the analysis to evaluate specifically the relationship of complement and coagulation proteins with psychotic symptoms and functional outcome in CHR.

MATERIALS AND METHODS

We carried out plasma proteomics and measured plasma cytokine levels, and erythrocyte membrane fatty acid levels in a sub-sample (n = 158) from the NEURAPRO clinical trial at baseline and 6 months follow up. Functional outcome was measured using Social and Occupational Functional assessment Score (SOFAS) scale. Firstly, we used support vector machine learning techniques to develop predictive models for functional outcome at 12 months. Secondly, we developed linear regression models to understand the association between 6-month follow-up levels of complement and coagulation proteins with 6-month follow-up measures of positive symptoms summary (PSS) scores and functional outcome.

RESULTS AND CONCLUSION

A prediction model based on clinical and biological data including the plasma proteome, erythrocyte fatty acids and cytokines, poorly predicted functional outcome at 12 months follow-up in CHR participants. In linear regression models, four complement and coagulation proteins (coagulation protein X, Complement C1r subcomponent like protein, Complement C4A & Complement C5) indicated a significant association with functional outcome; and two proteins (coagulation factor IX and complement C5) positively associated with the PSS score. Our study does not provide support for the utility of cytokines, proteomic or fatty acid data for prediction of functional outcomes in individuals at high-risk for psychosis. However, the association of complement protein levels with clinical outcome suggests a role for the complement system and the activity of its related pathway in the functional impairment and positive symptom severity of CHR patients.

Thalamocortical dysrhythmia in patients with schizophrenia spectrum disorder and individuals at clinical high risk for psychosis

Thalamocortical dysrhythmia (TCD) is a model characterized by abnormal resting-state thalamic oscillatory patterns where the alpha rhythm is replaced by cross-frequency coupling of low- and high-frequency rhythms. Although disrupted thalamic function is a suggested important pathophysiological mechanism underlying schizophrenia, knowledge regarding the TCD model in schizophrenia spectrum disorder (SSD) patients and individuals at clinical high risk (CHR) for psychosis is limited. A total of 169 SSD patients, 106 individuals at CHR for psychosis, and 105 healthy controls (HCs) underwent resting-state electroencephalography recordings. We performed mean global field power (MGFP) spectral analysis between 1 and 49 Hz as well as source-level theta phase-gamma amplitude coupling (TGC) analysis and compared resting-state oscillatory patterns across groups. Correlations between altered TGC values and psychotic symptom severity in the patient group were investigated. Spectral MGFP of low- and high-frequencies was larger in the SSD and CHR groups than in the HC group. The TGC of SSD patients was greater than that of HCs in the right frontal, right parietal, and left and right limbic lobes. Greater TGC in the right frontal and limbic lobes was associated with positive symptom severity in SSD patients. However, TGC in the CHR group was comparable to that in the HCs and was smaller than that in the SSD group in widespread cortical regions. The TCD pattern may be apparent after frank psychotic disorder onset in tandem with overt positive symptoms. A psychosis-risk state without overt psychotic symptoms could be characterized by abnormally increased low- and high-frequency activities with relatively preserved TGC.

Identifying Electroencephalography Biomarkers in Individuals at Clinical High Risk for Psychosis in an International Multi-Site Study

BACKGROUND

The clinical high-risk for psychosis (CHR-P) paradigm was introduced to detect individuals at risk of developing psychosis and to establish preventive strategies. While current prediction of outcomes in the CHR-P state is based mostly on the clinical assessment of presenting features, several emerging biomarkers have been investigated in an attempt to stratify CHR-P individuals according to their individual trajectories and refine the diagnostic process. However, heterogeneity across subgroups is a key challenge that has limited the impact of the CHR-P prediction strategies, as the clinical validity of the current research is limited by a lack of external validation across sites and modalities. Despite these challenges, electroencephalography (EEG) biomarkers have been studied in this field and evidence suggests that EEG used in combination with clinical assessments may be a key measure for improving diagnostic and prognostic accuracy in the CHR-P state. The PSYSCAN EEG study is an international, multi-site, multimodal longitudinal project that aims to advance knowledge in this field.

METHODS

Participants at 6 international sites take part in an EEG protocol including EEG recording, cognitive and clinical assessments. CHR-P participants will be followed up after 2 years and subcategorised depending on their illness progression regarding transition to psychosis. Differences will be sought between CHR-P individuals and healthy controls and between CHR-P individuals who transition and those who do not transition to psychosis using data driven computational analyses.

DISCUSSION

This protocol addresses the challenges faced by previous studies of this kind to enable valid identification of predictive EEG biomarkers which will be combined with other biomarkers across sites to develop a prognostic tool in CHR-P. The PSYSCAN EEG study aims to pave the way for incorporating EEG biomarkers in the assessment of CHR-P individuals, to refine the diagnostic process and help to stratify CHR-P subjects according to risk of transition. This may improve our understanding of the CHR-P state and therefore aid the development of more personalized treatment strategies.

Early Detection and Prevention of Schizophrenic Psychosis-A Review

Psychotic disorders often run a chronic course and are associated with a considerable emotional and social impact for patients and their relatives. Therefore, early recognition, combined with the possibility of preventive intervention, is urgently warranted since the duration of untreated psychosis (DUP) significantly determines the further course of the disease. In addition to established diagnostic tools, neurobiological factors in the development of schizophrenic psychoses are increasingly being investigated. It is shown that numerous molecular alterations already exist before the clinical onset of the disease. As schizophrenic psychoses are not elicited by a single mutation in the deoxyribonucleic acid (DNA) sequence, epigenetics likely constitute the missing link between environmental influences and disease development and could potentially serve as a biomarker. The results from transcriptomic and proteomic studies point to a dysregulated immune system, likely evoked by epigenetic alterations. Despite the increasing knowledge of the neurobiological mechanisms involved in the development of psychotic disorders, further research efforts with large population-based study designs are needed to identify suitable biomarkers. In conclusion, a combination of blood examinations, functional imaging techniques, electroencephalography (EEG) investigations and polygenic risk scores should be considered as the basis for predicting how subjects will transition into manifest psychosis.

The iPSC perspective on schizophrenia

Over a decade of schizophrenia research using human induced pluripotent stem cell (iPSC)-derived neural models has provided substantial data describing neurobiological characteristics of the disorder in vitro. Simultaneously, translation of the results into general mechanistic concepts underlying schizophrenia pathophysiology has been trailing behind. Given that modeling brain function using cell cultures is challenging, the gap between the in vitro models and schizophrenia as a clinical disorder has remained wide. In this review, we highlight reproducible findings and emerging trends in recent schizophrenia-related iPSC studies. We illuminate the relevance of the results in the context of human brain development, with a focus on processes coinciding with critical developmental periods for schizophrenia.

Genetic underpinnings of schizophrenia-related electroencephalographical intermediate phenotypes: A systematic review and meta-analysis

Although substantial research into genetics of psychotic disorders has been conducted, a large proportion of their genetic architecture has remained unresolved. Electroencephalographical intermediate phenotypes (EIP) have the potential to constitute a valuable tool when studying genetic risk loci for schizophrenia, in particular P3b amplitude, P50 suppression, mismatch negativity (MMN) and resting state power spectra of the electroencephalogram (EEG). Here, we systematically reviewed studies investigating the association of single nucleotide polymorphisms (SNPs) with these EIPs and meta-analysed them when appropriate. We retrieved 45 studies (N = 34,971 study participants). Four SNPs investigated in more than one study were genome-wide significant for an association with schizophrenia and three were genome-wide suggestive, based on a lookup in the influential 2014 GWAS (Ripke et al., 2014). However, in our meta-analyses, rs1625579 failed to reach a statistically significant association with p3b amplitude decrease and rs4680 risk allele carrier status was not associated with p3b amplitude decrease or with impaired p50 suppression. In conclusion, evidence for SNP associations with EIPs remains limited to individual studies. Careful selection of EIPs and SNPs, combined with consistent reporting of effect sizes, directions of effect and p-values would aid future meta-analyses.

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.

Thalamus-related anomalies as candidate mechanism-based biomarkers for psychosis

Identification of reliable biomarkers of prognosis in subjects with high risk to psychosis is an essential step to improve care and treatment of this population of help-seekers. Longitudinal studies highlight some clinical criteria, cognitive deficits, patterns of gray matter alterations and profiles of blood metabolites that provide some levels of prediction regarding the conversion to psychosis. Further effort is warranted to validate these results and implement these types of approaches in clinical settings. Such biomarkers may however fall short in entangling the biological mechanisms underlying the disease progression, an essential step in the development of novel therapies. Circuit-based approaches, which map on well-identified cerebral functions, could meet these needs. Converging evidence indicates that thalamus abnormalities are central to schizophrenia pathophysiology, contributing to clinical symptoms, cognitive and sensory deficits. This review highlights the various thalamus-related anomalies reported in individuals with genetic risks and in the different phases of the disorder, from prodromal to chronic stages. Several anomalies are potent endophenotypes, while others exist in clinical high-risk subjects and worsen in those who convert to full psychosis. Aberrant functional coupling between thalamus and cortex, low glutamate content and readouts from resting EEG carry predictive values for transition to psychosis or functional outcome. In this context, thalamus-related anomalies represent a valuable entry point to tackle circuit-based alterations associated with the emergence of psychosis. This review also proposes that longitudinal surveys of neuroimaging, EEG readouts associated with circuits encompassing the mediodorsal, pulvinar in high-risk individuals could unveil biological mechanisms contributing to this psychiatric disorder.

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

BACKGROUND

The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied.

METHODS

We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality.

RESULTS

A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects.

CONCLUSIONS

Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.

Electroencephalography and Event-Related Potential Biomarkers in Individuals at Clinical High Risk for Psychosis

Clinical outcomes vary among youths at clinical high risk for psychosis (CHR-P), with approximately 20% progressing to full-blown psychosis over 2 to 3 years and 30% achieving remission. Recent research efforts have focused on identifying biomarkers that precede psychosis onset and enhance the accuracy of clinical outcome prediction in CHR-P individuals, with the ultimate goal of developing staged treatment approaches based on the individual's level of risk. Identifying such biomarkers may also facilitate progress toward understanding pathogenic mechanisms underlying psychosis onset, which may support the development of mechanistically informed early interventions for psychosis. In recent years, electroencephalography-based event-related potential measures with established sensitivity to schizophrenia have gained traction in the study of CHR-P and its clinical outcomes. In this review, we describe the evidence for event-related potential abnormalities in CHR-P and discuss how they inform our understanding of information processing deficits as vulnerability markers for emerging psychosis and as indicators of future outcomes. Among the measures studied, P300 and mismatch negativity are notable because deficits predict conversion to psychosis and/or CHR-P remission. However, the accuracy with which these and other measures predict outcomes in CHR-P has been obscured in the prior literature by the tendency to only report group-level differences, underscoring the need for inclusion of individual predictive accuracy metrics in future studies. Nevertheless, both P300 and mismatch negativity show promise as electrophysiological markers of risk for psychosis, as target engagement measures for clinical trials, and as potential translational bridges between human studies and animal models focused on novel drug development for early psychosis.

Potential Roles of Redox Dysregulation in the Development of Schizophrenia

Converging evidence implicates redox dysregulation as a pathological mechanism driving the emergence of psychosis. Increased oxidative damage and decreased capacity of intracellular redox modulatory systems are consistent findings in persons with schizophrenia as well as in persons at clinical high risk who subsequently developed frank psychosis. Levels of glutathione, a key regulator of cellular redox status, are reduced in the medial prefrontal cortex, striatum, and thalamus in schizophrenia. In humans with schizophrenia and in rodent models recapitulating various features of schizophrenia, redox dysregulation is linked to reductions of parvalbumin containing gamma-aminobutyric acid (GABA) interneurons and volumes of their perineuronal nets, white matter abnormalities, and microglia activation. Importantly, the activity of transcription factors, kinases, and phosphatases regulating diverse aspects of neurodevelopment and synaptic plasticity varies according to cellular redox state. Molecules regulating interneuron function under redox control include NMDA receptor subunits GluN1 and GluN2A as well as KEAP1 (regulator of transcription factor NRF2). In a rodent schizophrenia model characterized by impaired glutathione synthesis, the Gclm knockout mouse, oxidative stress activated MMP9 (matrix metalloprotease 9) via its redox-responsive regulatory sites, causing a cascade of molecular events leading to microglia activation, perineural net degradation, and impaired NMDA receptor function. Molecular pathways under redox control are implicated in the etiopathology of schizophrenia and are attractive drug targets for individualized drug therapy trials in the contexts of prevention and treatment of psychosis.

EPA guidance on the early intervention in clinical high risk states of psychoses

This guidance paper from the European Psychiatric Association (EPA) aims to provide evidence-based recommendations on early intervention in clinical high risk (CHR) states of psychosis, assessed according to the EPA guidance on early detection. The recommendations were derived from a meta-analysis of current empirical evidence on the efficacy of psychological and pharmacological interventions in CHR samples. Eligible studies had to investigate conversion rate and/or functioning as a treatment outcome in CHR patients defined by the ultra-high risk and/or basic symptom criteria. Besides analyses on treatment effects on conversion rate and functional outcome, age and type of intervention were examined as potential moderators. Based on data from 15 studies (n = 1394), early intervention generally produced significantly reduced conversion rates at 6- to 48-month follow-up compared to control conditions. However, early intervention failed to achieve significantly greater functional improvements because both early intervention and control conditions produced similar positive effects. With regard to the type of intervention, both psychological and pharmacological interventions produced significant effects on conversion rates, but not on functional outcome relative to the control conditions. Early intervention in youth samples was generally less effective than in predominantly adult samples. Seven evidence-based recommendations for early intervention in CHR samples could have been formulated, although more studies are needed to investigate the specificity of treatment effects and potential age effects in order to tailor interventions to the individual treatment needs and risk status.

Models Predicting Psychosis in Patients With High Clinical Risk: A Systematic Review

OBJECTIVE

The present study reviews predictive models used to improve prediction of psychosis onset in individuals at clinical high risk for psychosis (CHR), using clinical, biological, neurocognitive, environmental, and combinations of predictors.

METHODS

A systematic literature search on PubMed was carried out (from 1998 through 2019) to find all studies that developed or validated a model predicting the transition to psychosis in CHR subjects.

RESULTS

We found 1,406 records. Thirty-eight of them met the inclusion criteria; 11 studies using clinical predictive models, seven studies using biological models, five studies using neurocognitive models, five studies using environmental models, and 18 studies using combinations of predictive models across different domains. While the highest positive predictive value (PPV) in clinical, biological, neurocognitive, and combined predictive models were relatively high (all above 83), the highest PPV across environmental predictive models was modest (63%). Moreover, none of the combined models showed a superiority when compared with more parsimonious models (using only neurocognitive, clinical, biological, or environmental factors).

CONCLUSIONS

The use of predictive models may allow high prognostic accuracy for psychosis prediction in CHR individuals. However, only ten studies had performed an internal validation of their models. Among the models with the highest PPVs, only the biological and neurocognitive but not the combined models underwent validation. Further validation of predicted models is needed to ensure external validity.

Frontal slow wave resting EEG power is higher in individuals at Ultra High Risk for psychosis than in healthy controls but is not associated with negative symptoms or functioning

Decreased brain activity in the frontal region, as indicated by increased slow wave EEG power measured by electrodes place on the skull over this area, in association with negative symptoms has previously been shown to distinguish ultra-high risk (UHR) individuals who later transitioned to psychosis (UHR-P) from those who did not transition (UHR-NP). The aims of the current study were to: 1) replicate these results and 2) investigate whether similar association between increased frontal slow wave activity and functioning shows any value in the prediction of transition to psychosis in UHR individuals. The brain activity, recorded using EEG, of 44 UHR individuals and 38 healthy controls was included in the analyses. Symptom severity was assessed in UHR participants and functioning was measured in both groups. The power in the theta frequency band in the frontal region of UHR individuals was higher than in controls. However, there was no difference between the UHR-P and the UHR-NP groups, and no change in slow frequency power following transition to psychosis. The correlation between delta frequency power and negative symptoms previously observed was not present in our UHR cohort, and there was no association between frontal delta or theta and functioning in either group. Increased delta power was rather correlated with depressive symptoms in the UHR group. Future research will be needed to better understand when, in the course of the illness, does the slow wave activity in the frontal area becomes impaired.

Prevalence, course and psychosis-predictive value of negative symptoms in 22q11.2 deletion syndrome

BACKGROUND

The 22q11.2 deletion syndrome (22q11DS) is one of the highest known risk factors for schizophrenia and recent findings have highlighted the clinical relevance of ultra-high risk (UHR) criteria in this population. However, studies in other at-risk populations have shown that the presence of negative symptoms (NS) is also of clinical relevance in predicting transition to psychosis. The present study examined in detail the presence and course of NS in 22q11DS, as well as their value in predicting transition to psychosis.

METHODS

A total of 111 participants aged between 8 and 33 years were assessed with the Structured Interview for Psychosis-Risk Syndromes (SIPS). A follow-up assessment was available for 89 individuals.

RESULTS

Core NS of at least moderate severity were present in 50.5% of the sample and were more severe in individuals meeting UHR criteria. They predominantly remained stable over time and their emergence between baseline and follow-up assessment was associated with significant functional decline. Some NS were significant predictors of conversion to psychosis and the emergence/persistence of psychosis risk.

CONCLUSIONS

Altogether, these findings highlight that NS are core manifestations of psychosis in individuals with 22q11DS that strongly impact global functioning. The presence of NS should be a primary target of early therapeutic intervention in this population.

Social anhedonia and gamma band abnormalities as a composite/multivariate endophenotype for schizophrenia: a dense array EEG study

AIM

Social anhedonia and gamma band oscillations are proposed as a promising endophenotype for schizophrenia (SZ). The aim was to assess whether social anhedonia and spontaneous gamma band oscillations could be used as multivariate/composite endophenotypic measures for SZ.

METHODS

Sixty consented subjects, of which 20 remitted SZ patients, 20 unaffected siblings of patients with schizophrenia (US) and 20 healthy controls (HC) were recruited for the study. The Revised Social Anhedonia Scale, Temporal Experience of Pleasure Scale and the Schizotypal Personality Questionnaire assessed social anhedonia, temporal experience of pleasure and schizotypal features. All participants underwent awake, resting state 192-channel dense array electroencephalographic recording. Gamma spectral power and coherence were calculated. We performed chi-square test, one-way analysis of variance, Pearsons correlation coefficient and step-by-step linear discriminant functional analysis.

RESULTS

Social anhedonia was significantly higher and anticipatory aspects of pleasure were significantly lower in both SZ and US compared with HC. US scored significantly higher than HC in the Schizotypal Personality Questionnaire. Spectral power of high gamma band (>70 Hz) was significantly lower over the right temporo-parietal and midline regions in both SZ and US than HC. We accurately classified (85%) three groups when social anhedonia, high gamma band spectral power of midline, right frontal and right fronto-temporal interhemispheric gamma coherence were considered as composite measures rather than each variable representing independently.

CONCLUSION

We propose region-specific high gamma spectral 'power and coherence' and social anhedonia as composite/multivariate measures could be a useful measure in distinguishing schizophrenia patients and unaffected siblings from healthy controls.

Viral encephalitis in quantitative EEG

OBJECTIVE

Our aim was to study the character of quantitative EEG of viral encephalitis.

METHOD

Collect the EEG data of hospitalized children with viral encephalitis diagnosed by pediatricians from January 2013 to September 2016, and EEG data of normal cases at the same age were control group. Using quantitative EEG analysis technologies to obtain power spectrum value, power spectrum value, 1 power spectrum value, 12 power spectrum value, 21 power spectrum value, and 12 power spectrum value. Relative power spectrum values of 2 and were obtained by calculation. All the cases were divided into 5 groups according to the EEG character: age 3 group, age 4 group, age 5-6 group, age 7-9-year group, and age 10-14-year group. Viral encephalitis group and normal cases group were statistically compared to obtain characters of quantitative EEG with viral encephalitis.

RESULTS

Power spectrum values and power spectrum values of 3-14-year-old cases with encephalitis increased. 1 power spectrum values existed in age 3 group with viral encephalitis and declined at the post-head lead, while 11 and 12 power spectrum values existed in age 4-14 group with viral encephalitis declined at the post-head lead. The value of 2 Power spectrum values of age 3-9 group was limited in diagnosing viral encephalitis. 1 and 12 power spectrum values decrease in age 10-14 group with viral encephalitis. Relative power spectrum values of 2 and increased in age 3-14 group with viral encephalitis; Relative power spectrum values of decreased in age 3-14 group with viral encephalitis, most lead of relative power spectrum decreased in age 3-14 group with viral encephalitis.

CONCLUSION

The character of quantitative EEG of cases with viral encephalitis is similar to EEG, but more detailed, more precise, more intuitive and can be used for clinical diagnose of viral encephalitis.

Electroencephalographic delta/alpha frequency activity differentiates psychotic disorders: a study of schizophrenia, bipolar disorder and methamphetamine-induced psychotic disorder

Electroencephalography (EEG) has been proposed as a neurophysiological biomarker to delineate psychotic disorders. It is known that increased delta and decreased alpha, which are apparent in psychosis, are indicative of inappropriate arousal state, which leads to reduced ability to attend to relevant information. On this premise, we investigated delta/alpha frequency activity, as this ratio of frequency activity may serve as an effective neurophysiological biomarker. The current study investigated differences in delta/alpha frequency activity, in schizophrenia (SCZ), bipolar I disorder with psychotic features and methamphetamine-induced psychosis. One hundred and nine participants, including individuals with SCZ (n = 28), bipolar I disorder with psychotic features (n = 28), methamphetamine-induced psychotic disorder (MPD) (n = 24) and healthy controls (CON, n = 29). Diagnosis was ascertained with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition disorders and current medication was recorded. EEG was undertaken in three testing conditions: resting eyes open, resting eyes closed and during completion of a simple cognitive task (visual continuous performance task). EEG delta/alpha frequency activity was investigated across these conditions. First, delta/alpha frequency activity during resting eyes closed was higher in SCZ and MPD globally, when compared to CON, then lower for bipolar disorder (BPD) than MPD for right hemisphere. Second, delta/alpha frequency activity during resting eyes open was higher in SCZ, BPD and MPD for all electrodes, except left frontal, when compared to CON. Third, delta/alpha frequency activity during the cognitive task was higher in BPD and MPD for all electrodes, except left frontal, when compared to CON. Assessment of EEG delta/alpha frequency activity supports the delineation of underlying neurophysiological mechanisms present in psychotic disorders, which are likely related to dysfunctional thalamo-cortical connectivity. Delta/alpha frequency activity may provide a useful neurophysiological biomarker to delineate psychotic disorders.

Towards a neurodynamical understanding of the prodrome in schizophrenia

The identification of biomarkers for the early diagnosis of schizophrenia that could inform novel treatment developments is an important objective of current research. This paper will summarize recent work that has investigated changes in oscillatory activity and event-related potentials with Electro/Magnetoencephalography (EEG/MEG) in participants at high-risk for the development of schizophrenia, highlighting disruptions in sensory and cognitive operations prior to the onset of the syndrome. Changes in EEG/MEG-data are consistent with evidence for alterations in Glutamatergic and GABAergic neurotransmission as disclosed by Magnetic Resonance Spectroscopy and brain stimulation, indicating changes in Excitation/Inhibition balance parameters prior to the onset of psychosis. Together these data emphasize the importance of research into neuronal dynamics as a crucial approach to establish functional relationships between impairments in neural circuits and emerging psychopathology that together could be fundamental for early intervention and the identification of novel treatments for emerging psychosis.

Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation

Discriminating subjects at clinical high risk (CHR) for psychosis who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalized prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modeling studies using clinical, biological, neurocognitive, environmental, and combinations of predictors. In a second step, we performed statistical simulations to test different probabilistic sequential 3-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive, and combined models) yielded PPVs of above 82%. Using only data from validated models, 3-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + electroencephalography), then structural magnetic resonance imaging and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT = 2) for an individual with 3 positive sequential tests, 71%-82% (NNT = 3) with 2 positive tests, 12%-21% (NNT = 11-18) with 1 positive test, and 1% (NNT = 219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multistage sequential testing may allow individual risk stratification of CHR individuals and optimize the prediction of psychosis.

Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers

Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview.

Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients

OBJECTIVES

This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.

METHODS

We assessed the individualised prediction of psychosis by detecting specific patterns of beta and gamma oscillations using machine-learning algorithms. Prediction models were trained and tested on 53 neuroleptic-naïve patients with a clinical high-risk for psychosis. Of these, 18 later transitioned to psychosis. All patients were followed up for at least 3 years. For an honest estimation of the generalisation capacity, the predictive performance of the models was assessed in unseen test cases using repeated nested cross-validation.

RESULTS

Transition to psychosis could be predicted from current-source density (CSD; area under the curve [AUC] = 0.77), but not from lagged phase synchronicity data (LPS; AUC = 0.56). Combining both modalities did not improve the predictive accuracy (AUC = 0.78). The left superior temporal gyrus, the left inferior parietal lobule and the precuneus most strongly contributed to the prediction of psychosis.

CONCLUSIONS

Our results suggest that CSD measurements extracted from clinical resting state EEG can help to improve the prediction of psychosis on a single-subject level.

The early identification of psychosis: can lessons be learnt from cardiac stress testing?

Psychotic disorders including schizophrenia are amongst the most debilitating psychiatric disorders. There is an urgent need to develop methods to identify individuals at risk with greater precision and as early as possible. At present, a prerequisite for a diagnosis of schizophrenia is the occurrence of a psychotic episode. Therefore, attempting to detect schizophrenia on the basis of psychosis is analogous to diagnosing coronary artery disease (CAD) after the occurrence of a myocardial infarction (MI). The introduction of cardiac stress testing (CST) has revolutionized the detection of CAD and the prevention and management of angina and MI. In this paper, we attempt to apply lessons learnt from CST to the early detection of psychosis by proposing the development of an analogous psychosis stress test. We discuss in detail the various parameters of a proposed psychosis stress test including the choice of a suitable psychological or psychopharmacological "stressor," target population, outcome measures, safety of the approach, and the necessary evolution of test to become clinically informative. The history of evolution of CST may guide the development of a similar approach for the detection and management of psychotic disorders. The initial development of a test to unmask latent risk for schizophrenia will require the selection of a suitable and safe stimulus and the development of outcome measures as a prelude to testing in populations with a range of risk to determine predictive value. The use of CST in CAD offers the intriguing possibility that a similar approach may be applied to the detection and management of schizophrenia.

Revisiting the Basic Symptom Concept: Toward Translating Risk Symptoms for Psychosis into Neurobiological Targets

In its initial formulation, the concept of basic symptoms (BSs) integrated findings on the early symptomatic course of schizophrenia and first in vivo evidence of accompanying brain aberrations. It argued that the subtle subclinical disturbances in mental processes described as BSs were the most direct self-experienced expression of the underlying neurobiological aberrations of the disease. Other characteristic symptoms of psychosis (e.g., delusions and hallucinations) were conceptualized as secondary phenomena, resulting from dysfunctional beliefs and suboptimal coping styles with emerging BSs and/or concomitant stressors. While BSs can occur in many mental disorders, in particular affective disorders, a subset of perceptive and cognitive BSs appear to be specific to psychosis and are currently employed in two alternative risk criteria. However, despite their clinical recognition in the early detection of psychosis, neurobiological research on the aetiopathology of psychosis with neuroimaging methods has only just begun to consider the neural correlate of BSs. This perspective paper reviews the emerging evidence of an association between BSs and aberrant brain activation, connectivity patterns, and metabolism, and outlines promising routes for the use of BSs in aetiopathological research on psychosis.

Detection and treatment of at-risk mental state for developing a first psychosis: making up the balance

The at-risk mental state (ARMS) has been substantially researched and used as the basis for new clinical settings and strategies over the past two decades. However, it has also caused controversy and intense debate. In this Review, we assess available evidence and propose future directions. Accumulating research suggests that a blend of clinical staging and profiling, which naturally incorporates ARMS, might be a better guide for treatment of patients in different stages of psychiatric illness than the categorical DSM and ICD systems. Furthermore, clinical staging, with its emphasis on balancing risks and benefits, could help to prevent premature treatment or overtreatment with psychotropic drugs. Meta-analyses and reviews show that treatment of ARMS leads to a significant reduction in transition rate to a first psychosis. The debate about stigma associated with ARMS is based on scarce published work. The few studies that have been done suggest that stigma (including self-stigma) arises largely from negative societal views on psychiatric disorders and, depending on the setting and approach, not from engagement in treatment for ARMS per se. The evidence base suggests that definition of ARMS is an important step in implementation of clinical staging and profiling in psychiatry. However, more research across traditional diagnostic boundaries is needed to refine these clinical phenotypes and link them to biomarkers with the goal of personalised stepwise care. Health-system reform is overdue and a parallel process to support this approach is needed, which is similar to how physical forms of non-communicable disease are treated.

Aberrant Current Source-Density and Lagged Phase Synchronization of Neural Oscillations as Markers for Emerging Psychosis

BACKGROUND

Converging evidence indicates that neural oscillations coordinate activity across brain areas, a process which is seemingly perturbed in schizophrenia. In particular, beta (13-30 Hz) and gamma (30-50 Hz) oscillations were repeatedly found to be disturbed in schizophrenia and linked to clinical symptoms. However, it remains unknown whether abnormalities in current source density (CSD) and lagged phase synchronization of oscillations across distributed regions of the brain already occur in patients with an at-risk mental state (ARMS) for psychosis.

METHODS

To further elucidate this issue, we assessed resting-state EEG data of 63 ARMS patients and 29 healthy controls (HC). Twenty-three ARMS patients later made a transition to psychosis (ARMS-T) and 40 did not (ARMS-NT). CSD and lagged phase synchronization of neural oscillations across brain areas were assessed using eLORETA and their relationships to neurocognitive deficits and clinical symptoms were analyzed using linear mixed-effects models.

RESULTS

ARMS-T patients showed higher gamma activity in the medial prefrontal cortex compared to HC, which was associated with abstract reasoning abilities in ARMS-T. Furthermore, in ARMS-T patients lagged phase synchronization of beta oscillations decreased more over Euclidian distance compared to ARMS-NT and HC. Finally, this steep spatial decrease of phase synchronicity was most pronounced in ARMS-T patients with high positive and negative symptoms scores.

CONCLUSIONS

These results indicate that patients who will later make the transition to psychosis are characterized by impairments in localized and synchronized neural oscillations providing new insights into the pathophysiological mechanisms of schizophrenic psychoses and may be used to improve the prediction of psychosis.

Early Detection of Psychosis: Recent Updates from Clinical High-Risk Research

The debilitating nature of schizophrenia necessitates early detection of individuals at clinical high-risk (CHR) in order to facilitate early intervention. In particular, comparisons between those who develop fully psychotic features (CHR+) and those who do not (CHR-) offer the opportunity to reveal distinct risk factors for psychosis, as well as possible intervention target points. Recent studies have investigated baseline clinical, neurocognitive, neuroanatomic, neurohormonal, and psychophysiological predictors of outcome; premorbid social dysfunction, deficits in neurocognitive performance, neuroanatomic changes, and hypothalamic-pituitary-adrenal (HPA) axis dysfunction have been implicated in psychosis emergence. However, several challenges within CHR research remain: heterogeneity in long-term diagnostic outcome, the variability of research tools and definitions utilized, and limited longitudinal follow-up. Future work in the field should focus on replication via extended longitudinal designs, aim to explore the trajectories and inter-relationships of hypothesized biomarkers, and continue to investigate interventions that seek to prevent psychosis emergence through symptom reduction.

Prediction and prevention of psychosis: current progress and future tasks

Prevention of psychoses has been intensively investigated within the past two decades, and particularly, prediction has been much advanced. Depending on the applied risk indicators, current criteria are associated with average, yet significantly heterogeneous transition rates of ≥30 % within 3 years, further increasing with longer follow-up periods. Risk stratification offers a promising approach to advance current prediction as it can help to reduce heterogeneity of transition rates and to identify subgroups with specific needs and response patterns, enabling a targeted intervention. It may also be suitable to improve risk enrichment. Current results suggest the future implementation of multi-step risk algorithms combining sensitive risk detection by cognitive basic symptoms (COGDIS) and ultra-high-risk (UHR) criteria with additional individual risk estimation by a prognostic index that relies on further predictors such as additional clinical indicators, functional impairment, neurocognitive deficits, and EEG and structural MRI abnormalities, but also considers resilience factors. Simply combining COGDIS and UHR criteria in a second step of risk stratification produced already a 4-year hazard rate of 0.66. With regard to prevention, two recent meta-analyses demonstrated that preventive measures enable a reduction in 12-month transition rates by 54-56 % with most favorable numbers needed to treat of 9-10. Unfortunately, psychosocial functioning, another important target of preventive efforts, did not improve. However, these results are based on a relatively small number of trials; and more methodologically sound studies and a stronger consideration of individual profiles of clinical needs by modular intervention programs are required.

[Prediction of psychoses]

BACKGROUND

The worldwide established early detection and prevention centers for psychosis follow the modern program of predictive, preventive and personalized medicine.

OBJECTIVES

If primary prevention is to succeed, the individual risk of the disease has to be estimated correctly and the psychosis onset has to be accurately predicted. Accordingly, this article presents the current possibilities for prediction.

MATERIALS AND METHODS

An overview on the recent prediction analyses in clinical high risk for psychosis research is provided.

RESULTS

The previously identified high-risk criteria achieve a considerable predictive power, which can be further enhanced by their combined use as well as other strategies of risk enrichment and risk stratification.

CONCLUSIONS

Clinical prediction already allows risk-adapted prevention measures and is currently being enhanced even further by additional biological brain diagnostics.

Recent advances in understanding schizophrenia

Schizophrenia is a highly disabling disorder whose causes remain to be better understood, and treatments have to be improved. However, several recent advances have been made in diagnosis, etiopathology, and treatment. Whereas reliability of diagnosis has improved with operational criteria, including Diagnostic and Statistical Manual of Mental Disorders, (DSM) Fifth Edition, validity of the disease boundaries remains unclear because of substantive overlaps with other psychotic disorders. Recent emphasis on dimensional approaches and translational bio-behavioral research domain criteria may eventually help move toward a neuroscience-based definition of schizophrenia. The etiology of schizophrenia is now thought to be multifactorial, with multiple small-effect and fewer large-effect susceptibility genes interacting with several environmental factors. These factors may lead to developmentally mediated alterations in neuroplasticity, manifesting in a cascade of neurotransmitter and circuit dysfunctions and impaired connectivity with an onset around early adolescence. Such etiopathological understanding has motivated a renewed search for novel pharmacological as well as psychotherapeutic targets. Addressing the core features of the illness, such as cognitive deficits and negative symptoms, and developing hypothesis-driven early interventions and preventive strategies are high-priority goals for the field. Schizophrenia is a severe, chronic mental disorder and is among the most disabling disorders in all of medicine. It is estimated by the National Institute of Mental Health (NIMH) that 2.4 million people over the age of 18 in the US suffer from schizophrenia. This illness typically begins in adolescence and derails the formative goals of school, family, and work, leading to considerable suffering and disability and reduced life expectancy by about 20 years. Treatment outcomes are variable, and some people are successfully treated and reintegrated (i.e. go back to work). Despite the effort of many experts in the field, however, schizophrenia remains a chronic relapsing and remitting disorder associated with significant impairments in social and vocational functioning and a shortened lifespan. Comprehensive treatment entails a multi-modal approach, including psychopharmacology, psychosocial interventions, and assistance with housing and financial sustenance. Research to date suggests a network of genetic, neural, behavioral, and environmental factors to be responsible for its development and course. This article aims to summarize and explain recent advancements in research on schizophrenia, to suggest how these recent discoveries may lead to a better understanding and possible further development of effective therapies, and to highlight the paradigm shifts that have taken place in our understanding of the diagnosis, etiopathology, and treatment.