Connectomics modeling of regional networks of white-matter fractional anisotropy to predict the severity of young adult drinking.

Abstract

Background: Alcohol use impacts brain structure, including white matter integrity, which can be quantified by fractional anisotropy (FA) in diffusion tensor imaging (DTI). This study explored the relationship between the severity of alcohol consumption and white matter FA changes, and its sex differences, in young adults, using data from the Human Connectome Project.

Methods: We analyzed DTI data from 949 participants (491 females) and used principal component analysis (PCA) of 15 drinking metrics to quantify drinking severity. Connectome-based predictive modeling (CPM) was employed to predict the principal component of drinking severity from network FA values in a matrix of 116×116 regions. Mediation analyses were conducted to explore the interrelationships among networks identified by CPM, drinking severity, and rule-breaking behavior.

Results: Significant correlations were found between drinking severity and network FA values. Both men and women showed significant correlations between negative network connectivity and drinking severity (men: r=0.15, P=0.001; women: r=0.30, P<0.001). Sex differences were observed in the brain regions contributing to drinking severity predictions. Mediation analyses revealed significant inter-relationships between network features, drinking severity, and rule-breaking behavior.

Conclusions: The connectomics of white matter FA can predict the severity of alcohol consumption, and by incorporating brain network pathways, identify sex differences. This approach provides new clues to the biological basis of alcohol abuse and evaluates how these regions interact in broader brain networks for understanding alcohol misuse and its comorbidities.