Neurophysiology for Detection of High Risk for Psychosis

Intact Mismatch Negativity Responses in Clinical High Risk for Psychosis and First-Episode Psychosis: Evidence From Source-Reconstructed Event-Related Fields and Time-Frequency Data

BACKGROUND

This study examined whether mismatch negativity (MMN) responses are impaired in participants at clinical high risk for psychosis (CHR-P) and patients with first-episode psychosis (FEP) and whether MMN deficits predict clinical outcomes in CHR-Ps.

METHODS

Magnetoencephalography data were collected during a duration-deviant MMN paradigm for a group of 116 CHR-P participants, 33 FEP patients (15 antipsychotic-naïve), clinical high risk negative group (n = 38) with substance abuse and affective disorder, and 49 healthy control participants. Analysis of group differences of source-reconstructed event-related fields as well as time-frequency and intertrial phase coherence focused on the bilateral Heschl's gyri and bilateral superior temporal gyri.

RESULTS

Significant magnetic MMN responses were found across participants in the bilateral Heschl's gyri and bilateral superior temporal gyri. However, MMN amplitude as well as time-frequency and intertrial phase coherence responses were intact in CHR-P participants and FEP patients compared with healthy control participants. Furthermore, MMN deficits were not related to persistent attenuated psychotic symptoms or transitions to psychosis in CHR-P participants.

CONCLUSIONS

Our data suggest that magnetic MMN responses in magnetoencephalography data are not impaired in early-stage psychosis and may not predict clinical outcomes in CHR-P participants.

Mismatch Negativity in Response to Auditory Deviance and Risk for Future Psychosis in Youth at Clinical High Risk for Psychosis

Importance

Although clinical criteria for identifying youth at risk for psychosis have been validated, they are not sufficiently accurate for predicting outcomes to inform major treatment decisions. The identification of biomarkers may improve outcome prediction among individuals at clinical high risk for psychosis (CHR-P).

Objective

To examine whether mismatch negativity (MMN) event-related potential amplitude, which is deficient in schizophrenia, is reduced in young people with the CHR-P syndrome and associated with outcomes, accounting for effects of antipsychotic medication use.

Design, Setting, and Participants

MMN data were collected as part of the multisite case-control North American Prodrome Longitudinal Study (NAPLS-2) from 8 university-based outpatient research programs. Baseline MMN data were collected from June 2009 through April 2013. Clinical outcomes were assessed throughout 24 months. Participants were individuals with the CHR-P syndrome and healthy controls with MMN data. Participants with the CHR-P syndrome who developed psychosis (ie, converters) were compared with those who did not develop psychosis (ie, nonconverters) who were followed up for 24 months. Analysis took place between December 2019 and December 2021.

Main Outcomes and Measures

Electroencephalography was recorded during a passive auditory oddball paradigm. MMN elicited by duration-, pitch-, and duration + pitch double-deviant tones was measured.

Results

The CHR-P group (n = 580; mean [SD] age, 19.24 [4.39] years) included 247 female individuals (42.6%) and the healthy control group (n = 241; mean age, 20.33 [4.74] years) included 114 female individuals (47.3%). In the CHR-P group, 450 (77.6%) were not taking antipsychotic medication at baseline. Baseline MMN amplitudes, irrespective of deviant type, were deficient in future CHR-P converters to psychosis (n = 77, unmedicated n = 54) compared with nonconverters (n = 238, unmedicated n = 190) in both the full sample (d = 0.27) and the unmedicated subsample (d = 0.33). In the full sample, baseline medication status interacted with group and deviant type indicating that double-deviant MMN, compared with single deviants, was reduced in unmedicated converters compared with nonconverters (d = 0.43). Further, within the unmedicated subsample, deficits in double-deviant MMN were most strongly associated with earlier conversion to psychosis (hazard ratio, 1.40 [95% CI, 1.03-1.90]; P = .03], which persisted over and above positive symptom severity.

Conclusions and Relevance

This study found that MMN amplitude deficits were sensitive to future psychosis conversion among individuals at risk of CHR-P, particularly those not taking antipsychotic medication at baseline, although associations were modest. While MMN shows limited promise as a biomarker of psychosis onset on its own, it may contribute novel risk information to multivariate prediction algorithms and serve as a translational neurophysiological target for novel treatment development in a subgroup of at-risk individuals.

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

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

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.

Development of Neuroimaging-Based Biomarkers in Psychiatry

This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.

Psychosis risk screening in different populations using the Prodromal Questionnaire: A systematic review

AIM

Diagnosing individuals at ultra high risk (UHR) for psychosis can improve early access to treatment, and a two-stage model utilizing self-report screening followed by a clinical interview can be accurate and efficient. However, it is currently unclear which screening cut-offs to adopt with different populations.

METHODS

A systematic review of diagnostic accuracy studies evaluating the Prodromal Questionnaire (PQ) as a preliminary screener for UHR and psychosis was conducted to examine screening effectiveness in different contexts. MedLine, PsycInfo, SCOPUS and ProQuest Dissertations and Abstracts databases were electronically searched, along with a review screen and citation search of key papers. Findings were summarized in a narrative synthesis.

RESULTS

In total, 14 diagnostic accuracy studies and 45 studies using the PQ as a screening tool for UHR and psychosis were included. In all settings, the 3 different versions of the PQ were all found to accurately identify UHR and full psychosis. Higher cut-off points were required in non-help-seeking samples, relative to general help-seeking populations, which in turn were higher than those needed in samples highly enriched with UHR participants.

CONCLUSION

The findings support the use of the PQ as a preliminary screening tool for UHR in different settings; however, higher thresholds in lower UHR-prevalence populations are necessary to minimize false positives. Including the distress criteria, rather than just number of symptoms, may improve screening effectiveness. Different thresholds may be appropriate in different contexts depending on the importance of sensitivity vs specificity. Protocol registration: CRD42016033004.