Additional Insights into the Relationship Between Brain Network Architecture and Susceptibility and Resilience to the Psychiatric Sequelae of Childhood Maltreatment

对大脑网络结构与童年虐待精神后遗症的易感性和恢复力之间关系的进一步见解

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Abstract

Childhood maltreatment is associated with increased risk for psychiatric and substance use disorders. However, some maltreated individuals appear resilient to these consequences while manifesting the same array of brain changes as maltreated individuals with psychopathology. Hence, a critical issue has been to identify compensatory brain alterations in these resilient individuals. We recently reported that maltreatment is associated with a more vulnerable structural brain network architecture. Resilient individuals have this same vulnerability but appeared to be able to effectively compensate due to reduced nodal efficiency (ability of a node to influence the global network) in 9 specific brain regions that moderate the relationship between maltreatment and psychopathology. Following up we now report that network vulnerability increases progressively during late adolescence to plateau at about 21 years of age, which may help to explain age of onset of psychopathology. Further, we found that network vulnerability was most significantly affected by parental verbal abuse between 16-18 years of age and number of types of maltreatment during childhood. Asymptomatic individuals with no history of psychopathology had more prominent alterations in nodal efficiency than asymptomatic individuals with prior history, who specifically showed reduced nodal efficiency in right amygdala and right subcallosal gyrus. Experiencing inadequate financial sufficiency during childhood increased risk of susceptibility versus resilience by 2.98-fold (95% CI 1.49-5.97, p = 0.002) after adjusting for differences in exposure to maltreatment. Interestingly, adequate-to-higher financial sufficiency appeared to be protective and was associated with reduced nodal efficiency in the right postcentral gyrus and subcallosal gyrus 'resilience' nodes.

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