Mitja Bodatsch

Prediction of psychosis by mismatch negativity.

BACKGROUND To develop risk-adapted prevention of psychosis, an accurate estimation of the individual risk of psychosis at a given time is needed. Inclusion of biological parameters into multilevel prediction models is thought to improve predictive accuracy of models on the basis of clinical variables. To this aim, mismatch negativity (MMN) was investigated in a sample clinically at high risk, comparing individuals with and without subsequent conversion to psychosis. METHODS At baseline, an auditory oddball paradigm was used in 62 subjects meeting criteria of a late risk at-state who remained antipsychotic-naive throughout the study. Median follow-up period was 32 months (minimum of 24 months in nonconverters, n = 37). Repeated-measures analysis of covariance was employed to analyze the MMN recorded at frontocentral electrodes; additional comparisons with healthy controls (HC, n = 67) and first-episode schizophrenia patients (FES, n = 33) were performed. Predictive value was evaluated by a Cox regression model. RESULTS Compared with nonconverters, duration MMN in converters (n = 25) showed significantly reduced amplitudes across the six frontocentral electrodes; the same applied in comparison with HC, but not FES, whereas the duration MMN in in nonconverters was comparable to HC and larger than in FES. A prognostic score was calculated based on a Cox regression model and stratified into two risk classes, which showed significantly different survival curves. CONCLUSIONS Our findings demonstrate the duration MMN is significantly reduced in at-risk subjects converting to first-episode psychosis compared with nonconverters and may contribute not only to the prediction of conversion but also to a more individualized risk estimation and thus risk-adapted prevention.

Forecasting Psychosis by Event-Related Potentials—Systematic Review and Specific Meta-Analysis

BACKGROUND Prediction and prevention of psychosis have become major research topics. Clinical approaches warrant objective biological parameters to enhance validity in prediction of psychosis onset. In this regard, event-related potentials (ERPs) have been identified as promising tools for improving psychosis prediction. METHODS Herein, the focus is on sensory gating, mismatch negativity (MMN) and P300, thereby discussing which parameters allow for a timely and valid detection of future converters to psychosis. In a first step, we systematically reviewed the studies that resulted from a search of the MEDLINE database. In a second step, we performed a meta-analysis of those investigations reporting transitions that statistically compared ERPs in converting versus nonconverting subjects. RESULTS Sensory gating, MMN, and P300 have been demonstrated to be impaired in subjects clinically at risk of developing a psychotic disorder. In the meta-analysis, duration MMN achieved the highest effect size measures. CONCLUSIONS In summary, MMN studies have produced the most convincing results until now, including independent replication of the predictive validity. However, a synopsis of the literature revealed a relative paucity of ERP studies addressing the psychosis risk state. Considering the high clinical relevance of valid psychosis prediction, future research should question for the most informative paradigms and should allow for meta-analytic evaluation with regard to specificity and sensitivity of the most appropriate parameters.

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

BACKGROUND Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis. METHODS This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis. RESULTS Cox regression yielded a model including frontal theta (HR=1.82; [95% CI 1.00-3.32]) and delta (HR=2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR=.52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81). CONCLUSIONS Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.

Chances and risks of predicting psychosis

Prevention is currently regarded a promising strategy for fighting the unfavorable consequences of psychosis. Yet, for the error probability inherent in any predictive approach, benefits and costs must be carefully weighed against each other. False attribution of risk may unnecessarily provoke stress and anxiety, and lead to unwarranted intervention exposure. However, clinical risk samples already exhibit psychopathological symptoms, cognitive and functional impairments, and help-seeking for mental problems. Thus, the risk of futile interventions is low as long as preventive measures also provide treatment for current complaints. Differentiation between still normal and clinically relevant mental states is another challenge as psychotic-like phenomena occur frequently in the general population, especially in younger adolescents. Reported prevalence rates vary with age, and if severe in terms of frequency and persistence, these phenomena considerably increase risk of psychosis in clinical as well as general population samples. Stigmatization is another concern, though insufficiently studied. Yet, at least more severe states of risk, which are accompanied by changes in thinking, feeling, and behavior, might lead to unfavorable, (self-) stigmatizing effects already by themselves, independent of any diagnostic “label,” and to stress and confusion for the lack of understanding of what is going on. To further improve validity of risk criteria, advanced risk algorithms combining multi-step detection and risk stratification procedures should be developed. However, all prediction models possess a certain error probability. Thus, whether a risk model justifies preventive measures can only be decided by weighing the costs of unnecessary intervention and the benefits of avoiding a potentially devastating outcome.

Pharmacological Prevention and Treatment in Clinical At-Risk States for Psychosis

Over the last couple of decades, the treatment of psychoses has much advanced; yet, despite all progress, the individual and societal burden associated with psychosis and particularly schizophrenia has largely remained unchanged. Therefore, much hope is currently placed on indicated prevention as a mean to fight these burdens before they set in. Though the number of studies investigating pharmacological interventions is still limited, encouraging results have been reported from the pioneering trials, despite several methodological limitations. Furthermore, it has become clear that persons characterized by the at-risk criteria are already ill and do not only need preventive intervention, but also treatment. In consequence, outcome criteria have to be broadened to cover the current needs of the patients. As is indicated by a recent study successfully using Omega-3 fatty acids for both purposes, it may be promising to develop and investigate interventions especially for the at-risk state, independent of their effectiveness in manifest disease states. Treatment studies may become promoted by the proposed introduction of a new disorder category into DSM-V. Future prevention studies, however, need to solve the challenge of changing immediate transition rates, demanding for new risk enrichment strategies as a prerequisite for feasible trial designs.

Contributions of experimental psychiatry to research on the psychosis prodrome.

In the recent decades, a paradigmatic change in psychosis research and treatment shifted attention toward the early and particularly the prodromal stages of illness. Despite substantial progress with regard to the neuronal underpinnings of psychosis development, the crucial biological mechanisms leading to manifest illness are yet insufficiently understood. Until today, one significant approach to elucidate the neurobiology of psychosis has been the modeling of psychotic symptoms by psychedelic substances in healthy individuals. These models bear the opportunity to evoke particular neuronal aberrations and the respective psychotic symptoms in a controlled experimental setting. In the present paper, we hypothesize that experimental psychiatry bears unique opportunities in elucidating the biological mechanisms of the prodromal stages of psychosis. Psychosis risk symptoms are attenuated, transient, and often only retrospectively reported. The respective neuronal aberrations are thought being dynamic. The correlation of unstable psychopathology with observed neurofunctional disturbances is thus yet largely unclear. In modeling psychosis, the experimental setting allows not only for evoking particular symptoms, but for the concomitant assessment of psychopathology, neurophysiology, and neuropsychology. Herein, the glutamatergic model will be highlighted exemplarily, with special emphasis on its potential contribution to the elucidation of psychosis development. This model of psychosis appears as candidate for modeling the prodrome by inducing psychotic-like symptoms in healthy individuals. Furthermore, it alters pre-attentive processing like the Mismatch Negativity, an electrophysiological component which has recently been identified as a potential predictive marker of psychosis development. In summary, experimental psychiatry bears the potential to further elucidate the biological mechanisms of the psychosis prodrome. A better understanding of the respective pathophysiology might assist in the identification of predictive markers, and the development of preventive treatments.

Basic Disturbances of Information Processing in Psychosis Prediction

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

Deep Brain Stimulation in Schizophrenia

Deep brain stimulation (DBS) has successfully advanced treatment options of putative therapy-resistant neuropsychiatric diseases. Building on this strong foundation more and more mental disorders in the stadium of therapy-resistance are considered as possible indications for DBS. Especially schizophrenia with its associated severe and difficult to treat symptoms is gaining attention. This attention demands critical questions regarding the assumed mechanisms of DBS and its possible influence on the supposed pathophysiology of schizophrenia. Here we synoptically compare current approaches and theories of DBS and discuss the feasibility of DBS in schizophrenia as well as the transferability from other psychiatric disorders successfully treated with DBS. For this we consider recent advances in animal models of schizophrenic symptoms, results regarding the influence of DBS on dopaminergic transmission as well as data concerning neural oscillation and synchronization. In conclusion the use of DBS for some symptoms of schizophrenia seems to be a promising approach, but the lack of a comprehensive theory of the mechanisms of DBS as well as its impact on schizophrenia might void the use of DBS in schizophrenia at this point.