This study focused on alpha EEGc, which is just one of several potentially informative EEG phenotypes. The number of EEG phenotypes available is so vast that future research would likely benefit from integrating machine learning and other methods for the management of big data (Golmohammadi, Harati Nejad Torbati, Lopez de Diego, Obeid, & Picone, 2019; Pawan & Dhiman, 2023) while maintaining a focus on clinical relevance when executing these techniques. In addition, studies that more comprehensively assess childhood trauma (e.g. repeated/prolonged trauma), maltreatment (e.g. neglect), and other stressful life experiences (e.g. discrimination, neighborhood violence) are needed to better understand the way that adverse experiences in childhood may impact neural connectivity and subsequent psychopathology. Another important future direction for this research is the integration of genetic and other biological factors (e.g. family history) associated with trauma, PTSD, and alcohol-related outcomes (e.g. consumption, problems, AUD) and EEG connectivity. Including these informative measures may shed further light on the shared neurobiological mechanisms of risk between PTSD and AUD, as well as increase our ability to better identify individuals who may benefit most from intervention.
