Network Specificity in Predicting Childhood Trauma Characteristics Using Effective Connectivity

Area of Science:

• Neuroscience
• Psychiatry
• Brain Imaging

Background:

• Childhood maltreatment (CM) is a significant stressor affecting brain development.
• Previous research focused on functional connectivity, leaving dynamic effective connectivity (EC) less understood.

Purpose of the Study:

• To investigate the relationship between brain effective connectivity (EC) and childhood maltreatment severity.
• To identify specific brain networks whose EC patterns can predict CM severity.

Main Methods:

• Resting-state fMRI data from 215 adults were analyzed using dynamic causal modeling for whole-brain EC estimation.
• Regression models, including LASSO, were used to predict Childhood Trauma Questionnaire (CTQ) scores based on EC features.
• EC features were selected based on correlation thresholds (5%, 10%, 20%) and validated.

Main Results:

• Whole-brain EC showed a marginal association with predicting CM severity (CTQ scores).
• Effective connectivity within the default mode network (DMN) significantly predicted CTQ scores.
• DMN-specific EC features consistently showed predictive power across different selection thresholds.

Conclusions:

• Brain effective connectivity can reflect individual differences in childhood maltreatment severity.
• The default mode network (DMN) emerges as a key predictor in the context of CM.