Umbrella Review: Atlas of the Meta-Analytical Evidence of Early-Onset Psychosis

OBJECTIVE

Early-onset psychosis (EOP) refers to the development of psychosis before the age of 18 years. We aimed to summarize, for the first time, the meta-analytical evidence in the field of this vulnerable population and to provide evidence-based recommendations.

METHOD

We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant, pre-registered (PROSPERO: CRD42022350868) systematic review of several databases and registers to identify meta-analyses of studies conducted in EOP individuals to conduct an umbrella review. Literature search, screening, data extraction, and quality assessment were carried out independently. Results were narratively reported, clustered across core domains. Quality assessment was performed with the Assessment of Multiple Systematic Reviews-2 (AMSTAR-2) tool.

RESULTS

A total of 30 meta-analyses were included (373 individual studies, 25,983 participants, mean age 15.1 years, 38.3% female). Individuals with EOP showed more cognitive impairments compared with controls and individuals with adult/late-onset psychosis. Abnormalities were observed meta-analytically in neuroimaging markers but not in oxidative stress and inflammatory response markers. In all, 60.1% of EOP individuals had a poor prognosis. Clozapine was the antipsychotic with the highest efficacy for overall, positive, and negative symptoms. Tolerance to medication varied among the evaluated antipsychotics. The risk of discontinuation of antipsychotics for any reason or side effects was low or equal compared to placebo.

CONCLUSION

EOP is associated with cognitive impairment, involuntary admissions, and poor prognosis. Antipsychotics can be efficacious in EOP, but tolerability and safety need to be taken into consideration. Clozapine should be considered in EOP individuals who are resistant to 2 non-clozapine antipsychotics. Further meta-analytical research is needed on response to psychological interventions and other prognostic factors.

STUDY PREREGISTRATION INFORMATION

Early Onset Psychosis: Umbrella Review on Diagnosis, Prognosis and Treatment factors; https://www.crd.york.ac.uk/PROSPERO/; CRD42022350868.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk

Background

Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects.

Methods

We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data.

Results

Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks.

Conclusions

Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.