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

N400 event-related brain potential index of semantic processing and two-year clinical outcomes in persons at high risk for psychosis: A longitudinal study

The N400 event-related brain potential (ERP) semantic priming effect reflects greater activation of contextually related versus unrelated concepts in long-term semantic memory. Deficits in this measure have been found in persons with schizophrenia and those at clinical high risk (CHR) for this disorder. In CHR patients, we previously found that these deficits predict poorer social functional outcomes after 1 year. In the present study, we tested whether these deficits predicted greater psychosis-spectrum symptom severity and functional impairment over 2 years. We measured N400 semantic priming effects at baseline in CHR patients (n = 47) who viewed prime words each followed by a related/unrelated target word at stimulus-onset asynchronies (SOAs) of 300 or 750 ms. We measured psychosis-spectrum symptoms using the Structured Interview for Prodromal Symptoms and role and social functioning with the Global Functioning: Role and Social scales, at baseline, 1 (n = 29) and 2 years (n = 25). There was a significant interaction between the N400 semantic priming effect at the 300-ms SOA and time on GF:Role scores, indicating that, contrary to expectations, smaller baseline N400 semantic priming effects were associated with more improvement in role functioning from baseline to Year 1, but baseline N400 priming effects did not predict role functioning at Year 2. N400 priming effects were not significantly associated with different trajectories in psychosis-spectrum symptoms or social functioning. Thus, CHR patients' N400 semantic priming effects did not predict clinical outcomes over 2 years, suggesting that this ERP measure may have greater value as a state or short-term prognostic neurophysiological biomarker.

Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis

BACKGROUND AND HYPOTHESIS

Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes.

STUDY DESIGN

We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score.

STUDY RESULTS

After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores.

CONCLUSIONS

Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.

Value of P300 amplitude in the diagnosis of untreated first-episode schizophrenia and psychosis risk syndrome in children and adolescents

BACKGROUND

Identifying the characteristic neurobiological changes of early psychosis is helpful for early clinical diagnosis. However, previous studies on the brain electrophysiology of children and adolescents with psychosis are rare.

METHODS

This study compared P300 amplitude at multiple electrodes between children and adolescents with first-episode schizophrenia (FES, n = 48), children and adolescents with psychosis risk syndrome (PRS, n = 24), and healthy controls (HC, n = 30). Receiver operating characteristic (ROC) analysis was used to test the ability of P300 amplitude to distinguish FES, PRS and HC individuals.

RESULTS

The P300 amplitude in the FES group were significantly lower than those in the HC at the Cz, Pz, and Oz electrodes. The P300 amplitude was also significantly lower in the prodromal group than in the HC at the Pz and Oz electrodes. ROC curve analysis showed that at the Pz electrode, the P300 amplitude evoked by the target and standard stimulus showed high sensitivity, specificity, accuracy, and area under the curve value for distinguishing FES from HC individuals.

CONCLUSIONS

This study found early visual P300 deficits in children and adolescents with FES and PRS, with the exclusion of possible influence of medication and chronic medical conditions, suggesting the value of P300 amplitude for the identification of early psychosis.

Developmental trajectories of premorbid functioning predict cognitive remediation treatment response in first-episode schizophrenia

BACKGROUND

Cognitive development after schizophrenia onset can be shaped by interventions such as cognitive remediation, yet no study to date has investigated whether patterns of early behavioral development may predict later cognitive changes following intervention. We therefore investigated the extent to which premorbid adjustment trajectories predict cognitive remediation gains in schizophrenia.

METHODS

In a total sample of 215 participants (170 first-episode schizophrenia participants and 45 controls), we classified premorbid functioning trajectories from childhood through late adolescence using the Cannon-Spoor Premorbid Adjustment Scale. For the 62 schizophrenia participants who underwent 6 months of computer-assisted, bottom-up cognitive remediation interventions, we identified MATRICS Consensus Cognitive Battery scores for which participants demonstrated mean changes after intervention, then evaluated whether developmental trajectories predicted these changes.

RESULTS

Growth mixture models supported three premorbid functioning trajectories: stable-good, deteriorating, and stable-poor adjustment. Schizophrenia participants demonstrated significant cognitive remediation gains in processing speed, verbal learning, and overall cognition. Notably, participants with stable-poor trajectories demonstrated significantly greater improvements in processing speed compared to participants with deteriorating trajectories.

CONCLUSIONS

This is the first study to our knowledge to characterize the associations between premorbid functioning trajectories and cognitive remediation gains after schizophrenia onset, indicating that 6 months of bottom-up cognitive remediation appears to be sufficient to yield a full standard deviation gain in processing speed for individuals with early, enduring functioning difficulties. Our findings highlight the connection between trajectories of premorbid and postmorbid functioning in schizophrenia and emphasize the utility of considering the lifespan developmental course in personalizing therapeutic interventions.

Visual P3 abnormalities in patients with first-episode schizophrenia, unaffected siblings of schizophrenia patients and individuals at ultra-high risk for psychosis

Cued version of the continuous performance test (AX-CPT) assesses sustained attention, working memory and cognitive control processes, which have been reported as impaired in schizophrenia. This study investigated visual P3 event-related potential elicited during cued CPT in patients with schizophrenia and in individuals who were at clinical or genetic high risk for psychosis to determine whether any abnormality may provide a marker of vulnerability for psychosis. Visual P3 elicited during cued CPT have not been reported in individuals at high risk for psychosis. Visual Go and NoGo P3 potentials were assessed in 34 antipsychotic-naive patients with first-episode schizophrenia (FES), 25 clinically unaffected siblings of these patients (familial high-risk for psychosis, FHR), 49 antipsychotic-naive individuals at ultra-high risk for psychosis (UHR) and 37 healthy control (HC) participants. FES patients had overall smaller P3 amplitudes than all other groups. P3 amplitude of the UHR participants was in-between the HC participants and FES patients. The anteroposterior P3 topography differed between the groups, with FES patients and FHR participants showing a more frontally distributed P3 compared with the HC participants. These findings suggest that the reduction in visual P3 amplitude may provide a vulnerability marker for psychosis in individuals who are at clinical high risk for psychosis and that a more frontally distributed visual P3 may be a marker of genetic liability for psychosis.

Prediction of psychosis: model development and internal validation of a personalized risk calculator

BACKGROUND

Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years.

METHODS

Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis.

RESULTS

The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels.

CONCLUSIONS

Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

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.

A systematic review of premorbid cognitive functioning and its timing of onset in schizophrenia spectrum disorders

Cognitive impairments are core features of established schizophrenia spectrum disorders (SSD). However, it remains unclear whether specific cognitive functions are differentially impaired pre-onset and at what age these impairments can be detected. The purpose of this review was to elucidate these issues through a systematic summary of results from longitudinal studies investigating impairment in specific cognitive domains as antecedents of SSD. Relevant studies were identified by electronic and manual literature searches and included any original study of cognitive domains any time pre-onset of SSDs that included a control group. Effect sizes were calculated by domain for studies comparing high-risk participants who developed SSD with those who did not. The strongest evidence for impairment pre-onset was for mental processing speed, verbal learning and memory, executive function, and social cognition. Some verbal impairments, like language abilities at age 3 and verbal learning and memory at age 7, may develop as static deficits. Conversely, some non-verbal impairments, like mental processing speed, visuospatial abilities, and visual working memory manifest as developmental lag and become significant later in life. Most effect sizes were small to moderate, except for verbal fluency (d' = 0,85), implying this impairment as central in high-risk participants who develop SSD. The present review documents extensive cognitive impairments pre-onset of SSD, and that these impairments start early in life, in line with the neurodevelopmental hypothesis of schizophrenia. Increased knowledge about cognitive impairments preonset can provide a better basis for understanding the complex pathogenesis of SSD as well as informing cognitive remediation programs.

N400 event-related brain potential and functional outcome in persons at clinical high risk for psychosis: A longitudinal study

BACKGROUND

The N400 event-related brain potential (ERP) semantic priming effect is thought to reflect activation by meaningful stimuli of related concepts in semantic memory and has been found to be deficient in schizophrenia. We tested the hypothesis that, among individuals at clinical high risk (CHR) for psychosis, N400 semantic priming deficits predict worse symptomatic and functional outcomes after one year.

METHODS

We measured N400 semantic priming at baseline in CHR patients (n = 47) and healthy control participants (n = 25) who viewed prime words each followed by a related or unrelated target word, at stimulus-onset asynchronies (SOAs) of 300 or 750 ms. We measured patients' psychosis-like symptoms with the Scale of Prodromal Symptoms (SOPS) Positive subscale, and academic/occupational and social functioning with the Global Functioning (GF):Role and Social scales, respectively, at baseline and one-year follow-up (n = 29).

RESULTS

CHR patients exhibited less N400 semantic priming than controls across SOAs; planned contrasts indicated this difference was significant at the 750-ms but not the 300-ms SOA. In patients, reduced N400 semantic priming at the 750-ms SOA was associated with lower GF:Social scores at follow-up, and greater GF:Social decrements from baseline to follow-up. Patients' N400 semantic priming was not associated with SOPS Positive or GF:Role scores at follow-up, or change in these from baseline to follow-up.

CONCLUSIONS

In CHR patients, reduced N400 semantic priming at baseline predicted worse social functioning after one year, and greater decline in social functioning over this period. Thus, the N400 may be a useful prognostic biomarker of real-world functional outcome in CHR patients.

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.

Follow-up of subjects labelled with putative pre-psychotic states: Viewed from a transdiagnostic clinical high-at-risk mental state (CHARMS) paradigm

BACKGROUND

Follow-up of subjects with putative pre-psychotic states is essential to clarify the transition process to psychosis, while "non-converters" also deserve clinical attention as many may evolve into other psychiatric disorders with diverse outcomes. This study aimed to examine help-seeking individuals who have been labelled at clinical high-risk state but not converting to full-blown psychosis during first two years of follow-up.

METHODS

A retrospective observational cohort study of help-seeking subjects was conducted by reviewing medical records of participants in a previous early psychosis study at the study hospital between 2006 and 2020. We portrayed those who developed first episode psychosis after first 2-year follow-up in detail, and provided sketches of clinical macrophenotypes other than psychosis emerging from subjects among different risk groups.

RESULTS

Among 132 eligible subjects, data of 98 (74.2%) were available for detailed evaluation. Of these, 15 transitioned to first-episode psychosis (11.4%) with time to psychosis from 2 to 11 years, 11 had anxiety spectrum (8.3%), 11 had depressive spectrum (8.3%), 10 had obsessive compulsive (7.6%), 5 had bipolar spectrum disorders (3.8%), 13 had predominantly schizotypal (9.8%) and 4 had other personality traits (3%), and 13 had problems attributable to adjustment or developmental issues (9.8%).

CONCLUSIONS

Various diagnoses, either full- or sub-threshold, appropriately describe the diverse clinical phenomenology of a cohort presenting with non-specific and/or subthreshold psychotic symptoms. The clinical high-at-risk mental state (CHARMS) paradigm provides a reasonable transdiagnostic approach for orienting clinicians' attention toward young subjects seeking mental health help at an early stage of illness to potentially pluripotent trajectories.

Prediction of clinical outcomes beyond psychosis in the ultra-high risk for psychosis population

AIM

Several prediction models have been introduced to identify young people at greatest risk of transitioning to psychosis. To date, none has examined the possibility of developing a clinical prediction model of outcomes other than transition. The aims of this study were to examine the association between baseline clinical predictors and outcomes including, but not limited to, transition to psychosis in young people at risk for psychosis, and to develop a prediction model for these outcomes.

METHODS

Several evidence-based variables previously associated with transition to psychosis and some important clinical comorbidities experienced by ultra-high risk (UHR) individuals were identified in 202 UHR individuals. Secondary analysis of the Neurapro clinical trial were conducted to investigate the associations between these variables and favourable (remission and recovery) or unfavourable (transition to psychosis, no remission, any recurrence and relapse) clinical outcomes. Logistic regression, best subset selection, Akaike Information Criterion and receiver operating characteristic curves were used to seek the best prediction model for clinical outcomes from all combinations of possible predictors.

RESULTS

When considered individually, only higher general psychopathology levels (P = .023) was associated with the unfavourable outcomes. Prediction models suggest that general psychopathology and functioning are predictive of unfavourable outcomes.

CONCLUSION

The predictive performance of the resulting models was modest and further research is needed. Nonetheless, when designing early intervention centres aiming to support individuals in the early phases of a mental disorder, the proper assessment of general psychopathology and functioning should be considered in order to inform interventions and length of care provided.

Timing dysfunction and cerebellar resting state functional connectivity abnormalities in youth at clinical high-risk for psychosis

BACKGROUND

Consistent with pathophysiological models of psychosis, temporal disturbances in schizophrenia spectrum populations may reflect abnormal cortical (e.g. prefrontal cortex) and subcortical (e.g. striatum) cerebellar connectivity. However, few studies have examined associations between cerebellar connectivity and timing dysfunction in psychosis populations, and none have been conducted in youth at clinical high-risk (CHR) for psychosis. Thus, it is currently unknown if impairments in temporal processes are present in CHR youth or how they may be associated with cerebellar connectivity and worsening of symptoms.

METHODS

A total of 108 (56 CHR/52 controls) youth were administered an auditory temporal bisection task along with a resting state imaging scan to examine cerebellar resting state connectivity. Positive and negative symptoms at baseline and 12 months later were also quantified.

RESULTS

Controlling for alcohol and cannabis use, CHR youth exhibited poorer temporal accuracy compared to controls, and temporal accuracy deficits were associated with abnormal connectivity between the bilateral anterior cerebellum and a right caudate/nucleus accumbens striatal cluster. Poor temporal accuracy accounted for 11% of the variance in worsening of negative symptoms over 12 months.

CONCLUSIONS

Behavioral findings suggest CHR youth perceive durations of auditory tones as shortened compared to objective time, which may indicate a slower internal clock. Poorer temporal accuracy in CHR youth was associated with abnormalities in brain regions involved in an important cerebellar network implicated in prominent pathophysiological models of psychosis. Lastly, temporal accuracy was associated with worsening of negative symptoms across 12 months, suggesting temporal dysfunction may be sensitive to illness progression.

Prolonged P300 Latency in Antipsychotic-Free Subjects with At-Risk Mental States Who Later Developed Schizophrenia

We measured P300, an event-related potential, in subjects with at-risk mental states (ARMS) and aimed to determine whether P300 parameter can predict progression to overt schizophrenia. Thirty-three subjects with ARMS, 39 with schizophrenia, and 28 healthy controls participated in the study. All subjects were antipsychotic-free. Subjects with ARMS were followed-up for more than two years. Cognitive function was measured by the Brief assessment of Cognition in Schizophrenia (BACS) and Schizophrenia Cognition Rating Scale (SCoRS), while the modified Global Assessment of Functioning (mGAF) was used to assess global function. Patients with schizophrenia showed smaller P300 amplitudes and prolonged latency at Pz compared to those of healthy controls and subjects with ARMS. During the follow-up period, eight out of 33 subjects with ARMS developed overt psychosis (ARMS-P) while 25 did not (ARMS-NP). P300 latency of ARMS-P was significantly longer than that of ARMS-NP. At baseline, ARMS-P elicited worse cognitive functions, as measured by the BACS and SCoRS compared to ARMS-NP. We also detected a significant relationship between P300 amplitudes and mGAF scores in ARMS subjects. Our results suggest the usefulness of prolonged P300 latency and cognitive impairment as a predictive marker of later development of schizophrenia in vulnerable individuals.

Forecasting Remission From the Psychosis Risk Syndrome With Mismatch Negativity and P300: Potentials and Pitfalls

Clinical outcomes vary for individuals at clinical high risk (CHR) for psychosis, ranging from conversion to a psychotic disorder to full remission from the risk syndrome. Given that most CHR individuals do not convert to psychosis, recent research efforts have turned toward identifying specific predictors of CHR remission, a task that is conceptually and empirically dissociable from the identification of predictors of conversion to psychosis, and one that may reveal specific biological characteristics that confer resilience to psychosis and provide further insights into the mechanisms associated with the pathogenesis of schizophrenia and those underlying a transient CHR syndrome. Such biomarkers may ultimately facilitate the development of novel early interventions and support the optimization of individualized care. In this review, we focus on two event-related brain potential measures, mismatch negativity and P300, that have attracted interest as predictors of future psychosis among CHR individuals. We describe several recent studies examining whether mismatch negativity and P300 predict subsequent CHR remission and suggest that intact mismatch negativity and P300 may reflect the integrity of specific neurocognitive processes that confer resilience against the persistence of the CHR syndrome and its associated risk for future transition to psychosis. We also highlight several major methodological concerns associated with these studies that apply to the broader literature examining predictors of CHR remission. Among them is the concern that studies that predict dichotomous remission versus nonremission and/or dichotomous conversion versus nonconversion outcomes potentially confound remission and conversion effects, a phenomenon we demonstrate with a data simulation.

N400 event-related brain potential as an index of real-world and neurocognitive function in patients at clinical high risk for schizophrenia

AIM

The N400 event-related potential is a neurophysiological index of cognitive processing of real-world knowledge. In healthy populations, N400 amplitude is smaller in response to stimuli that are more related to preceding context. This 'N400 semantic priming effect' is thought to reflect activation of contextually related information in semantic memory (SM). N400 semantic priming deficits have been found in schizophrenia, and in patients at clinical high risk (CHR) for this disorder. Because this abnormality in processing relationships between meaningful stimuli could affect ability to navigate everyday situations, we hypothesized it would be associated with real-world functional impairment in CHR patients. Second, we hypothesized it would correlate with global neurocognitive impairment in this group.

METHODS

We measured N400 semantic priming in 35 CHR patients who viewed prime words each followed by a related or unrelated target word, at stimulus-onset asynchrony (SOA) of 300 or 750 ms. We measured academic/occupational and social function with the global function (GF): Role and Social scales, and cognitive function with the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

Decreased N400 semantic priming at the 300-ms SOA correlated with lower GF:Role scores. Decreased N400 semantic priming at the 750-ms SOA correlated with lower MCCB composite scores.

CONCLUSIONS

Deficits in activating contextually related concepts in SM over short time intervals may contribute to functional impairment in CHR patients. Furthermore, N400 priming deficits over longer intervals may be a biomarker of global cognitive dysfunction in this population. Longitudinal studies are needed to determine whether these deficits are associated with schizophrenia risk within this population.

Relationships between cognitive event-related brain potential measures in patients at clinical high risk for psychosis

Neurophysiological measures of cognitive functioning that are abnormal in patients with schizophrenia are promising candidate biomarkers for predicting development of psychosis in individuals at clinical high risk (CHR). We examined the relationships among event-related brain potential (ERP) measures of early sensory, pre-attentional, and attention-dependent cognition, in antipsychotic-naïve help-seeking CHR patients (n = 36) and healthy control participants (n = 22). These measures included the gamma auditory steady-state response (ASSR; early sensory); mismatch negativity (MMN) and P3a (pre-attentional); and N400 semantic priming effects - a measure of using meaningful context to predict related items - over a shorter and a longer time interval (attention-dependent). Compared to controls, CHR patients had significantly smaller P3a amplitudes (d = 0.62, p = 0.03) and N400 priming effects over the long interval (d = 0.64, p = 0.02). In CHR patients, gamma ASSR evoked power and phase-locking factor were correlated (r = 0.41, p = 0.03). Reductions in mismatch negativity (MMN) and P3a amplitudes were also correlated (r = -0.36, p = 0.04). Moreover, lower gamma ASSR evoked power correlated with smaller MMN amplitudes (r = -0.45, p = 0.02). MMN amplitude reduction was also associated with reduced N400 semantic priming over the shorter but not the longer interval (r = 0.52, p < 0.002). This pattern of results suggests that, in a subset of CHR patients, impairment in pre-attentional measures of early information processing may contribute to deficits in attention-dependent cognition involving rapid, more automatic processing, but may be independent from pathological processes affecting more controlled or strategic processing. Thus, combining neurophysiological indices of cognitive deficits in different domains offers promise for improving their predictive power as prognostic biomarkers of clinical outcome.

Expanding the search for emerging mental ill health to safeguard student potential and vocational success in high school: A narrative review

AIM

Young people experiencing mental ill health are more likely than their healthy aged peers to drop out of high school. This can result in social exclusion and vocational derailment. Identifying young people at risk and taking action before an illness is established or school dropout occurs is an important goal. This study aimed to examine evidence for the risk markers and at risk mental states of the clinical staging model (stage 0-1b) and whether these risk states and early symptoms impact school participation and academic attainment.

METHOD

This narrative review assembles research from both the psychiatry and education literature. It examines stage 0 to stage 1b of the clinical staging model and links the risk states and early symptoms to evidence about the academic success of young people in high school.

RESULTS

In accordance with the clinical staging model and evidence from education literature, childhood trauma and parental mental illness can impact school engagement and academic progress. Sleep disturbance can result in academic failure. Undifferentiated depression and anxiety can increase the risk for school dropout. Subthreshold psychosis and hypomanic states are associated with functional impairment and high rates of Not in Employment, Education, or Training (NEET) but are not recognized in the education literature.

CONCLUSION

Risk markers for emerging mental ill health can be identified in education research and demonstrate an impact on a student's success in high school. Clear referral protocols need to be embedded into school life to reduce risk of progression to later stages of illness and support school participation and success.

P300 as an index of transition to psychosis and of remission: Data from a clinical high risk for psychosis study and review of literature

Auditory P300 oddball and novel components index working memory operations and salience processing, respectively, and are regarded as biomarkers of neurocognitive changes in both chronic and first-episode schizophrenia. Much less is known about whether P300 abnormalities exist in individuals at clinical high risk for psychosis (CHR) and if they are predictors of both transition to psychosis and remission from symptoms. One hundred and four CHR and 69 healthy control individuals (HC) completed P300 oddball paradigm, and 131 CHR and 69 HC subjects completed P300 novel paradigm. All CHR subjects were followed up for one year and stratified into CHR converters (CHRC) and non-converters (CHR-NC), with CHR-NC further stratified into remitted and non-remitted subgroups. Between-group comparisons of P300 oddball and novel amplitude and latency were performed among CHRC, CHR-NC and HC, as well as among CHRC, non-remitted CHR, remitted CHR and HC. CHR converters had lower fronto-central P300 novel amplitude as well as marginally lower P300 oddball amplitude relative to HC. When CHR non-converters were stratified into remitted and non-remitted subgroups, P300 novel amplitude in remitted CHR subjects was comparable to HC, and it was higher than that in CHR subjects who converted to psychosis or who did not remit. Thus, reduced P300 novel amplitude indexing impaired salience processing marked both conversion to psychosis and remission from psychotic symptoms.

Longitudinal evaluation of visual P300 amplitude in clinical high-risk subjects: An event-related potential study

AIM

We previously reported abnormal P300 and N200 in a visual oddball task, and progressive P300 amplitude reduction at 1-year follow-up in patients with first-episode schizophrenia. P300 reduction as well as intact P1/N1 were also observed in clinical high-risk subjects (CHR), but whether or not these components change over time is unknown. This study evaluates, longitudinally, the visual P300, as well as P1, N1, and N200, in CHR.

METHODS

Visual event-related potentials (ERP) were recorded twice, once at baseline and once at 1-year follow-up in CHR (n = 19) and healthy comparison subjects (HC; n = 28). Participants silently counted infrequent target stimuli ('x') among standard stimuli ('y') presented on the screen while the 64-channel electroencephalogram was recorded.

RESULTS

No CHR converted to psychosis from baseline to 1-year follow-up in this study. Visual P300 amplitude was reduced and the latency was delayed significantly in CHR at both time points compared with HC. Furthermore, CHR subjects who had more positive symptoms showed more amplitude reduction at both time points. P1, N1, and N200 did not differ between groups.

CONCLUSION

Visual P300 amplitude was found to be reduced in CHR individuals compared with HC. We note that this finding is in subjects who did not convert to psychosis at 1-year follow-up. The association between visual P300 amplitude and symptoms suggests that for CHR who often experience clinical symptoms and seek medical care, visual P300 may be an important index that reflects the pathophysiological impairment underlying such clinical states.

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.

Familial and socioeconomic contributions to premorbid functioning in psychosis: Impact on age at onset and treatment response

Background.

Premorbid adjustment (PA) abnormalities in psychotic disorders are associated with an earlier age at onset (AAO) and unfavorable clinical outcomes, including treatment resistance. Prior family studies suggest that familial liability, likely reflecting increased genetic risk, and socioeconomic status (SES) contribute to premorbid maladjustment. However, their joint effect possibly indicating gene–environment interaction has not been evaluated.

Methods.

We examined whether family history of psychosis (FHP) and parental SES may predict PA and AAO in unrelated cases with first-episode psychosis (n = 108) and schizophrenia (n = 104). Premorbid academic and social functioning domains during childhood and early adolescence were retrospectively assessed. Regression analyses were performed to investigate main effects of FHP and parental SES, as well as their interaction. The relationships between PA, AAO, and response to antipsychotic medication were also explored.

Results.

Positive FHP associated with academic PA difficulties and importantly interacted with parental SES to moderate social PA during childhood (interaction p = 0.024). Positive FHP and parental SES did not predict differences in AAO. Nevertheless, an earlier AAO was observed among cases with worse social PA in childhood (β = −0.20; p = 0.005) and early adolescence (β = −0.19; p = 0.007). Further, confirming evidence emerged for an association between deficient childhood social PA and poor treatment response (p = 0.04).

Conclusions.

Familial risk for psychosis may interact with parental socioeconomic position influencing social PA in childhood. In addition, this study supports the link between social PA deviations, early psychosis onset, and treatment resistance, which highlights premorbid social functioning as a promising clinical indicator.

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.

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.

Brain functional connectivity data enhance prediction of clinical outcome in youth at risk for psychosis

The first episode of psychosis is typically preceded by a prodromal phase with subthreshold symptoms and functional decline. Improved outcome prediction in this stage is needed to allow targeted early intervention. This study assesses a combined clinical and resting-state fMRI prediction model in 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome at one-year follow-up, participants were separated into three outcome categories including good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Validated clinical predictors from the psychosis-risk calculator were combined with measures of resting-state functional connectivity. Using multinomial logistic regression analysis and leave-one-out cross-validation, a clinical-only prediction model did not achieve a significant level of outcome prediction (F₁ = 0.32, p = .154). An imaging-only model yielded a significant prediction model (F₁ = 0.41, p = .016), but a combined model including both clinical and connectivity measures showed the best performance (F₁ = 0.46, p < .001). Influential predictors in this model included functional decline, verbal learning performance, a family history of psychosis, default-mode and frontoparietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, sensorimotor, and cerebellar networks. These findings suggest that brain changes reflected by alterations in functional connectivity may be useful for outcome prediction in the prodromal stage.

Psychosocial characteristics differentiate non-distressing and distressing voices in 10,346 adolescents

Adolescents hearing non-existent voices may be at risk for psychosis, but the prevalence of voice-hearing (VH) in the general population complicates clinical interpretations. Differentiating between VH with and without distress may aid treatment decisions in psychosis services, but understanding the differences between these two phenomena as they present in the normal adolescent population is necessary to validate this differentiation. The present study compared VH with and without distress in 10,346 adolescents in relation to clinical characteristics, known risk factors, predictors and psychosocial moderators of psychosis. A population-based cohort of Norwegian 16-19 years old adolescents completed a comprehensive web-based questionnaire, including two questions from the extended Launay-Slade Hallucinations Scale: (1) I often hear a voice speaking my thoughts aloud and (2) I have been troubled by hearing voices in my head. Adolescents reporting no VH, non-distressing VH or distressing VH were compared on 14 psychosocial and clinical variables. A multinomial regression model showed that non-disturbing voices were predicted by better school grades, social dysfunction, distractibility, affective symptoms and experience of trauma, while the disturbing voices were predicted by the experience of bullying and trauma, perceived negative self-worth and self-efficacy, less family support, dysregulation of activation, distractibility, self-harm and anxiety. Hearing voices without distress versus being distressed by the voices is related to different constellations of psychosocial variables, suggesting that they represent two separate groups of adolescents. The findings validate the emphasis on distress in clinical practice.

The Global Functioning: Social and Role Scales-Further Validation in a Large Sample of Adolescents and Young Adults at Clinical High Risk for Psychosis

OBJECTIVE

Traditional measures for assessing functioning with adult patients with schizophrenia have been shown to be insufficient for assessing the issues that occur in adolescents and young adults at clinical high risk (CHR) for psychosis. The current study provides an expanded validation of the Global Functioning: Social (GF:Social) and Role (GF:Role) scales developed specifically for use with CHR individuals and explores the reliability and accuracy of the ratings, the validity of the scores in comparison to other established clinical measures, stability of functioning over a 2-year period, and psychosis predictive ability.

METHODS

Seven hundred fifty-five CHR individuals and 277 healthy control (HC) participants completed the GF:Social and Role scales at baseline as part of the North American Prodrome Longitudinal Study (NAPLS2).

RESULTS

Inter-rater reliability and accuracy were high for both scales. Correlations between the GF scores and other established clinical measures demonstrated acceptable convergent and discriminant validity. In addition, GF:Social and Role scores were unrelated to positive symptoms. CHR participants showed large impairments in social and role functioning over 2-years, relative to the HCs, even after adjusting for age, IQ, and attenuated positive symptoms. Finally, social decline prior to baseline was more pronounced in CHR converters, relative to non-converters.

CONCLUSIONS

The GF scales can be administered in a large-scale multi-site study with excellent inter-rater reliability and accuracy. CHR individuals showed social and role functioning impairments over time that were not confounded by positive symptom severity levels. The results of this study demonstrate that social decline is a particularly effective predictor of conversion outcome.

Relationship between metacognitive beliefs and psychosocial performance in at-risk states of psychosis and patients with first psychotic episodes

AIMS

Improving diagnostic batteries to identify individuals at-risk for developing psychotic disorders as early as possible is an ongoing challenge in schizophrenia research. Here, we sought to explore whether metacognition in at-risk of developing psychosis would differ from that of first episode psychosis and unaffected controls and whether dysfunctional metacognitive beliefs would be associated with psychosocial functioning in the clinical groups.

METHODS

Twenty-three subjects at-risk of psychosis were compared with a group of 15 first psychotic episode patients and 21 healthy controls with regard to their metacognitive beliefs and psychosocial functioning. Metacognition was assessed using the Metacognition Questionnaire (MCQ), psychosocial functioning was rated using the Personal and Social Performance Scale (PSP). Depression and anxiety were also evaluated.

RESULTS

The clinical groups differed significantly from controls in several MCQ scores, particularly the subscales "negative beliefs" and "need for control," as well as on all PSP scales. Furthermore, significant correlations emerged between the metacognition and psychosocial functioning. A mediation analysis revealed that dysfunctional metacognitive beliefs had no direct effect on psychosocial functioning, but was mediated by depressive symptoms.

CONCLUSIONS

These results corroborate findings assigning depressive symptoms an important role in early recognition of psychosis.

Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis

Background: The Clinical High Risk state for Psychosis (CHR-P) has become the cornerstone of modern preventive psychiatry. The next stage of clinical advancements rests on the ability to formulate a more accurate prognostic estimate at the individual subject level. Individual Participant Data Meta-Analyses (IPD-MA) are robust evidence synthesis methods that can also offer powerful approaches to the development and validation of personalized prognostic models. The aim of the study was to develop and validate an individualized, clinically based prognostic model for forecasting transition to psychosis from a CHR-P stage. Methods: A literature search was performed between January 30, 2016, and February 6, 2016, consulting PubMed, Psychinfo, Picarta, Embase, and ISI Web of Science, using search terms ("ultra high risk" OR "clinical high risk" OR "at risk mental state") AND [(conver* OR transition* OR onset OR emerg* OR develop*) AND psychosis] for both longitudinal and intervention CHR-P studies. Clinical knowledge was used to a priori select predictors: age, gender, CHR-P subgroup, the severity of attenuated positive psychotic symptoms, the severity of attenuated negative psychotic symptoms, and level of functioning at baseline. The model, thus, developed was validated with an extended form of internal validation. Results: Fifteen of the 43 studies identified agreed to share IPD, for a total sample size of 1,676. There was a high level of heterogeneity between the CHR-P studies with regard to inclusion criteria, type of assessment instruments, transition criteria, preventive treatment offered. The internally validated prognostic performance of the model was higher than chance but only moderate [Harrell's C-statistic 0.655, 95% confidence interval (CIs), 0.627-0.682]. Conclusion: This is the first IPD-MA conducted in the largest samples of CHR-P ever collected to date. An individualized prognostic model based on clinical predictors available in clinical routine was developed and internally validated, reaching only moderate prognostic performance. Although personalized risk prediction is of great value in the clinical practice, future developments are essential, including the refinement of the prognostic model and its external validation. However, because of the current high diagnostic, prognostic, and therapeutic heterogeneity of CHR-P studies, IPD-MAs in this population may have an limited intrinsic power to deliver robust prognostic models.

Psychosis Polyrisk Score (PPS) for the Detection of Individuals At-Risk and the Prediction of Their Outcomes

Primary prevention in individuals at Clinical High Risk for psychosis (CHR-P) can ameliorate the course of psychotic disorders. Further advancements of knowledge have been slowed by the standstill of the field, which is mostly attributed to its epidemiological weakness. The latter, in turn, underlies the limited identification power of at-risk individuals and the relatively modest ability of CHR-P interviews to rule-in a state of risk for psychosis. In the first part, this perspective review discusses these limitations and traces a new approach to overcome them. Theoretical concepts to support a Psychosis Polyrisk Score (PPS) integrating genetic and non-genetic risk and protective factors for psychosis are presented. The PPS hinges on recent findings indicating that risk enrichment in CHR-P samples is accounted for by the accumulation of non-genetic factors such as: parental and sociodemographic risk factors, perinatal risk factors, later risk factors, and antecedents. In the second part of this perspective review we present a prototype of a PPS encompassing core predictors beyond genetics. The PPS prototype may be piloted in the next generation of CHR-P research and combined with genetic information to refine the detection of individuals at-risk of psychosis and the prediction of their outcomes, and ultimately advance clinical research in this field.

At-risk studies and clinical antecedents of psychosis, bipolar disorder and depression: a scoping review in the context of clinical staging

Identifying young people at risk of developing serious mental illness and identifying predictors of onset of illness has been a focus of psychiatric prediction research, particularly in the field of psychosis. Work in this area has facilitated the adoption of the clinical staging model of early clinical phenotypes, ranging from at-risk mental states to chronic and severe mental illness. It has been a topic of debate if these staging models should be conceptualised as disorder-specific or transdiagnostic. In order to inform this debate and facilitate cross-diagnostic discourse, the present scoping review provides a broad overview of the body of literature of (a) longitudinal at-risk approaches and (b) identified antecedents of (homotypic) illness progression across three major mental disorders [psychosis, bipolar disorder (BD) and depression], and places these in the context of clinical staging. Stage 0 at-risk conceptualisations (i.e. familial high-risk approaches) were identified in all three disorders. However, formalised stage 1b conceptualisations (i.e. ultra-high-risk approaches) were only present in psychosis and marginally in BD. The presence of non-specific and overlapping antecedents in the three disorders may support a general staging model, at least in the early stages of severe psychotic or mood disorders.

Dynamic prediction of transition to psychosis using joint modelling

Considerable research has been conducted seeking risk factors and constructing prediction models for transition to psychosis in individuals at ultra-high risk (UHR). Nearly all such research has only employed baseline predictors, i.e. data collected at the baseline time point, even though longitudinal data on relevant measures such as psychopathology have often been collected at various time points. Dynamic prediction, which is the updating of prediction at a post-baseline assessment using baseline and longitudinal data accumulated up to that assessment, has not been utilized in the UHR context. This study explored the use of dynamic prediction and determined if it could enhance the prediction of frank psychosis onset in UHR individuals. An emerging statistical methodology called joint modelling was used to implement the dynamic prediction. Data from the NEURAPRO study (n = 304 UHR individuals), an intervention study with transition to psychosis study as the primary outcome, were used to investigate dynamic predictors. Compared with the conventional approach of using only baseline predictors, dynamic prediction using joint modelling showed significantly better sensitivity, specificity and likelihood ratios. As dynamic prediction can provide an up-to-date prediction for each individual at each new assessment post entry, it can be a useful tool to help clinicians adjust their prognostic judgements based on the unfolding clinical symptomatology of the patients. This study has shown that a dynamic approach to psychosis prediction using joint modelling has the potential to aid clinicians in making decisions about the provision of timely and personalized treatment to patients concerned.

Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis

IMPORTANCE

Social and occupational impairments contribute to the burden of psychosis and depression. There is a need for risk stratification tools to inform personalized functional-disability preventive strategies for individuals in at-risk and early phases of these illnesses.

OBJECTIVE

To determine whether predictors associated with social and role functioning can be identified in patients in clinical high-risk (CHR) states for psychosis or with recent-onset depression (ROD) using clinical, imaging-based, and combined machine learning; assess the geographic, transdiagnostic, and prognostic generalizability of machine learning and compare it with human prognostication; and explore sequential prognosis encompassing clinical and combined machine learning.

DESIGN, SETTING, AND PARTICIPANTS

This multisite naturalistic study followed up patients in CHR states, with ROD, and with recent-onset psychosis, and healthy control participants for 18 months in 7 academic early-recognition services in 5 European countries. Participants were recruited between February 2014 and May 2016, and data were analyzed from April 2017 to January 2018.

AIN OUTCOMES AND MEASURES

Performance and generalizability of prognostic models.

RESULTS

A total of 116 individuals in CHR states (mean [SD] age, 24.0 [5.1] years; 58 [50.0%] female) and 120 patients with ROD (mean [SD] age, 26.1 [6.1] years; 65 [54.2%] female) were followed up for a mean (SD) of 329 (142) days. Machine learning predicted the 1-year social-functioning outcomes with a balanced accuracy of 76.9% of patients in CHR states and 66.2% of patients with ROD using clinical baseline data. Balanced accuracy in models using structural neuroimaging was 76.2% in patients in CHR states and 65.0% in patients with ROD, and in combined models, it was 82.7% for CHR states and 70.3% for ROD. Lower functioning before study entry was a transdiagnostic predictor. Medial prefrontal and temporo-parieto-occipital gray matter volume (GMV) reductions and cerebellar and dorsolateral prefrontal GMV increments had predictive value in the CHR group; reduced mediotemporal and increased prefrontal-perisylvian GMV had predictive value in patients with ROD. Poor prognoses were associated with increased risk of psychotic, depressive, and anxiety disorders at follow-up in patients in the CHR state but not ones with ROD. Machine learning outperformed expert prognostication. Adding neuroimaging machine learning to clinical machine learning provided a 1.9-fold increase of prognostic certainty in uncertain cases of patients in CHR states, and a 10.5-fold increase of prognostic certainty for patients with ROD.

CONCLUSIONS AND RELEVANCE

Precision medicine tools could augment effective therapeutic strategies aiming at the prevention of social functioning impairments in patients with CHR states or with ROD.

Temperament in child and adolescent offspring of patients with schizophrenia and bipolar disorder

Shared vulnerability in offspring of individuals with schizophrenia (SzO) and bipolar disorder (BpO) might manifest early during development through common temperament traits. Temperament dimensions in child and adolescent BpO (N = 80), SzO (N = 34) and the offspring of community controls (CcO) (N = 101) were assessed using the Revised Dimensions of Temperament Survey. The association between temperament dimensions and lifetime psychopathology (including threshold and subthreshold DSM-IV-TR diagnoses) and current socio-academic adjustment was assessed using logistic regression. Fully adjusted models showed that both BpO and SzO scored significantly lower in the positive mood dimension and in the adaptability factor than CcO, with small-medium effect sizes (Cohen's d ~ 0.3-0.5). BpO also scored lower in the activity factor and the activity dimensions than CcO (Cohen's d ~ 0.3). Lower scores in the positive mood dimension were associated with increased risk of impaired adjustment both in BpO [OR 2.30, 95% CI (1.18-4.46)] and in SzO [OR 2.87, 95% CI (1.07-7.66)]. In BpO, lower scores in positive mood were also associated with increased likelihood of internalizing [OR 1.84, 95% CI (1.28-2.64)] and externalizing disorders [OR 1.48, 95% CI (1.01-2.18)]; in SzO, higher scores in activity and flexibility were associated with increased likelihood of internalizing [OR 2.31, 95% CI (1.22-4.38)] and externalizing disorders [OR 3.28, 95% CI (1.2-9)], respectively. Early difficulties in emotion regulation might represent a shared vulnerability phenotype in BpO and SzO. The identification of extreme temperament traits could help to characterize subgroups at greater risk of psychopathology and impaired adjustment, in which targeted interventions are warranted.

Variation in reward- and error-related neural measures attributable to age, gender, race, and ethnicity

Event-related potentials (ERPs) have been widely applied to the study of individual differences in reward and error processing, including recent proposals of several ERPs as possible biomarkers of mental illness. A criterion for all biomarkers, however, is that they be generalizable across the relevant populations, something which has yet to be demonstrated for many commonly studied reward- and error-related ERPs. The aim of this study was to examine variation in reward and error-related ERPs across core demographic variables: age, gender, race, and ethnicity. Data was drawn from three studies with relatively large samples (N range 207-527). Results demonstrated that ERPs varied across the demographic variables of interest. Several examples include attenuated reward-related ERPs with increasing age, larger error-related ERPs for men than women, and larger ERPs to feedback after losses for individuals who identified as Hispanic/Latino. Overall, these analyses suggest systematic variation in ERPs that is attributable to core demographic variables, which could give rise to seemingly inconsistent results across studies to the extent that these sample characteristics differ. Future psychophysiological studies should include these analyses as standard practice and assess how these differences might exacerbate, mask, or confound relationships of interest.

Event-Related Potentials in the Clinical High-Risk (CHR) State for Psychosis: A Systematic Review

There is emerging evidence that identification and treatment of individuals in the prodromal or clinical high-risk (CHR) state for psychosis can reduce the probability that they will develop a psychotic disorder. Event-related brain potentials (ERPs) are a noninvasive neurophysiological technique that holds promise for improving our understanding of neurocognitive processes underlying the CHR state. We aimed to systematically review the current literature on cognitive ERP studies of the CHR population, in order to summarize and synthesize the results, and their implications for our understanding of the CHR state. Across studies, amplitudes of the auditory P300 and duration mismatch negativity (MMN) ERPs appear reliably reduced in CHR individuals, suggesting that underlying impairments in detecting changes in auditory stimuli are a sensitive early marker of the psychotic disease process. There are more limited data indicating that an earlier-latency auditory ERP response, the N100, is also reduced in amplitude, and in the degree to which it is modulated by stimulus characteristics, in the CHR population. There is also evidence that a number of auditory ERP measures (including P300, MMN and N100 amplitudes, and N100 gating in response to repeated stimuli) can further refine our ability to detect which CHR individuals are most at risk for developing psychosis. Thus, further research is warranted to optimize the predictive power of algorithms incorporating these measures, which could help efforts to target psychosis prevention interventions toward those most in need.

Detail, dynamics and depth: useful correctives for some current research trends

Several research trends in contemporary psychiatry would benefit from greater emphasis on detailed assessment, modelling dynamic change, and micro-level analysis. This may assist with clarifying nosological and pathoaetiological issues. We make this case by referring to three areas: psychopathology and nosology; prediction research; and 'big N' data sets.Declaration of interestNone.

Modulation of Electrophysiology by Transcranial Direct Current Stimulation in Psychiatric Disorders: A Systematic Review

OBJECTIVE

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique increasingly used to relieve symptoms of psychiatric disorders. Electrophysiologic markers, such as electroencephalography (EEG) and event-related potentials (ERP), have high temporal resolution sensitive to detect plastic changes of the brain associated with symptomatic improvement following tDCS application.

METHODS

We performed systematic review to identify electrophysiological markers that reflect tDCS effects on plastic brain changes in psychiatric disorders. A total of 638 studies were identified by searching PubMed, Embase, psychINFPO. Of these, 21 full-text articles were assessed eligible and included in the review.

RESULTS

Although the reviewed studies were heterogeneous in their choices of tDCS protocols, targeted electrophysiological markers, and disease entities, their results strongly support EEG/ERPs to sensitively reflect plastic brain changes and the associated symptomatic improvement following tDCS.

CONCLUSION

EEG/ERPs may serve a potent tool in revealing the mechanisms underlying psychiatric symptoms, as well as in localizing the brain area targeted for stimulation. Future studies in each disease entities employing consistent tDCS protocols and electrophysiological markers would be necessary in order to substantiate and further elaborate the findings of studies included in the present systematic review.

Predicting Remission in Subjects at Clinical High Risk for Psychosis Using Mismatch Negativity

BACKGROUND

The declining transition rate to psychotic disorder and the increasing rate of nonpsychotic poor outcomes among subjects at clinical high risk (CHR) for psychosis have increased the need for biomarkers to predict remission regardless of transition. This study investigated whether mismatch negativity (MMN) predicts the prognosis of CHR individuals during a 6-year follow-up period.

METHODS

A total of 47 healthy control (HC) subjects and 48 subjects at CHR for psychosis participated in the MMN assessment. The clinical statuses of the CHR subjects were examined at baseline and regularly for up to 6 years. The CHR subjects were divided into remitter and nonremitter groups, and the baseline MMN amplitudes and latencies were compared across the remitter, nonremitter, and HC groups. Regression analyses were performed to identify the predictive factors of remission, the improvement of attenuated positive symptoms, and functional recovery.

RESULTS

CHR nonremitters showed reduced MMN amplitudes at baseline compared to CHR remitters and HC subjects. A logistic regression analysis revealed that the baseline MMN amplitude at the frontal electrode site was the only significant predictor of remission. In a multiple regression analysis, the MMN amplitude, antipsychotic use, and years of education predicted an improvement in attenuated positive symptoms. The MMN amplitude at baseline predicted functional recovery.

CONCLUSIONS

These results suggest that MMN is a putative predictor of prognosis regardless of the transition to psychotic disorder in subjects at CHR. Early prognosis prediction and the provision of appropriate interventions based on the initial CHR status might be aided using MMN.

Predictors of longer-term outcome in the Vienna omega-3 high-risk study

Longer-term data on ω-3 polyunsaturated fatty acids (PUFA) for prevention of psychosis in (ultra high risk) UHR individuals have initially shown promising results. This analysis aimed to assess clinical predictors of longer-term outcome in UHR individuals treated with ω-3 PUFAs versus placebo. Data derived from an RCT in 81 UHR individuals treated with ω-3 PUFAs versus placebo for 12weeks and follow-up assessment after a median of 6.7years. Baseline GAF, baseline PANSS global score, pre-to-post-intervention change in EPA (Eicosapentaenoic acid) level were significant predictors of transition to psychosis, PANSS negative score and baseline MADRS reached trend-levels. In the final multivariate Cox regression analysis change in EPA levels remained the only significant predictor. Taking into account all other significant predictors, changes in EPA levels were found to be the single most significant predictor for transition to psychosis in a longer term observation of UHR individuals.

A new method for analysing transition to psychosis: Joint modelling of time-to-event outcome with time-dependent predictors

An active area in psychosis research is the identification of predictors of transition to a psychotic state among those who are assessed as being at high risk of psychosis. Many of the potential predictors are time dependent in the sense that they may change over time and are measured at a number of assessment time points. Examples are various psychopathological measures such as negative symptoms, positive symptoms, depression, and anxiety. Most research in transition to psychosis has not made use of the dynamic nature of these measures, probably because suitable statistical methods and software have not been easily available. However, a relatively new statistical methodology is well suited to include such time-dependent predictors in transition to psychosis analysis. This methodology is called joint modelling and has recently been incorporated in mainstream statistical software. This paper describes this methodology and demonstrates its usefulness using data from one of the pioneering studies on transition to psychosis.

Decomposing P300 into correlates of genetic risk and current symptoms in schizophrenia: An inter-trial variability analysis

BACKGROUND

The P300 event-related potential (ERP) component, which reflects cognitive processing, is a candidate biomarker for schizophrenia. However, the role of P300 in the pathophysiology of schizophrenia remains unclear because averaged P300 amplitudes reflect both genetic predisposition and current clinical status. Thus, we sought to identify which aspects of P300 are associated with genetic risk versus symptomatic status via an inter-trial variability analysis.

METHODS

Auditory P300, clinical symptoms, and neurocognitive function assessments were obtained from forty-five patients with schizophrenia, thirty-two subjects at genetic high risk (GHR), thirty-two subjects at clinical high risk (CHR), and fifty-two healthy control (HC) participants. Both conventional averaging and inter-trial variability analyses were conducted for P300, and results were compared across groups using analysis of variance (ANOVA). Pearson's correlation was utilized to determine associations among inter-trial variability for P300, current symptoms and neurocognitive status.

RESULTS

Average P300 amplitude was reduced in the GHR, CHR, and schizophrenia groups compared with that in the HC group. P300 inter-trial variability was elevated in the CHR and schizophrenia groups but relatively normal in the GHR and HC groups. Furthermore, P300 inter-trial variability was significantly related to negative symptom severity and neurocognitive performance results in schizophrenia patients.

CONCLUSIONS

These results suggest that P300 amplitude is an endophenotype for schizophrenia and that greater inter-trial variability of P300 is associated with more severe negative and cognitive symptoms in schizophrenia patients.

Effects of early trauma on psychosis development in clinical high-risk individuals and stability of trauma assessment across studies: a review

Early trauma (ET), though broadly and inconsistently defined, has been repeatedly linked to numerous psychological disturbances, including various developmental stages of psychotic disorders. The prodromal phase of psychosis highlights a unique and relevant population that provides insight into the critical periods of psychosis development. As such, a relatively recent research focus on individuals at clinical high risk (CHR) for psychosis reveals robust associations of early life trauma exposures with prodromal symptoms and function in these cohorts. While prevalence rates of ET in CHR cohorts remain consistently high, methodological measures of traumatic experiences vary across studies, presenting potential problems for reliability and validity of results. This review aims to 1) highlight the existing evidence identifying associations of ET, of multiple forms, with both symptom severity and transition rates to psychosis in CHR individuals, 2) present data on the variability among trauma assessments and its implications for conclusions about its relationship with clinical variables, 3) describe cognitive deficits common in CHR cohorts, including perceptual and neurocognitive impairments, and their neural correlates, that may modify the relationship of ET to symptoms, and 4) propose future directions for standardization of trauma assessment in CHR cohorts to better understand its clinical and cognitive correlates.

Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis

Recent studies have reported an association between psychopathology and subsequent clinical and functional outcomes in people at ultra-high risk (UHR) for psychosis. This has led to the suggestion that psychopathological information could be used to make prognostic predictions in this population. However, because the current literature is based on inferences at group level, the translational value of the findings for everyday clinical practice is unclear. Here we examined whether psychopathological information could be used to make individualized predictions about clinical and functional outcomes in people at UHR. Participants included 416 people at UHR followed prospectively at the Personal Assessment and Crisis Evaluation (PACE) Clinic in Melbourne, Australia. The data were analysed using Support Vector Machine (SVM), a supervised machine learning technique that allows inferences at the individual level. SVM predicted transition to psychosis with a specificity of 60.6%, a sensitivity of 68.6% and an accuracy of 64.6% (p<0.001). In addition, SVM predicted functioning with a specificity of 62.5%, a sensitivity of 62.5% and an accuracy of 62.5% (p=0.008). Prediction of transition was driven by disorder of thought content, attenuated positive symptoms and functioning, whereas functioning was best predicted by attention disturbances, anhedonia-asociality and disorder of thought content. These results indicate that psychopathological information allows individualized prognostic predictions with statistically significant accuracy. However, this level of accuracy may not be sufficient for clinical translation in real-world clinical practice. Accuracy might be improved by combining psychopathological information with other types of data using a multivariate machine learning framework.

Prediction of transition to psychosis in patients with a clinical high risk for psychosis: a systematic review of methodology and reporting

BACKGROUND

To enhance indicated prevention in patients with a clinical high risk (CHR) for psychosis, recent research efforts have been increasingly directed towards estimating the risk of developing psychosis on an individual level using multivariable clinical prediction models. The aim of this study was to systematically review the methodological quality and reporting of studies developing or validating such models.

METHOD

A systematic literature search was carried out (up to 14 March 2016) to find all studies that developed or validated a clinical prediction model predicting the transition to psychosis in CHR patients. Data were extracted using a comprehensive item list which was based on current methodological recommendations.

RESULTS

A total of 91 studies met the inclusion criteria. None of the retrieved studies performed a true external validation of an existing model. Only three studies (3.5%) had an event per variable ratio of at least 10, which is the recommended minimum to avoid overfitting. Internal validation was performed in only 14 studies (15%) and seven of these used biased internal validation strategies. Other frequently observed modeling approaches not recommended by methodologists included univariable screening of candidate predictors, stepwise variable selection, categorization of continuous variables, and poor handling and reporting of missing data.

CONCLUSIONS

Our systematic review revealed that poor methods and reporting are widespread in prediction of psychosis research. Since most studies relied on small sample sizes, did not perform internal or external cross-validation, and used poor model development strategies, most published models are probably overfitted and their reported predictive accuracy is likely to be overoptimistic.

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.