In this context, the goal of the current study is to identify specific features of FC, neuropsychological, and impulsivity measures that contribute to a successful classification of AUD individuals from healthy controls using an RF algorithm. Based on findings from previous studies, we also expect that the RF method will prove highly useful to successfully extract salient features from all three domains (FC, neuropsychological, and impulsivity) to classify AUD individuals from unaffected controls. Since individuals with chronic AUD are known to have deficits in all three domains that are related among themselves and with AUD, we hypothesize that specific connections across the DMN regions, especially the prefrontal–parietal and prefrontal–hippocampal connections, along with particular subsets of neuropsychological and impulsivity features will contribute to AUD classification as revealed by the importance rankings of the RF parameters.
