AUD is a multi-factorial disorder; therefore, it is important for the predictive models of alcohol-related neurocognitive outcomes, such as memory impairment, to include features from multiple domains, including polygenic risk scores (PRS) [35,36] and personality dimensions [36,37,38,39,40,41]. The identification of important variables that will reliably predict alcohol-related memory problems in older individuals may have important implications for preventive measures. Therefore, the aim of the current study is to understand and identify various features that may have predictive value in classifying individuals with memory problems. Specifically, the goal of the present study is to identify a set of multi-domain factors that can differentiate individuals with alcohol-related memory impairments from those without, using (i) resting-EEG-based functional connectivity measures of the default mode network as derived from eLORETA, (ii) PRS related to alcohol outcomes, (iii) personality and life experience measures derived from established questionnaires, and (iv) measures of alcohol consumption and associated health consequences from the recent follow-up interview. Identifying specific default mode network functional connections underlying alcohol-induced memory problems may be useful for early preventive measures and for brain-based treatment strategies such
