Identifying Patterns of Cognitive, Motor, and Brain Structural Abnormalities Differentiating Alcohol Use Disorder with and without HIV Infection Comorbidity

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SUMMARY Alcohol Use Disorder (AUD) occurs in the largest segment of individuals in the U.S. who are dependent on a substance. The co-occurrence of AUD in individuals with human immunodeficiency virus (HIV) infection is high, occurring at twice the rate as occurs in the general population. AUD and HIV infection each are responsible for disruption of brain structural integrity and cognitive and motor impairments, affect some different and some overlapping neural systems, but can also exacerbate the untoward effects on selective systems through synergistic or additive processes. The goal of this research project is to develop novel machine learning methods to differentiate the compounding factors and effects of these two disorders involving brain to improve the mechanistic and dynamic understanding of HIV/AUD comorbidity effects on the brain. Efficient study of the untoward effects associated with the comorbidity of AUD and HIV on brain morphology requires identifying differences across multiple diagnostic groupings, i.e., healthy controls (CTRL), HIV-negative alcoholics (AUD), HIV-positive without alcohol dependency (HIV), and HIV-positive with alcohol dependency (HIV/AUD). Testing inferences across multiple diagnostic groupings of complex disorders commonly yields inconclusive or conflicting findings when done by conventional, univariate, cross-sectional study designs, which are constructed to model two cohorts at a time and hold "nuisance" variables constant with little power to include multiple factors comprising the complexity and dynamism that may well be relevant for distinguishing the primary disorders. Herein, I propose to develop robust and reliable machine learning technology that can identify patterns from longitudinal MRI data unique to each cohort. This type of data-driven method is able to analyze all data points concurrently and thus provide alternative approaches to conventional methods, which focus on correlating subsets of data guided by expert domain knowledge. I envision that this project will expand my understanding of the neuropsychiatry of AUD and HIV and will also result in extending a mechanistic understanding of neurofactors relevant to HIV/AUD comorbidity. Improving the mechanistic understanding of HIV/AUD comorbidity and diagnostic differences may enhance physician and caregiver awareness and aid clinicians in developing targeted therapeutic options for sustained symptomatic benefits.