Polygenic Risk Score for Schizophrenia and Face-Processing Network in Young Adulthood

Theory of mind in schizophrenia through a clinical liability approach: a sib-pair study

Background

Consistent findings indicate that Theory of Mind (ToM) is impaired in schizophrenia (SZ). To investigate whether such deficits are trait- or state-dependent, we investigated if ToM is modified by clinical liability markers (such as basic symptoms and psychotic-like experiences), focusing on the analysis of unaffected siblings of individuals diagnosed with SZ.

Methods

The study included a total of 65 participants: 38 patients diagnosed with a schizophrenia-spectrum disorder and 27 healthy siblings. ToM was assessed using the Hinting Task (HT), Basic symptoms with The Frankfurt Complaint Questionnaire (FCQ), Psychotic-like-experiences with the Community Assessment of Psychic Experiences (CAPE) and Family history with the Family Interview for Genetic Studies.

Results

First, a comparison of HT performance between patients and siblings (linear mixed model adjusted for age, sex and Intelligence Quotient (IQ)) showed that patients presented lower scores than siblings (p = 0.022). These differences did not remain significant after adjusting for clinical vulnerability markers. Second, within siblings, linear regression analyses (adjusted for age, sex, IQ and family history) showed that higher FCQ Depressiveness and CAPE negative scores were related to poorer ToM performance (p = 0.007 and p = 0.032, respectively).

Conclusion

Our findings suggest that clinical liability markers are valuable for delineating variations in ToM capabilities within healthy individuals. Moreover, our results indicate that ToM deficits are not solely linked to SZ but also extend to its clinical vulnerability, suggesting that ToM could serve as an endophenotypic marker. This implies that ToM could help distinguish particularly susceptible individuals from a population at risk, such as those with a genetic predisposition (siblings).

Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review

BACKGROUND AND HYPOTHESIS

Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology.

STUDY DESIGN

In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools.

STUDY RESULTS

In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations.

CONCLUSIONS

Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.

Is it possible to stage schizophrenia? A systematic review

INTRODUCTION

A staging model is a clinical tool used to define the development of a disease over time. In schizophrenia, authors have proposed different theoretical staging models of increasing complexity. Therefore, the aims of our study were to provide an updated and critical view of the proposed clinical staging models for schizophrenia and to review the empirical data that support them.

METHODS

Systematic literature review following PRISMA guidelines. From the PubMed database and backward reference search, a total of 141 records were retrieved, but only 20 were selected according to the inclusion criteria: (a) available in English; (b) participants with schizophrenia ≥ 18 years; and (c) theoretical and empirical research studies intended to develop, validate, and/or improve staging models of schizophrenia.

RESULTS

Different clinical staging models for schizophrenia were identified, information about the proposed stages was tabulated and presented in the Results section (Tables 1, 2). Most of which include neuroimaging, functioning, and psychopathology, but only two models add objective biomarkers and none include patient point of view. However, few models have been psychometrically tested or used small samples and thus have been validated only partially. In addition, five studies proposed therapeutic interventions according to the stage of the disorder from a theoretical point of view.

DISCUSSION

In conclusion, it is possible to stage schizophrenia, but the models developed have several limitations. Empirical validation and inclusion of more specific biomarkers and measures of other life areas affected by schizophrenia could help in the development of more valid models.

Functional connectome-wide associations of schizophrenia polygenic risk

Schizophrenia is a highly heritable mental disorder characterized by functional dysconnectivity across the brain. However, the relationships between polygenic risk factors and connectome-wide neural mechanisms are unclear. Here, combining genetic and multiparadigm fMRI data of 623 healthy Caucasian adults drawn from the Human Connectome Project, we found that higher schizophrenia polygenic risk scores were significantly correlated with lower functional connectivity in a large-scale brain network primarily encompassing the visual system, default-mode system, and frontoparietal system. Such correlation was robustly observed across multiple fMRI paradigms, suggesting a brain-state-independent neural phenotype underlying individual genetic liability to schizophrenia. Moreover, using an independent clinical dataset acquired from the Consortium for Neuropsychiatric Phenomics, we further demonstrated that the connectivity of the identified network was reduced in patients with schizophrenia and significantly correlated with general cognitive ability. These findings provide the first evidence for connectome-wide associations of schizophrenia polygenic risk at the systems level and suggest that disrupted integration of sensori-cognitive information may be a hallmark of genetic effects on the brain that contributes to the pathogenesis of schizophrenia.

Markers of Psychosis Risk in the General Population

The categorical approach to defining schizophrenia spectrum disorders requires meeting established criteria. To advance early identification and intervention in young people, the field has progressed to studying help-seeking individuals who are at clinical high risk based on subthreshold psychosis spectrum symptoms, and criteria have been articulated for qualifying individuals as at risk. A broader dimensional examination of psychosis has been applied to population-based studies on non-help seekers. This review highlights the ascertainment and assessment approaches to such population-based studies. Most studies are cross-sectional and rely on questionnaires with limited overlap of tools. However, several consistent findings emerge on symptoms, neurocognitive deficits, and neuroimaging parameters and other biomarkers associated with emergence and persistence of psychotic features. The findings are consistent with the literature on abnormalities associated with schizophrenia, including the presence of neurocognitive deficits; abnormalities in brain structure, function, and connectivity that are related to distress; impairment; and functional outcome. These findings support the validity of studying psychosis experiences during development in a way that can chart the emergence of psychosis in the context of general psychopathology. Such studies are necessary for establishing developmental trajectories that characterize this emergence and for identifying risk and resilience biomarkers moderating or modulating the full range of schizophrenia-related manifestations. More community-based studies are needed, with better standardization and harmonization of measures and incorporating longitudinal follow-up, to establish mechanistic links between cellular-molecular aberrations and specific manifestations of psychosis as envisioned by the precision medicine agenda.

Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment-resistant and are eligible for clozapine treatment. Treatment-resistant patients show a more severe course of the disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date, findings do not allow the disentanglement of its polygenic nature. More recent studies implemented polygenic risk score, gene-based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.

Psychiatric research in the Northern Finland Birth Cohort 1986 - a systematic review

The Northern Finland Birth Cohort 1986 is a large population-based birth cohort, which aims to promote health and wellbeing of the population. In this paper, we systematically review the psychiatric research performed in the cohort until today, i.e. at the age of 32 years of the cohort (2018). We conducted a systematic literature search using the databases of PubMed and Scopus and complemented it with a manual search. We found a total of 94 articles, which were classified as examining ADHD, emotional and behavioural problems, psychosis risk or other studies relating to psychiatric subjects. The articles are mainly based on two large comprehensive follow-up studies of the cohort and several substudies. The studies have often used also nationwide register data. The studies have found several early predictors for the aforementioned psychiatric outcomes, such as problems at pregnancy and birth, family factors in childhood, physical inactivity and substance use in adolescence. There are also novel findings relating to brain imaging and cognition, for instance regarding familial risk of psychosis in relation to resting state functional MRI. The Northern Finland Birth Cohort 1986 has been utilised frequently in psychiatric research and future data collections are likely to lead to new scientifically important findings.

Abbreviations: attention deficit hyperactivity disorder (ADHD); magnetic resonance imaging (MRI)