Characterizing childhood trauma in individuals based on patterns of intrinsic brain connectivity.

Childhood maltreatment represents a strong psychological stressor that may lead to the development of later psychopathology as well as a heightened risk of health and social problems. Despite a surge of interest in examining behavioral, neurocognitive, and brain connectivity profiles sculpted by such early adversity over the past decades, little is known about the neurobiological substrates underpinning childhood maltreatment. Here, we aim to detect the effects of childhood maltreatment on whole-brain resting-state functional connectivity (RSFC) in a cohort of healthy adults and to explore whether such RSFC profiles can be used to predict the severity of childhood trauma in subjects based on a data-driven connectome-based predictive modeling (CPM). Resting-state functional MRI (rs-fMRI) data were acquired from 97 healthy adults, each of whom was assessed for childhood maltreatment levels using the Childhood Trauma Questionnaire-Short Form (CTQ-SF). CPM was used to examine the association between whole-brain RSFC and childhood maltreatment levels. The results showed that CPM was able to decode individual childhood maltreatment levels from RSFC across multiple neural systems including RSFC between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes contributing to the prediction model included the amygdala, prefrontal, and anterior cingulate regions that have been linked to childhood maltreatment. These results remained robust using different validation procedures. Our findings revealed that RSFC among multiple neural systems can be used to predict childhood maltreatment levels in individuals.