The purpose of this study is to identify the latent profiles of Chinese adolescents' family (parent-adolescent and sibling) relationships prior to and during the COVID-19 pandemic, as well as associations between those profiles and adolescents' emotional and behavioral responses. A total of 2,305 adolescents from China aged between 10 and 18 years completed measures of parent-adolescent relationships, sibling relationships, and emotional and behavioral responses during the pandemic. Four profiles of family relationships were identified via latent profile analysis and categorized as Cohesive-Decline, Mild-Decline, Conflictual-Stable, and Indifferent-Stable. Adolescents with a Conflictual-Stable profile reported more emotional and behavioral responses compared to the other profiles. In contrast, adolescents with a Cohesive-Decline profile exhibited fewer emotional responses compared to the other profiles. Adolescents with a Mild-Decline profile had fewer emotional responses than those with an Indifferent-Stable profile. These results shed light on the patterns and consequences of family relationships during the COVID-19 pandemic and have substantial implications for interventions involving family relationships in the context of regular epidemic prevention and control.

OBJECTIVE: In a previous epidemiological study, we reported on the ascertainment and outcomes of clinical high risk (CHR) individuals at the Shanghai Mental Health Center (SMHC, 2011 cohort). The current study compares demographic and clinical characteristics, including conversion rates, of this sample with a subsequently recruited, independent CHR sample and with published data from western samples. METHOD: A new sample of 100 CHR subjects (2013 cohort) was selected based on screening and semi-structured interviews. Both studies used the Structured Interview for Prodromal Syndromes (SIPS) for CHR assessment and conducted a naturalistic two-year follow-up. The two cohorts were compared on conversion rates, demographic and clinical characteristics, psychosis risk symptoms, and risk factors for psychotic conversion. RESULTS: Ninety one (91%) of the 2013 cohort subjects completed the clinical two-year follow-up and 25 (27.5% of the 91) converted to a psychotic disorder over the follow-up period. A comparison of conversions to full psychosis between the 2013 and the 2011 cohorts showed no significant difference in time to conversion (Pairwise comparison: χ2=0.3, p=0.562). Both cohort studies showed that CHR subjects with more severe clinical symptoms at baseline and decline in functioning were more likely to convert to psychosis. CONCLUSIONS: Conversion rates in this new, independent Chinese sample are similar to those reported in non-Chinese samples and to the 2011 cohort. Future research is needed to examine whether the implementation of early intervention for CHR/prodromal symptoms reduces the risk of psychosis and decreases the conversion rate.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.