According to current evidence and guidelines, continued antipsychotic treatment is key for preventing relapse in people with schizophrenia‐spectrum disorders, but evidence‐based recommendations for the choice of the individual antipsychotic for maintenance treatment are lacking. Although oral antipsychotics are often prescribed first line for practical reasons, long‐acting injectable antipsychotics (LAIs) are a valuable resource to tackle adherence issues since the earliest phase of disease. Medline, EMBASE, PsycINFO, CENTRAL and CINAHL databases and online registers were searched to identify randomized controlled trials comparing LAIs or oral antipsychotics head‐to‐head or against placebo, published until June 2021. Relative risks and standardized mean differences were pooled using random‐effects pairwise and network meta‐analysis. The primary outcomes were relapse and dropout due to adverse events. We used the Cochrane Risk of Bias tool to assess study quality, and the CINeMA approach to assess the confidence of pooled estimates. Of 100 eligible trials, 92 (N=22,645) provided usable data for meta‐analyses. Regarding relapse prevention, the vast majority of the 31 included treatments outperformed placebo. Compared to placebo, “high” confidence in the results was found for (in descending order of effect magnitude) amisulpride‐oral (OS), olanzapine‐OS, aripiprazole‐LAI, olanzapine‐LAI, aripiprazole‐OS, paliperidone‐OS, and ziprasidone‐OS. “Moderate” confidence in the results was found for paliperidone‐LAI 1‐monthly, iloperidone‐OS, fluphenazine‐OS, brexpiprazole‐OS, paliperidone‐LAI 1‐monthly, asenapine‐OS, haloperidol‐OS, quetiapine‐OS, cariprazine‐OS, and lurasidone‐OS. Regarding tolerability, none of the antipsychotics was significantly worse than placebo, but confidence was poor, with only aripiprazole (both LAI and OS) showing “moderate” confidence levels. Based on these findings, olanzapine, aripiprazole and paliperidone are the best choices for the maintenance treatment of schizophrenia‐spectrum disorders, considering that both LAI and oral formulations of these antipsychotics are among the best‐performing treatments and have the highest confidence of evidence for relapse prevention. This finding is of particular relevance for low‐ and middle‐income countries and constrained‐resource settings, where few medications may be selected. Results from this network meta‐analysis can inform clinical guidelines and national and international drug regulation policies.

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International FTD-Genetics Consortium (IFGC), the German Frontotemporal Lobar Degeneration (FTLD) Consortium, and the PRONIA Consortium IMPORTANCE The behavioral and cognitive symptoms of severe psychotic disorders overlap with those seen in dementia. However, shared brain alterations remain disputed, and their relevance for patients in at-risk disease stages has not been explored so far.OBJECTIVE To use machine learning to compare the expression of structural magnetic resonance imaging (MRI) patterns of behavioral-variant frontotemporal dementia (bvFTD), Alzheimer disease (AD), and schizophrenia; estimate predictability in patients with bvFTD and schizophrenia based on sociodemographic, clinical, and biological data; and examine prognostic value, genetic underpinnings, and progression in patients with clinical high-risk (CHR) states for psychosis or recent-onset depression (ROD). DESIGN, SETTING, AND PARTICIPANTS This study included 1870 individuals from 5 cohorts, including (1) patients with bvFTD (n = 108), established AD (n = 44), mild cognitive impairment or early-stage AD (n = 96), schizophrenia (n = 157), or major depression (n = 102) to derive and compare diagnostic patterns and (2) patients with CHR (n = 160) or ROD (n = 161) to test patterns' prognostic relevance and progression. Healthy individuals (n = 1042) were used for age-related and cohort-related data calibration.

Objective: Olanzapine is an atypical antipsychotic that is widely used in the treatment of schizophrenia and has shown some degree of efficacy on negative and cognitive symptoms. We aimed to review the effects of olanzapine treatment on brain regions that are directly involved in cognitive and emotional processing.Methods: We used the PubMed database to perform a bibliographic search on functional magnetic resonance imaging studies that investigated the effects of olanzapine treatment on neural activity in patients with schizophrenia during cognitive and emotional tasks.Results: Despite the high variability of tasks and analysis methods employed, the weight of the evidence was consistent with the hypothesis that olanzapine treatment is associated with a normalization of brain activity in schizophrenia. Distinctive functional changes were found in frontal cortex and cingulate cortex activity during both cognitive and emotional tasks. During emotional processing, olanzapine treatment seems to specifically regulate the activity of the striatum and limbic system. Conclusions:The results of the reviewed studies suggest that in patients with schizophrenia, olanzapine treatment might lead to a more physiological brain activity coupled with regulation of dopamine release. Future studies should further corroborate these hypotheses using larger samples and homogeneous experimental tasks.

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

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Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.