The findings of the current study suggest that multidomain features drawn from the measures of brain connectivity, impulsivity, and neuropsychological tests can be successfully used in a machine learning framework to classify AUD individuals from healthy controls. In summary, our study revealed that the abstinent individuals with past AUD showed impaired brain connectivity across specific reward regions and also manifested relatively increased impulsivity and poor memory function. Evidence from the literature suggests that these anomalies may have been caused by neuroadaptation due to chronic drinking. Since treatment interventions intended to reverse these neuroadaptations show promise as potential therapeutic approaches for addiction [1], the findings of the current study may have important clinical implications. We suggest that future studies should further characterize these neural and behavioral abnormalities at multiple levels and groups, such as gender, race/ethnicity, educational attainment, socio-economic status, genomic liability, and drinking patterns, so that the findings may contribute towards personalized medicine.
