Chunk #41 — HOW DO NEURAL SIGNATURES ASSOCIATED WITH AUD HELP ELUCIDATE THE ROLE OF BRAIN FUNCTION IN THE RISK AND CONSEQUENCES OF ALCOHOL USE AND AUD ACROSS THE LIFESPAN? — How do these multi‐modal risk and protective factors fit together to influence the development and course of AUD?
events could all be used to classify individuals who would develop alcohol induced memory problems 20 years later. 31 More recently, COGA has used ML algorithms to predict the difference in AUD recovery status, identifying several discriminative features, including PGS related to alcohol use, personality and psychopathology, psychosocial factors and electrophysiological indicators including lower default mode network and fusiform connectivity and higher insula connectivity 137 (Figure S3). Taken together, these ML findings highlight the strength of the multidomain data and analytical expertise in COGA and provide examples of how this information can be synthesized across modalities to identify risk factors and improve prediction of the development of AUD, and patterns of persistence, resistance and recovery from AUD, across the lifespan.
