Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia.

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia.

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.

Weight changes following treatment with aripiprazole, risperidone and olanzapine: A 12-month study of first-episode schizophrenia patients in China.

OBJECTIVES: This study aimed to assess weight changes following antipsychotic treatment in first-episode schizophrenia (FES) patients and make a comparison of aripiprazole, risperidone and olanzapine. Predictors for long-term clinically relevant weight gain (CRW, ≥7%) were examined. METHODS: We carried out a second analysis of data from the Chinese First-Episode Schizophrenia Trial. Repeated measures general linear model (GLM) statistics were used to compare body weight at each follow-up point (month of 1, 2, 3, 6, 9and 12). Logistic regression models were constructed to evaluate possible predictors for CRW. RESULTS: Body weight increased with an average rate of 0.93 % per month, with the fastest growth rate occurring in first 3 months. CRW was observed in 79 % of patients. Participants from olanzapine group showed significantly higher weight gain than risperidone group and aripiprozole group. Repeated measures GLM revealed a significant main effect of time (p < 0.001) and asignificant time*group interaction was revealed (p < 0.001), while the between-subject group effect was not statistically significant (p = 0.272). Multivariate logistic regressionmodel showed that with smaller baseline BMI (OR = 1.33, p < 0.001), with a family history of mental disorder (OR = 5.08, p = 0.004), receiving olanzapine (OR = 2.35, p = 0.001), and CRW at first-month (OR = 4.29, p = 0.032) were independent predictors for first-year CRW. CONCLUSION: Antipsychotics are associated with a clinically significant weight gain in FES patients, which occurs mostly in first 3 months. Aripiprazole might not be an ideal choice in terms of long-term metabolic side-effects. Early and close metabolic monitoring should accompany antipsychotic prescription.

Factors associated with depression among HIV/AIDS children in China.

BACKGROUND: Depression in HIV/AIDS children not only worsens the progression and outcome of illness, but also impacts their quality of life, having a negative influence on society. The present study was conducted from a psychosocial perspective, considering children's social desirability, cognitive emotion regulation, and perceived social support to identify the factors influencing depression in HIV-infected children in China. METHODS: Participants were 155 children aged 8-18 years who were eligible to participate in this study assessing depression and associated risk factors using the Children's Depression Inventory, Cognitive Emotion Regulation Questionnaire, Multidimensional Scale of Perceived Social Support, and Children's Social Desirability scale. Hierarchical linear regression analysis was conducted to model the effects of social desirability, perceived social support, and cognitive emotion regulation on depression in HIV/AIDS children. RESULTS: Statistically significant linear relationships were found among social desirability, perceived social support, partial dimensions of cognitive emotion regulation, and children's depression scores. Perceived social support, planning and positive reappraisal were negatively related to the depression. Conversely, social desirability, catastrophizing and other-blame were positively associated with the depression. Linear regression analysis indicated that children's social desirability, perceived social support, and one dimension of cognitive emotion regulation (catastrophizing) were found to significantly predict depression. CONCLUSIONS: Psychosocial factors have an important influence on the depression experienced by HIV-infected children. Interventions from personal subjective psychosocial to reduce depression in HIV-infected children in China are warranted.