EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis

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.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

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.

Momentary Manifestations of Negative Symptoms as Predictors of Clinical Outcomes in People at High Risk for Psychosis: Experience Sampling Study

BACKGROUND

Negative symptoms occur in individuals at ultrahigh risk (UHR) for psychosis. Although there is evidence that observer ratings of negative symptoms are associated with level of functioning, the predictive value of subjective experience in daily life for individuals at UHR has not been studied yet.

OBJECTIVE

This study therefore aims to investigate the predictive value of momentary manifestations of negative symptoms for clinical outcomes in individuals at UHR.

METHODS

Experience sampling methodology was used to measure momentary manifestations of negative symptoms (blunted affective experience, lack of social drive, anhedonia, and social anhedonia) in the daily lives of 79 individuals at UHR. Clinical outcomes (level of functioning, illness severity, UHR status, and transition status) were assessed at baseline and at 1- and 2-year follow-ups.

RESULTS

Lack of social drive, operationalized as greater experienced pleasantness of being alone, was associated with poorer functioning at the 2-year follow-up (b=-4.62, P=.01). Higher levels of anhedonia were associated with poorer functioning at the 1-year follow-up (b=5.61, P=.02). Higher levels of social anhedonia were associated with poorer functioning (eg, disability subscale: b=6.36, P=.006) and greater illness severity (b=-0.38, P=.045) at the 1-year follow-up. In exploratory analyses, there was evidence that individuals with greater variability of positive affect (used as a measure of blunted affective experience) experienced a shorter time to remission from UHR status at follow-up (hazard ratio=4.93, P=.005).

CONCLUSIONS

Targeting negative symptoms in individuals at UHR may help to predict clinical outcomes and may be a promising target for interventions in the early stages of psychosis.

Resting state EEG in youth with ASD: age, sex, and relation to phenotype

BACKGROUND

Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy.

METHODS

We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations.

RESULTS

Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD.

CONCLUSIONS

Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.

Persistent negative symptoms in youth at clinical high risk for psychosis: A longitudinal study

BACKGROUND

Severity of negative symptoms has been associated with poor functioning, cognitive deficits, and defeatist beliefs in schizophrenia patients. However, one area that remains understudied is persistent negative symptoms (PNS). Negative symptoms, including PNS, have been observed in those at clinical high-risk (CHR) for psychosis. The aim of this study was to determine if PNS were associated with functioning, neurocognition, and defeatist beliefs in a CHR sample.

METHOD

CHR participants (n = 764) were recruited for the North American Prodrome Longitudinal Study. Negative symptoms were rated on the Scale of Psychosis-risk Symptoms. Generalized linear mixed models for repeated measures were used to examine changes over time between and within groups (PNS vs non-PNS).

RESULTS

The PNS group (n = 67) had significant deficits in functioning at baseline, 6, 12, 18, and 24-months compared to the non-PNS group (n = 673). Functioning improved over time in the non-PNS group, while functioning in the PNS group remained relatively stable and poor over a two-year period. A consistent trend emerged demonstrating higher defeatist beliefs in the PNS group; however, this result was lost when controlling for persistent depressive symptoms. There were no significant differences between the groups on neurocognition, social cognition, and transition to psychosis.

CONCLUSIONS

PNS exist in youth at CHR for psychosis, resulting in significant and persistent functional impairment, which remains when controlling for persistent depressive symptoms. PNS remain even in CHR youth who do not transition to psychosis. Thus, PNS may represent an unmet therapeutic need in CHR populations for which there are currently no effective treatments.

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.

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.

Do psychosis prodrome onset negative symptoms predict first presentation negative symptoms?

Background:

Negative symptoms have been previously reported during the psychosis prodrome, however our understanding of their relationship with treatment-phase negative symptoms remains unclear.

Objectives:

We report the prevalence of psychosis prodrome onset negative symptoms (PONS) and ascertain whether these predict negative symptoms at first presentation for treatment.

Methods:

Presence of expressivity or experiential negative symptom domains was established at first presentation for treatment using the Scale for Assessment of Negative Symptoms (SANS) in 373 individuals with a first episode psychosis. PONS were established using the Beiser Scale. The relationship between PONS and negative symptoms at first presentation was ascertained and regression analyses determined the relationship independent of confounding.

Results:

PONS prevalence was 50.3% in the schizophrenia spectrum group (n = 155) and 31.2% in the non-schizophrenia spectrum group (n = 218). In the schizophrenia spectrum group, PONS had a significant unadjusted (χ2 = 10.41, P < 0.001) and adjusted (OR = 2.40, 95% CI = 1.11–5.22, P = 0.027) association with first presentation experiential symptoms, however this relationship was not evident in the non-schizophrenia spectrum group. PONS did not predict expressivity symptoms in either diagnostic group.

Conclusion:

PONS are common in schizophrenia spectrum diagnoses, and predict experiential symptoms at first presentation. Further prospective research is needed to examine whether negative symptoms commence during the psychosis prodrome.

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.

Staging model in psychiatry: Review of the evolution of electroencephalography abnormalities in major psychiatric disorders

AIM

Clinical staging in psychiatry aims to classify patients according to the severity of their symptoms, from stage 0 (increased risk, asymptomatic) to stage 4 (severe illness), enabling adapted treatment at each stage of the illness. The staging model would gain specificity if one or more quantifiable biological markers could be identified. Several biomarkers reflecting possible causal mechanisms and/or consequences of the pathophysiology are candidates for integration into the clinical staging model of psychiatric illnesses.

METHODS

This review covers the evolution (from stage 0 to stage 4) of the most important brain functioning impairments as measured with electroencephalography (EEG), in psychosis spectrum and in severe mood disorders.

RESULTS

The present review of the literature demonstrates that it is currently not possible to draw any conclusion with regard to the state or trait character of any of the EEG impairments in both major depressive disorder and bipolar disorder. As for schizophrenia, the most promising markers of the stage of the illness are the pitch mismatch negativity as well as the p300 event-related potentials, as these components seem to deteriorate with increasing severity of the illness.

CONCLUSIONS

Given the complexity of major psychiatric disorders, and that not a single impairment can be observed in all patients, future research should most likely consider combinations of markers in the quest for a better identification of the stages of the psychiatric illnesses.

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.

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.

Electroencephalogram alpha-to-theta ratio over left fronto-temporal region correlates with negative symptoms in schizophrenia

BACKGROUND

Negative symptoms impair outcomes on occupational functioning, social interaction and interpersonal relationships in patients with schizophrenia. Past researchers have reported reduced alpha and increased theta and delta spectral powers on quantitative EEG recordings in those with prominent negative symptoms.

AIM

Present analysis aimed at exploring the relationships between lower-frequency EEG powers and negative symptoms, in schizophrenia, over a period of naturalistic antipsychotic treatment.

METHOD

Fifteen right-handed drug-free/drug-naïve schizophrenia patients (N=15;M=12,F=3) were recruited and assessed on PANSS. Spontaneous 192-channel resting-state EEG was recorded at baseline, and PANSS rating was repeated at 4 weeks. Baseline EEGs of patients were compared to 15 age-sex-handedness matched controls.

RESULTS

Non-significant differences emerged between patient and controls in terms of socio-demographic characteristics. Over left frontal and left temporal regions, baseline ratio of alpha/theta power (bATR) was significantly lower (p<0.001) in patients, correlated negatively with baseline PANSS negative score (PNS) (p<0.05); and over 4-weeks of antipsychotic treatment, left temporal bATR correlated positively with relative improvements in PNS.

CONCLUSION

Reduced alpha power over frontal regions might imply altered arousal and/or impaired attentional process, while abnormal theta range oscillations may relate to impairments in working memory via their influences on theta-gamma coupling. Our findings suggest left-predominant deficiencies in these processes might mediate negative symptoms. Further, since ATR might reflect integrity of certain cognitive processes, those with a higher bATR might possess better cognitive resource at baseline and therefore experience greater improvement in negative symptoms with antipsychotic treatments, at least during the first 4 weeks.

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.

Correlates of electroencephalographic resting states and erythrocyte membrane docosahexaenoic and eicosapentaenoic acid levels in individuals at ultra-high risk of psychosis

OBJECTIVE

Abnormal levels of polyunsaturated fatty acids (PUFAs) have been reported in individuals suffering from schizophrenia. The main aim of the present study was to investigate the relationship between erythrocyte membrane fatty acid levels and resting-state brain activity occurring in individuals at ultra-high risk (UHR) of psychosis.

METHOD

The association between erythrocyte membrane fatty acids levels and resting-state brain activity and its value in predicting psychosis was examined in 72 UHR individuals.

RESULTS

In the frontal area, the activity in the fast frequency band Beta2 was positively associated with docosahexaenoic acid (DHA) levels (R = 0.321, P = 0.017), and in the fronto-central area, Beta2 activity showed a positive correlation with eicosapentaenoic acid (EPA) levels (R = 0.305, P = 0.009), regardless of psychosis transition status. Conversely, the slow frequency band Theta was significantly negatively associated with EPA levels in the parieto-occipital region (R = -0.251, P = 0.033. Results also showed that Alpha power was negatively correlated with DHA levels in UHR individuals who did not transition to psychosis, while this correlation was not present in individuals who later transitioned.

CONCLUSION

Our results suggest that individuals at UHR for psychosis who have higher basal omega-3 fatty acids levels present with resting EEG features associated with better states of alertness and vigilance. Furthermore, the improvement in the Alpha synchrony observed along with increased DHA levels in participants who did not transition to psychosis is disturbed in those who did transition. However, these interesting results are limited by the small sample size and low statistical power of the study.

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.

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.

Forecasting psychosis by event-related potentials-systematic review and specific meta-analysis

BACKGROUND

Prediction and prevention of psychosis have become major research topics. Clinical approaches warrant objective biological parameters to enhance validity in prediction of psychosis onset. In this regard, event-related potentials (ERPs) have been identified as promising tools for improving psychosis prediction.

METHODS

Herein, the focus is on sensory gating, mismatch negativity (MMN) and P300, thereby discussing which parameters allow for a timely and valid detection of future converters to psychosis. In a first step, we systematically reviewed the studies that resulted from a search of the MEDLINE database. In a second step, we performed a meta-analysis of those investigations reporting transitions that statistically compared ERPs in converting versus nonconverting subjects.

RESULTS

Sensory gating, MMN, and P300 have been demonstrated to be impaired in subjects clinically at risk of developing a psychotic disorder. In the meta-analysis, duration MMN achieved the highest effect size measures.

CONCLUSIONS

In summary, MMN studies have produced the most convincing results until now, including independent replication of the predictive validity. However, a synopsis of the literature revealed a relative paucity of ERP studies addressing the psychosis risk state. Considering the high clinical relevance of valid psychosis prediction, future research should question for the most informative paradigms and should allow for meta-analytic evaluation with regard to specificity and sensitivity of the most appropriate parameters.

EPA guidance on the early detection of clinical high risk states of psychoses

The aim of this guidance paper of the European Psychiatric Association is to provide evidence-based recommendations on the early detection of a clinical high risk (CHR) for psychosis in patients with mental problems. To this aim, we conducted a meta-analysis of studies reporting on conversion rates to psychosis in non-overlapping samples meeting any at least any one of the main CHR criteria: ultra-high risk (UHR) and/or basic symptoms criteria. Further, effects of potential moderators (different UHR criteria definitions, single UHR criteria and age) on conversion rates were examined. Conversion rates in the identified 42 samples with altogether more than 4000 CHR patients who had mainly been identified by UHR criteria and/or the basic symptom criterion 'cognitive disturbances' (COGDIS) showed considerable heterogeneity. While UHR criteria and COGDIS were related to similar conversion rates until 2-year follow-up, conversion rates of COGDIS were significantly higher thereafter. Differences in onset and frequency requirements of symptomatic UHR criteria or in their different consideration of functional decline, substance use and co-morbidity did not seem to impact on conversion rates. The 'genetic risk and functional decline' UHR criterion was rarely met and only showed an insignificant pooled sample effect. However, age significantly affected UHR conversion rates with lower rates in children and adolescents. Although more research into potential sources of heterogeneity in conversion rates is needed to facilitate improvement of CHR criteria, six evidence-based recommendations for an early detection of psychosis were developed as a basis for the EPA guidance on early intervention in CHR states.

Psychosis prediction: stratification of risk estimation with information-processing and premorbid functioning variables

BACKGROUND

The period preceding the first psychotic episode is regarded as a promising period for intervention. We aimed to develop an optimized prediction model of a first psychosis, considering different sources of information. The outcome of this model may be used for individualized risk estimation.

METHODS

Sixty-one subjects clinically at high risk (CHR), participating in the Dutch Prediction of Psychosis Study, were assessed at baseline with instruments yielding data on neuropsychology, symptomatology, environmental factors, premorbid adjustment, and neurophysiology. The follow-up period was 36 months.

RESULTS

At 36 months, 18 participants (29.5%) had made a transition to psychosis. Premorbid adjustment (P = .001, hazard ratio [HR] = 2.13, 95% CI = 1.39/3.28) and parietal P300 amplitude (P = .004, HR = 1.27, 95% CI = 1.08/1.45) remained as predictors in the Cox proportional hazard model. The resulting prognostic score (PS) showed a sensitivity of 88.9% and a specificity of 82.5%. The area under the curve of the PS was 0.91 (95% CI = 0.83-0.98, cross-validation: 0.86), indicating an outstanding ability of the model to discriminate between transition and nontransition. The PS was further stratified into 3 risk classes establishing a prognostic index. In the class with the worst social-personal adjustment and lowest P300 amplitudes, 74% of the subjects made a transition to psychosis. Furthermore, transition emerged on average more than 17 months earlier than in the lowest risk class.

CONCLUSIONS

Our results suggest that predicting a first psychotic episode in CHR subjects could be improved with a model including premorbid adjustment and information-processing variables in a multistep algorithm combining risk detection and stratification.

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.

[Personalised medicine in psychiatry and psychotherapy. A review of the current state-of-the-art in the biomarker-based early recognition of psychoses]

The main goal of psychiatric high-risk research--the personalised early recognition and intervention of schizophrenic and affective psychoses--is one of the biggest challenges of current clinical psychiatry due to the immense socioeconomic burden of these disorders. In this regard, this review discusses the prospects and caveats of new clinical, neuropsychological, neurophysiological and imaging-based concepts aimed at optimising the current state-of-the-art of early recognition. Finally, multivariate modelling and machine learning methods are presented as a novel methodological framework facilitating the decoding of early psychosis into different intermediate phenotypes. In the future, these phenotypes could be employed for a more objective risk stratification that operates at the single-subject level. This could allow us to generate clinically applicable prognostic biomarkers for these disorders that would propel the individualised prevention of disease transition, chronification and psychopharmacological treatment resistance of psychotic disorders.

Resting EEG in psychosis and at-risk populations--a possible endophenotype?

BACKGROUND

Finding reliable endophenotypes for psychosis could lead to an improved understanding of aetiology, and provide useful alternative phenotypes for genetic association studies. Resting quantitative electroencephalography (QEEG) activity has been shown to be heritable and reliable over time. However, QEEG research in patients with psychosis has shown inconsistent and even contradictory findings, and studies of at-risk populations are scarce. Hence, this study aimed to investigate whether resting QEEG activity represents a candidate endophenotype for psychosis.

METHOD

QEEG activity at rest was compared in four frequency bands (delta, theta, alpha, and beta), between chronic patients with psychosis (N=48), first episode patients (N=46), at-risk populations ("at risk mental state", N=33; healthy relatives of patients, N=45), and healthy controls (N=107).

RESULTS

Results showed that chronic patients had significantly increased resting QEEG amplitudes in delta and theta frequencies compared to healthy controls. However, first episode patients and at-risk populations did not differ from controls in these frequency bands. There were no group differences in alpha or beta frequency bands.

CONCLUSION

Since no abnormalities were found in first episode patients, ARMS, or healthy relatives, resting QEEG activity in the frequency bands examined is unlikely to be related to genetic predisposition to psychosis. Rather than endophenotypes, the low frequency abnormalities observed in chronic patients are probably related to illness progression and/or to the long-term effects of treatments.

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

BACKGROUND

Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis.

METHODS

This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis.

RESULTS

Cox regression yielded a model including frontal theta (HR=1.82; [95% CI 1.00-3.32]) and delta (HR=2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR=.52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81).

CONCLUSIONS

Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.

Can cognitive deficits facilitate differential diagnosis between at-risk mental state for psychosis and depressive disorders?

AIM

Many studies have provided evidence of cognitive deficits in individuals in an 'At Risk Mental State' (ARMS) for psychosis, which makes neuropsychology potentially useful in the early detection of psychosis. As depression is an important differential diagnosis in prodromal states of psychosis, the specificity of neurocognitive deficits in ARMS individuals as compared with non-psychotic depressive disorders is investigated.

METHODS

Neurocognitive performance of four groups was analysed: 22 ARMS individuals with later transition to psychosis (ARMS-T), 25 ARMS individuals without later transition to psychosis (ARMS-NT), 34 controls with depressive disorders and 76 healthy controls. The subjects were assessed with a neurocognitive test battery covering the domains' intelligence, executive function and attention/ working memory. MANOVAs, ANOVAs and Tukey's tests were applied after adjustment for confounding factors.

RESULTS

ARMS-T showed significant cognitive deficits in working memory and in certain executive function tasks compared with healthy controls as well as with controls with depression. Controls with depression were only impaired in time per move in the tower of Hanoi test when compared with healthy controls.

CONCLUSIONS

The psychosis prodrome seems to be associated with cognitive deficits in the domains of working memory and executive function. In contrast, depressive patients showed no cognitive deficits, but slowing in one executive function task. Neurocognitive testing might therefore contribute to the differential diagnosis between prodromal psychosis and depressive disorders.

Basic disturbances of information processing in psychosis prediction

The basic symptoms (BS) approach provides a valid instrument in predicting psychosis onset and represents moreover a significant heuristic framework for research. The term "basic symptoms" denotes subtle changes of cognition and perception in the earliest and prodromal stages of psychosis development. BS are thought to correspond to disturbances of neural information processing. Following the heuristic implications of the BS approach, the present paper aims at exploring disturbances of information processing, revealed by functional magnetic resonance imaging (fMRI) and electro-encephalographic as characteristics of the at-risk state of psychosis. Furthermore, since high-risk studies employing ultra-high-risk criteria revealed non-conversion rates commonly exceeding 50%, thus warranting approaches that increase specificity, the potential contribution of neural information processing disturbances to psychosis prediction is reviewed. In summary, the at-risk state seems to be associated with information processing disturbances. Moreover, fMRI investigations suggested that disturbances of language processing domains might be a characteristic of the prodromal state. Neurophysiological studies revealed that disturbances of sensory processing may assist psychosis prediction in allowing for a quantification of risk in terms of magnitude and time. The latter finding represents a significant advancement since an estimation of the time to event has not yet been achieved by clinical approaches. Some evidence suggests a close relationship between self-experienced BS and neural information processing. With regard to future research, the relationship between neural information processing disturbances and different clinical risk concepts warrants further investigations. Thereby, a possible time sequence in the prodromal phase might be of particular interest.

The psychosis high-risk state: a comprehensive state-of-the-art review

CONTEXT

During the past 2 decades, a major transition in the clinical characterization of psychotic disorders has occurred. The construct of a clinical high-risk (HR) state for psychosis has evolved to capture the prepsychotic phase, describing people presenting with potentially prodromal symptoms. The importance of this HR state has been increasingly recognized to such an extent that a new syndrome is being considered as a diagnostic category in the DSM-5.

OBJECTIVE

To reframe the HR state in a comprehensive state-of-the-art review on the progress that has been made while also recognizing the challenges that remain.

DATA SOURCES

Available HR research of the past 20 years from PubMed, books, meetings, abstracts, and international conferences.

STUDY SELECTION AND DATA EXTRACTION

Critical review of HR studies addressing historical development, inclusion criteria, epidemiologic research, transition criteria, outcomes, clinical and functional characteristics, neurocognition, neuroimaging, predictors of psychosis development, treatment trials, socioeconomic aspects, nosography, and future challenges in the field.

DATA SYNTHESIS

Relevant articles retrieved in the literature search were discussed by a large group of leading worldwide experts in the field. The core results are presented after consensus and are summarized in illustrative tables and figures.

CONCLUSIONS

The relatively new field of HR research in psychosis is exciting. It has the potential to shed light on the development of major psychotic disorders and to alter their course. It also provides a rationale for service provision to those in need of help who could not previously access it and the possibility of changing trajectories for those with vulnerability to psychotic illnesses.

Frontal delta power associated with negative symptoms in ultra-high risk individuals who transitioned to psychosis

It has recently been shown that treatment with long-chain omega-3 polyunsaturated fatty acids (PUFAs) could decrease the rate of transition to psychosis, and improve psychiatric symptoms and global functioning in people at ultra-high risk (UHR) for psychosis. Previous studies have suggested that resting state brain activity measured with electroencephalography (EEG) may represent an objective biomarker of changes in neural function associated with supplementation with omega-3 PUFAs. It has also been proposed that although resting state EEG cannot, by itself, predict transition to psychosis in UHR individuals, the combination of resting state EEG with negative symptoms may be a valid predictor of transition. The present study investigated whether treatment with omega-3 PUFAs influenced resting state EEG in UHR participants, and whether or not the association of the participants' resting state EEG with their levels of negative symptoms was dependent on their transition status. The brain activity of 73 UHR participants was recorded in the context of a randomized, placebo-controlled trial of the effects of supplementation with omega-3 PUFAs. The UHR participants who subsequently transitioned to psychosis (UHR+) did not differ from those who did not transition (UHR-) in terms of resting state EEG power in any frequency band. However, negative symptom scores were associated with increased delta activity in the frontal region of the UHR+ participants, but not in the UHR- participants. Treatment with omega-3 PUFAs did not induce changes in resting state EEG in either group. The results suggest that decreased frontal delta activity, in combination with high levels of negative symptoms, may be a risk factor for subsequent transition to psychosis in UHR individuals.