Aim: Schizophrenia (SZ) is a severe psychiatric disorder caused by the interaction of genetic and environmental factors. Although somatic mutations that occur in the brain after fertilization may play an important role in the cause of SZ, their frequencies and patterns in the brains of patients and related animal models have not been well studied. This study aimed to find somatic mutations related to the pathophysiology of SZ.
Methods: We performed whole-exome sequencing (WES) of neuronal and nonneuronal nuclei isolated from the postmortem prefrontal cortex of patients with SZ (n = 10) and controls (n = 10). After detecting somatic mutations, we explored the similarities and differences in shared common mutations between two cell types and cell type-specific mutations. We also performed WES of prefrontal cortex samples from an animal model of SZ based on maternal immune activation (MIA) and explored the possible impact of MIA on the patterns of somatic mutations.
Results: We did not find quantitative differences in somatic mutations but found higher variant allele fractions of neuron-specific mutations in patients with SZ. In the mouse model, we found a larger variation in the number of somatic mutations in the offspring of MIA mice, with the occurrence of somatic mutations in neurodevelopment-related genes.
Conclusion: Somatic mutations occurring at an earlier stage of brain cell differentiation toward neurons may be important for the cause of SZ. MIA may affect somatic mutation profiles in the brain.
Aim: Recovery from stroke is adversely affected by neuropsychiatric complications, cognitive impairment, and functional disability. Better knowledge of their mutual relationships is required to inform effective interventions. Network theory enables the conceptualization of symptoms and impairments as dynamic and mutually interacting systems. We aimed to identify interactions of poststroke complications using network analysis in diverse stroke samples.
Methods: Data from 2185 patients were sourced from member studies of STROKOG (Stroke and Cognition Consortium), an international collaboration of stroke studies. Networks were generated for each cohort, whereby nodes represented neuropsychiatric symptoms, cognitive deficits, and disabilities on activities of daily living. Edges characterized associations between them. Centrality measures were used to identify hub items.
Results: Across cohorts, a single network of interrelated poststroke complications emerged. Networks exhibited dissociable depression, apathy, fatigue, cognitive impairment, and functional disability modules. Worry was the most central symptom across cohorts, irrespective of the depression scale used. Items relating to activities of daily living were also highly central nodes. Follow-up analysis in two studies revealed that individuals who worried had more densely connected networks than those free of worry (CASPER [Cognition and Affect after Stroke: Prospective Evaluation of Risks] study: S = 9.72, P = 0.038; SSS [Sydney Stroke Study]: S = 13.56, P = 0.069).
Conclusion: Neuropsychiatric symptoms are highly interconnected with cognitive deficits and functional disabilities resulting from stroke. Given their central position and high level of connectedness, worry and activities of daily living have the potential to drive multimorbidity and mutual reinforcement between domains of poststroke complications. Targeting these factors early after stroke may have benefits that extend to other complications, leading to better stroke outcomes.