Network Specificity in Predicting Childhood Trauma Characteristics Using Effective Connectivity.

Abstract

Background: Childhood maltreatment (CM) has become one of the leading psychological stressors, adversely impacting brain development during adolescence and into adulthood. Although previous studies have extensively explored functional connectivity associated with CM, the dynamic interaction of brain effective connectivity (EC) is not well documented.

Methods: Resting-state functional magnetic resonance imaging data were collected from 215 adults with an assessment using the Childhood Trauma Questionnaire (CTQ). Whole-brain EC was estimated by regression dynamic causal modeling and subsequently down-resampled into seven networks. To predict CTQ total scores, repeated cross-validated ridge-regularized linear regression was employed, with whole-brain and network-specific EC features selected at thresholds of 5% of the strongest positive and negative correlations between EC and scores, as well as 10% and 20% thresholds. Additionally, a least absolute shrinkage and selection operator (LASSO)-regularized linear regression model was utilized as validation analysis.

Results: Our findings revealed that whole-brain EC showed a marginal association with predicting CTQ total scores, and EC within the default mode network (DMN) significantly predicted these scores. EC features from other networks did not yield significant predictive results. Notably, across varying feature selection thresholds, DMN features consistently demonstrated significant predictive power, comparable to results from LASSO-regularized predictions.

Conclusions: These findings suggested that brain EC can capture individual differences in CM severity, with the DMN potentially serving as an important predictor related to CM.