Neurobiology of Alcohol and Nicotine Co-Addiction

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William Hoffman, PhD, MD, PI: Neurobiology of Alcohol and Nicotine Co-Addiction OBJECTIVE: This proposal, Neurobiology of Alcohol and Nicotine Co-Addiction addresses the critical absence of information about the neurobiology of recovery from Alcohol Use Disorder (AUD) in alcohol (EtOH) and nicotine (NIC) using veterans. AUD and nicotine use disorder (NUD), almost entirely cigarette smoking, are the most commonly abused (non-prescription) substances in the U.S. Co-addiction is particularly high in military veterans. Although nationwide estimates peg the rate of AUD/NUD co-addiction at 80%, the Substance Abuse Treatment Program (SATP) at the Veterans Affairs Portland Health Care System (VAPORHCS) finds that 90% of veterans treated for Alcohol Use Disorder (AUD) also meet criteria for Nicotine Use Disorder (NUD). PLAN: SA 1: Test a dual network model of alcohol and nicotine co-addiction via contrast of multiple neural aspects of AUD, NUD, NAUD and CS: a) task based (PDD) and stress modulated cue induced craving, b) volumetric estimates of cortical density (voxel-based morphometry [VBM], c) anatomical (diffusion tensor imaging [DTI] and d) resting state functional connectivity. SA 2: Develop a machine learning model that integrates behavioral, task and resting state functional activation, volumetric data and structural connectivity that a) differentiates the four groups and b) predicts treatment outcome at 3 months. METHODS: Four subject groups (Alcohol alone [AUD] alcohol plus smoking [NUD/AUD = NAUD], subjects who smoke [NUD] and never addicted controls [CS]) will be recruited from the Portland VA Health Care System (VAPORHCS). All will undergo a comprehensive evaluation at baseline including neuroimaging, cognitive testing, careful demographic and substance use history and laboratory evaluation. The AUD and AUD/NUD groups will be followed and re-evaluated with the entire battery at three months with monthly appointments to monitor progress. We hypothesize that a support vector machine learning algorithm will be able to use the measures to classify subjects as AUD, NUD both or neither and that the algorithm will predict outcome (sobriety or relapse) at 3 months. RELEVANCE TO THE VA MISSION: AUD and cigarette smoking are the most common addictive disorders in veterans and co-addiction is the rule. This proposal will disentangle the neurobiological correlates of single and co-addiction to these substances and develop a model that can help predict treatment response. Key words: Alcohol, nicotine, co-addiction, outcome