Influence of cannabis use on incidence of psychosis in people at clinical high risk.

AIMS: Evidence for case-control studies suggests that cannabis use is a risk factor for the development of psychosis. However, there have been limited prospective studies and the direction of this association remains controversial. The primary aim of the present study was to examine the association between cannabis use and the incidence of psychotic disorders in people at clinical high risk of psychosis. Secondary aims were to assess associations between cannabis use and the persistence of psychotic symptoms, and with functional outcome. METHODS: Current and previous cannabis use were assessed in individuals at clinical high risk of psychosis (n = 334) and healthy controls (n = 67), using a modified version of the Cannabis Experience Questionnaire. Participants were assessed at baseline and followed up for 2 years. Transition to psychosis and persistence of psychotic symptoms were assessed using the Comprehensive Assessment of At-Risk Mental States criteria. Level of functioning at follow up was assessed using the Global Assessment of Functioning disability scale. RESULTS: During follow up, 16.2% of the clinical high-risk sample developed psychosis. Of those who did not become psychotic, 51.4% had persistent symptoms and 48.6% were in remission. There was no significant association between any measure of cannabis use at baseline and either transition to psychosis, the persistence of symptoms, or functional outcome. CONCLUSIONS: These findings contrast with epidemiological data that suggest that cannabis use increases the risk of psychotic disorder.

Predictors of study drop-out and service disengagement in patients at clinical high risk for psychosis.

PURPOSE: Study drop-out during follow-up and service disengagement frequently occur in patients at clinical high risk for psychosis (CHR-P). However, little is known about their predictors. Therefore, we aimed to analyze the rate and reasons for drop-out and service disengagement in CHR-P patients and investigate their sociodemographic and clinical predictors. METHODS: Data from 200 patients of the prospective Früherkennung von Psychosen (FePsy) study were analyzed with competing risks survival models, considering drop-out and transition to psychosis as competing events. To investigate whether symptoms changed immediately before drop-out, t tests were applied. RESULTS: Thirty-six percent of patients dropped out within 5 years. Almost all drop-outs also disengaged from our service. Hence, study drop-out was used as a proxy for service disengagement. Patients with more severe baseline disorganized symptoms and a late inclusion into the study were significantly more likely to disengage. Immediately before disengagement, there was significant improvement in negative symptoms only. CONCLUSION: A considerable proportion of CHR-P patients disengaged from our clinical study and service. Patients who were included during a later study period with more assessments disengaged more often, which might have been due to more frequent invitations to follow-up assessments and thereby increasing participation burden. Hence, our study provides a cautionary note on high-frequency follow-up assessments. Larger-scale studies evaluating predictors on multiple domains would help to further elucidate drop-out and disengagement.

No associations between medial temporal lobe volumes and verbal learning/memory in emerging psychosis.

Grey matter (GM) volume alterations have been repeatedly demonstrated in patients with first episode psychosis (FEP). Some of these neuroanatomical abnormalities are already evident in the at-risk mental state (ARMS) for psychosis. Not only GM alterations but also neurocognitive impairments predate the onset of frank psychosis with verbal learning and memory (VLM) being among the most impaired domains. Yet, their interconnection with alterations in GM volumes remains ambiguous. Thus, we evaluated associations of different subcortical GM volumes in the medial temporal lobe with VLM performance in antipsychotic-naïve ARMS and FEP patients. Data from 59 ARMS and 31 FEP patients, collected within the prospective Früherkennung von Psychosen study, were analysed. Structural T1-weighted images were acquired using a 3 Tesla magnetic resonance imaging scanner. VLM was assessed using the California Verbal Learning Test and its factors Attention Span, Learning Efficiency, Delayed Memory and Inaccurate Memory. FEP patients showed significantly enlarged volumes of hippocampus, pallidum, putamen and thalamus compared to ARMS patients. A significant negative association between amygdala and pallidum volume and Attention Span was found in ARMS and FEP patients combined, which however did not withstand correction for multiple testing. Although we found significant between-group differences in subcortical volumes and VLM is among the most impaired cognitive domains in emerging psychosis, we could not demonstrate an association between low performance and subcortical GM volumes alterations in antipsychotic-naïve patients. Hence, deficits in this domain do not appear to stem from alterations in subcortical structures.

Pituitary gland volume in at-risk mental state for psychosis: a longitudinal MRI analysis.

INTRODUCTION: Pituitary enlargement has been reported in individuals with schizophrenic psychosis or an at-risk mental state for psychosis (ARMS). In a previous study, our group could show pituitary volume increase in first episode and ARMS patients with later transition to psychosis (ARMS-T). However, there are no longitudinal studies on this issue so far. We therefore examined longitudinally whether transition to psychosis would be accompanied by a further increase of pituitary volume in antipsychotic-naïve ARMS patients. METHODS: Magnetic resonance imaging (MRI) data were acquired from 23 antipsychotic-naïve individuals with an ARMS. Ten subjects developed psychosis (ARMS-T) and 13 did not (ARMS-NT). ARMS-T were re-scanned after the onset of psychosis, and ARMS-NT were re-scanned at the end of the study period. RESULTS: There was no significant difference of the pituitary volume between ARMS-T and ARMS-NT in our sample, and there were no significant pituitary volume changes over time. Discussion Longitudinally, we could not detect any further volumetric changes in the pituitary volume with transition to psychosis. CONCLUSIONS: This, together with the result of our previous study, could indicate that the perceived level of stress in ARMS patients is constantly high from very early onward.

Subjective long-term emotional and social effects of recreational MDMA use: the role of setting and intentions.

MDMA is a recreational drug commonly used to enhance euphoria, but it is also used in non-party settings with self-insight or social connection intentions. Yet, little is known about whether distinct consumer groups are formed based on consumption setting and intention. We aimed to characterize different types of recreational MDMA users based on consumption setting and intentions, and to examine their differences in perceptions of long-term social-emotional effects of MDMA use. We analyzed self-reports of 766 individuals (ages 18-61, mostly from Western countries), reporting on their MDMA consumption habits and perceived effects. We used a K-medoids clustering algorithm to identify distinct types of consumption settings and intentions. We identified three setting types - party settings with friends (N = 388), private home settings (N = 132), mixed settings (N = 246) - and three intention types - euphoria and energy (N = 302), self-insight (N = 219), mixed intentions (N = 245). Members of the self-insight and mixed intentions clusters reported considerably more long-term socio-emotional benefits than members of the euphoria and energy cluster. No differences were observed between the setting clusters. In this particular sample, more long-term benefits than harms were reported. Our findings suggest that the long-term social-emotional benefits of MDMA are associated with whether users seek self-insight or have mixed intentions.

Help-seeking and pathways to care in the early stages of psychosis.

PURPOSE: Delay in the treatment of a first psychotic episode can have a negative influence on the future course of the disease. In this context, it is important to examine pathways to care to understand factors contributing to delay in access to adequate care. METHODS: Using the Basel Interview for Psychosis, we examined the help-seeking behaviour of 61 individuals with an at-risk mental state for psychosis and 37 patients with a first episode of psychosis in a low threshold health care system as part of the Basel early detection of psychosis study. RESULTS: The median duration of untreated illness was 3.4 years, of untreated psychosis 12 months. Eighty-six percent of all individuals sought help of some kind before reaching our specialised early detection outpatient clinic, with a mean number of help-seeking contacts of 1.5 prior to referral. The most frequent first help-seeking contacts were family members or relatives n = 24 (26.7 %), close friends n = 17 (17.9 %), psychiatrists in private practice n = 13 (14.4 %) or general practitioners n = 11 (12.2 %). Most patients consulted other health professionals in the early course of the illness before reaching our specialised service; help-seeking with non-medical institutions was rare. Women had more help-seeking contacts than men before contact with our early detection clinic. CONCLUSIONS: Family, close friends and medical professionals play an important role in help-seeking leading to specialised psychiatric care. Men seek help less often; specific strategies for encouraging young, at-risk men to seek help should be developed.

Latent state-trait structure of BPRS subscales in clinical high-risk state and first episode psychosis.

To investigate the longitudinal latent state-trait structure of the different dimensions of psychosis symptoms in clinical high-risk state (CHRS) and first episode psychosis (FEP) individuals over a one year time-span. This paper examines if the symptom clusters Positive Symptoms, Negative Symptoms, Affectivity, Resistance, Activation, and Excitement according to the Brief Psychiatric Rating Scale (BPRS) differ in their trait and state characters in 196 CHRS and 131 FEP individuals. Statistical analysis was performed using latent state-trait analysis. On average, trait differences accounted for 72.2% of Positive Symptoms, 81.1% of Negative Symptoms, 57.0% of Affectivity, and 69.2% of Activation, whereas 15.0% of the variance of Resistance and 13.2% of the variance of Excitement were explained by trait differences. Explorative analyses showed a trait components' increase of 0.408 in Positive Symptoms from baseline up to the 9th month and an increase of 0.521 in Affectivity from baseline up to the 6th month. Negative Symptoms had the highest trait component levels of all subscales between baseline and 6 months. The finding that an increasing proportion of psychosis symptoms is persisting over time underlines the importance of early intervention programs in individuals with psychotic disorders.

Prediction of MDMA response in healthy humans: a pooled analysis of placebo-controlled studies.

BACKGROUND: 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") is used both recreationally and therapeutically. Little is known about the factors influencing inter- and intra-individual differences in the acute response to MDMA. Effects of other psychoactive substances have been shown to be critically influenced by personality traits and mood state before intake. METHODS: We pooled data from 10 randomized, double-blind, placebo-controlled, cross-over studies performed in the same laboratory in 194 healthy subjects receiving doses of 75 or 125mg of MDMA. We investigated the influence of drug dose, body weight, sex, age, drug pre-experience, genetics, personality and mental state before drug intake on the acute physiological and psychological response to MDMA. RESULTS: In univariable analyses, the MDMA plasma concentration was the strongest predictor for most outcome variables. When adjusting for dose per body weight, we found that (a) a higher activity of the enzyme CYP2D6 predicted lower MDMA plasma concentration, (b) a higher score in the personality trait "openness to experience" predicted more perceived "closeness", a stronger decrease in "general inactivation", and higher scores in the 5D-ASC (5 Dimensions of Altered States of Consciousness Questionnaire) scales "oceanic boundlessness" and "visionary restructuralization", and (c) subjects with high "neuroticism" or trait anxiety were more likely to have unpleasant and/or anxious reactions. CONCLUSIONS: Although MDMA plasma concentration was the strongest predictor, several personality traits and mood state variables additionally explained variance in the response to MDMA. The results confirm that both pharmacological and non-pharmacological variables influence the response to MDMA. These findings may be relevant for the therapeutic use of MDMA.

Relation between self-perceived stress, psychopathological symptoms and the stress hormone prolactin in emerging psychosis.

BACKGROUND: Psychosocial stress and the stress hormone prolactin are assumed to play an important role in the pathogenesis of schizophrenia and related psychoses, and have been frequently observed to be increased in antipsychotic-naïve patients with a clinical high risk for psychosis (CHR-P) or first episode of psychosis (FEP). The aim of this study was to further elucidate the relationships between self-perceived stress, psychopathological symptoms and prolactin levels in these patients. METHODS: In this cross-sectional study, 45 healthy controls, 31 CHR-P patients and 87 FEP patients were recruited from two different study centers. Prolactin was measured under standardized conditions between 8 and 10 am. All patients were antipsychotic-naïve and not taking any prolactin influencing medication. Self-perceived stress during the last month was measured with the perceived stress scale (PSS-10) immediately before blood taking. RESULTS: Both CHR-P and FEP patients showed significantly higher levels of self-perceived stress and prolactin than controls. Hyperprolactinemia (i.e. prolactin levels above the reference range) was observed in 26% of CHR-P and 45% of FEP patients. Self-perceived stress was significantly positively associated with affective symptoms, but not with other symptoms. There was no significant association between self-perceived stress and prolactin levels. CONCLUSION: Our results confirm that CHR-P and FEP patients have higher stress levels than healthy controls and frequently have hyperprolactinemia, independent of antipsychotic medication. However, although it is well established that prolactin increases in response to stress, our results do not support the notion that increased prolactin levels in these patients are due to stress.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.

BACKGROUND: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. METHODS: PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. FINDINGS: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (ß = .29, P = .03) and diagnostic compared to prognostic (ß = .84, p < .0001) and predictive (ß = .87, P = .002) models were associated with increased accuracy. INTERPRETATION: To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

Multimodal prognosis of negative symptom severity in individuals at increased risk of developing psychosis.

Negative symptoms occur frequently in individuals at clinical high risk (CHR) for psychosis and contribute to functional impairments. The aim of this study was to predict negative symptom severity in CHR after 9 months. Predictive models either included baseline negative symptoms measured with the Structured Interview for Psychosis-Risk Syndromes (SIPS-N), whole-brain gyrification, or both to forecast negative symptoms of at least moderate severity in 94 CHR. We also conducted sequential risk stratification to stratify CHR into different risk groups based on the SIPS-N and gyrification model. Additionally, we assessed the models' ability to predict functional outcomes in CHR and their transdiagnostic generalizability to predict negative symptoms in 96 patients with recent-onset psychosis (ROP) and 97 patients with recent-onset depression (ROD). Baseline SIPS-N and gyrification predicted moderate/severe negative symptoms with significant balanced accuracies of 68 and 62%, while the combined model achieved 73% accuracy. Sequential risk stratification stratified CHR into a high (83%), medium (40-64%), and low (19%) risk group regarding their risk of having moderate/severe negative symptoms at 9 months follow-up. The baseline SIPS-N model was also able to predict social (61%), but not role functioning (59%) at above-chance accuracies, whereas the gyrification model achieved significant accuracies in predicting both social (76%) and role (74%) functioning in CHR. Finally, only the baseline SIPS-N model showed transdiagnostic generalization to ROP (63%). This study delivers a multimodal prognostic model to identify those CHR with a clinically relevant negative symptom severity and functional impairments, potentially requiring further therapeutic consideration.

Development and Validation of a Dynamic Risk Prediction Model to Forecast Psychosis Onset in Patients at Clinical High Risk.

The prediction of outcomes in patients at Clinical High Risk for Psychosis (CHR-P) almost exclusively relies on static data obtained at a single snapshot in time (ie, baseline data). Although the CHR-P symptoms are intrinsically evolving over time, available prediction models cannot be dynamically updated to reflect these changes. Hence, the aim of this study was to develop and internally validate a dynamic risk prediction model (joint model) and to implement this model in a user-friendly online risk calculator. Furthermore, we aimed to explore the prognostic performance of extended dynamic risk prediction models and to compare static with dynamic prediction. One hundred ninety-six CHR-P patients were recruited as part of the "Basel Früherkennung von Psychosen" (FePsy) study. Psychopathology and transition to psychosis was assessed at regular intervals for up to 5 years using the Brief Psychiatric Rating Scale-Expanded (BPRS-E). Various specifications of joint models were compared with regard to their cross-validated prognostic performance. We developed and internally validated a joint model that predicts psychosis onset from BPRS-E disorganization and years of education at baseline and BPRS-E positive symptoms during the follow-up with good prognostic performance. The model was implemented as online risk calculator (http://www.fepsy.ch/DPRP/). The use of extended joint models slightly increased the prognostic accuracy compared to basic joint models, and dynamic models showed a higher prognostic accuracy than static models. Our results confirm that extended joint modeling could improve the prediction of psychosis in CHR-P patients. We implemented the first online risk calculator that can dynamically update psychosis risk prediction.

Moderators of treatment efficacy in individualized metacognitive training for psychosis (MCT+).

BACKGROUND AND OBJECTIVES: Individualized Metacognitive Training (MCT+) is a manualized intervention designed to improve delusional severity by reducing delusion-associated cognitive biases such as jumping-to-conclusions. Increased interest in personalized medicine stipulates the identification of patients who are more likely to benefit from specialized interventions. The present study aimed to explore baseline moderators of MCT+ efficacy on delusions and overall positive symptoms in psychosis. METHODS: We analyzed data from a randomized rater-blind controlled trial, in which 92 patients with psychotic disorders and current or past delusions were randomly assigned to either MCT+ or CogPack®, a cognitive remediation software. Baseline moderator variables consisted of jumping-to-conclusions, cognitive insight, quality of life, self-esteem, selective attention, and patients' attitudes towards their symptoms. Linear mixed-effects models were applied to investigate specific moderators of MCT+ efficacy. RESULTS: In MCT+ relative to CogPack, presence of a jumping-to-conclusions bias, a lowered decision threshold, and low self-esteem were associated with larger improvements in delusional severity and/or overall positive symptoms over time. Subjective reasoning style and insight, as well as subjective attitudes towards psychosis, did not moderate the treatment efficacy of MCT+ relative to CogPack. LIMITATIONS: Participation of both treatment groups in group MCT as a part of standard care, possibly leading to additional effects on delusional severity. CONCLUSIONS: Patients with low self-esteem and those who are prone to jumping-to-conclusions seem to particularly benefit from MCT+. Our results can help inform clinical practice as they provide specific criteria for selecting patients for whom MCT+ is most appropriate.

EEG Microstate Differences in Medicated vs. Medication-Naïve First-Episode Psychosis Patients.

There has been considerable interest in the role of synchronous brain activity abnormalities in the pathophysiology of psychotic disorders and their relevance for treatment; one index of such activity are EEG resting-state microstates. These reflect electric field configurations of the brain that persist over 60-120 ms time periods. A set of quasi-stable microstates classes A, B, C, and D have been repeatedly identified across healthy participants. Changes in microstate parameters coverage, duration and occurrence have been found in medication-naïve as well as medicated patients with psychotic disorders compared to healthy controls. However, to date, only two studies have directly compared antipsychotic medication effects on EEG microstates either pre- vs. post-treatment or between medicated and unmedicated chronic schizophrenia patients. The aim of this study was therefore to directly compare EEG resting-state microstates between medicated and medication-naïve (untreated) first-episode (FEP) psychosis patients (mFEP vs. uFEP). We used 19-channel clinical EEG recordings to compare temporal parameters of four prototypical microstate classes (A-D) within an overall sample of 47 patients (mFEP n = 17; uFEP n = 30). The results demonstrated significant decreases of microstate class A and significant increases of microstate class B in mFEP compared to uFEP. No significant differences between groups were found for microstate classes C and D. Further studies are needed to replicate these results in longitudinal designs that assess antipsychotic medication effects on neural networks at the onset of the disorder and over time during illness progression. As treatment response and compliance in FEP patients are relatively low, such studies could contribute to better understand treatment outcomes and ultimately improve treatment strategies.

Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning.

BACKGROUND: The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approaches have the potential to improve the prediction of suicide attempt (SA) risk. Using the epidemiological multiwave prospective-longitudinal Early Developmental Stages of Psychopathology (EDSP) data set, we compared four algorithms-logistic regression, lasso, ridge, and random forest-in predicting a future SA in a community sample of adolescents and young adults. METHODS: The EDSP Study prospectively assessed, over the course of 10 years, adolescents and young adults aged 14-24 years at baseline. Of 3021 subjects, 2797 were eligible for prospective analyses because they participated in at least one of the three follow-up assessments. Sixteen baseline predictors, all selected a priori from the literature, were used to predict follow-up SAs. Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance we used the area under the curve (AUC). RESULTS: The mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.828, 0.826, 0.829, and 0.824, respectively. CONCLUSIONS: Based on our comparison, each algorithm performed equally well in distinguishing between a future SA case and a non-SA case in community adolescents and young adults. When choosing an algorithm, different considerations, however, such as ease of implementation, might in some instances lead to one algorithm being prioritized over another. Further research and replication studies are required in this regard.

Anatomical integrity within the inferior fronto-occipital fasciculus and semantic processing deficits in schizophrenia spectrum disorders.

The core symptoms of schizophrenia spectrum disorders (SSD) include abnormal semantic processing which may rely on the ventral language stream of the human brain. Thus, structural disruption of the ventral language stream may play an important role in semantic deficits observed in SSD patients. Therefore, we compared white matter tract integrity in SSD patients and healthy controls using diffusion tensor imaging combined with probabilistic fiber tractography. For the ventral language stream, we assessed the inferior fronto-occipital fasciculus [IFOF], inferior longitudinal fasciculus, and uncinate fasciculus. The arcuate fasciculus and corticospinal tract were used as control tracts. In SSD patients, the relationship between semantic processing impairments and tract integrity was analyzed separately. Three-dimensional tract reconstructions were performed in 45/44 SSD patients/controls ("Bern sample") and replicated in an independent sample of 24/24 SSD patients/controls ("Basel sample"). Multivariate analyses of fractional anisotropy, mean, axial, and radial diffusivity of the left IFOF showed significant differences between SSD patients and controls (p(FDR-corr) < 0.001, ηp2 = 0.23) in the Bern sample. Axial diffusivity (AD) of the left UF was inversely correlated with semantic impairments (r = -0.454, p(FDR-corr) = 0.035). In the Basel sample, significant group differences for the left IFOF were replicated (p < .01, ηp2 = 0.29), while the correlation between AD of the left IFOF and semantic processing decline (r = -0.376, p = .09) showed a statistical trend. No significant effects were found for the dorsal language stream. This is direct evidence for the importance of the integrity of the ventral language stream, in particular the left IFOF, in semantic processing deficits in SSD.

Sex differences in cognitive functioning of patients at-risk for psychosis and healthy controls: Results from the European Gene-Environment Interactions study.

BACKGROUND: Sex differences in cognitive functioning have long been recognized in schizophrenia patients and healthy controls (HC). However, few studies have focused on patients with an at-risk mental state (ARMS) for psychosis. Thus, the aim of the present study was to investigate sex differences in neurocognitive performance in ARMS patients compared with HC. METHODS: The data analyzed in this study were collected within the multicenter European Gene-Environment Interactions study (11 centers). A total of 343 ARMS patients (158 women) and 67 HC subjects (33 women) were included. All participants completed a comprehensive neurocognitive battery. Linear mixed effects models were used to explore whether sex differences in cognitive functioning were present in the total group (main effect of sex) and whether sex differences were different for HC and ARMS (interaction between sex and group). RESULTS: Women performed better in social cognition, speed of processing, and verbal learning than men regardless of whether they were ARMS or HC. However, only differences in speed of processing and verbal learning remained significant after correction for multiple testing. Additionally, ARMS patients displayed alterations in attention, current IQ, speed of processing, verbal learning, and working memory compared with HC. CONCLUSIONS: Findings indicate that sex differences in cognitive functioning in ARMS are similar to those seen between healthy men and women. Thus, it appears that sex differences in cognitive performance may not be specific for ARMS, a finding resembling that in patients with schizophrenic psychoses.

Impaired prepulse inhibition and prepulse-elicited reactivity but intact reflex circuit excitability in unmedicated schizophrenia patients: a comparison with healthy subjects and medicated schizophrenia patients.

Deficient sensorimotor gating as indexed by prepulse inhibition (PPI) of the startle response has been reported repeatedly in patients suffering from schizophrenia. According to the widely accepted "protective hypothesis," PPI reflects the protection of ongoing information processing against interference by other stimuli. Alternatively, it has been proposed that PPI might be regulated by startle reflex circuit excitability. In the present study, we evaluated these 2 conceptually divergent approaches underlying the regulation of PPI. To this end, we assessed sensorimotor gating as indexed by PPI, the reactivity to the prepulse-alone stimulus indexed as prepulse-elicited reactivity (PPER), and acoustic blink reflex excitability in terms of paired pulse suppression (PPS) within a single recording session in 13 unmedicated and 24 medicated (11 first break) schizophrenia patients in comparison to 43 healthy control subjects. The results showed that PPI was significantly reduced in unmedicated, but not in medicated schizophrenia patients. Furthermore, unmedicated patients could be distinguished from the medicated patients and control subjects in terms of PPER. In contrast to PPI, PPS did not differ between patients and control subjects. These findings are in line with the "protective hypothesis" of PPI and indicate that reduced sensorimotor gating in schizophrenia patients might be based on a reduced perception and/or processing of the prepulse stimulus. The extent to which PPER may or may not be causally associated with sensorimotor gating in schizophrenia has to be further investigated in human and animal studies.

Clinical and functional ultra-long-term outcome of patients with a clinical high risk (CHR) for psychosis.

BACKGROUND: Few studies have followed up patients with a clinical high risk (CHR) for psychosis for more than 2-3 years. We aimed to investigate the rates and baseline predictors for remission from CHR and transition to psychosis over a follow-up period of up to 16 years. Additionally, we examined the clinical and functional long-term outcome of CHR patients who did not transition. METHODS: We analyzed the long-term course of CHR patients that had been included in the longitudinal studies "Früherkennung von Psychosen" (FePsy) or "Bruderholz" (BHS). Those patients who had not transitioned to psychosis during the initial follow-up periods (2/5 years), were invited for additional follow-ups. RESULTS: Originally, 255 CHR patients had been included. Of these, 47 had transitioned to psychosis during the initial follow-ups. Thus, 208 were contacted for the long-term follow-up, of which 72 (34.6%) participated. From the original sample of 255, 26%, 31%, 35%, and 38% were estimated to have transitioned after 3, 5, 10, and 16 years, respectively, and 51% had remitted from their high risk status at the latest follow-up. Better psychosocial functioning at baseline was associated with a higher rate of remission. Of the 72 CHR patients re-assessed at long-term follow-up, 60 had not transitioned, but only 28% of those were fully recovered clinically and functionally. CONCLUSIONS: Our study shows the need for follow-ups and clinical attention longer than the usual 2-3 years as there are several CHR patients with later transitions and only a minority of CHR those without transition fully recovers.

Individualized prediction of psychosis in subjects with an at-risk mental state.

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis. The discriminative neuroanatomical pattern underlying this finding consisted of many brain areas across all four lobes and the cerebellum. These results provide proof-of-concept that the early diagnosis of psychosis is feasible using neuroanatomical-based pattern recognition.