An augmented aging process in brain white matter in HIV |
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Authors: | Taylor Kuhn Tobias Kaufmann Nhat Trung Doan Lars T. Westlye Jacob Jones Rodolfo A. Nunez Susan Y. Bookheimer Elyse J. Singer Charles H. Hinkin April D. Thames |
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Affiliation: | 1. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, 740 Westwood Plaza, C8‐746, Los Angeles, California;2. Veterans Association Greater Los Angeles Healthcare Center, 11301 Wilshire Blvd, Los Angeles, California;3. NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway;4. Department of Psychology, University of Oslo, Oslo, Norway;5. Department of Cognitive Psychology, Tennenbaum Center for the Biology of Creativity, University of California, Los Angeles, 635 Charles E Young Dr. S, 260‐M, Los Angeles, California;6. Department of Neurology, University of California, Los Angeles, 710 Westwood Plaza, Los Angeles, California;7. Department of Psychology, University of Southern California, 3620 S. McClintock Avenue, Los Angeles, California |
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Abstract: | Objective HIV infection and aging are both associated with neurodegeneration. However, whether the aging process alone or other factors associated with advanced age account for the progression of neurodegeneration in the aging HIV‐positive (HIV+) population remains unclear. Methods: HIV+ (n = 70) and HIV‐negative (HIV?, n = 34) participants underwent diffusion tensor imaging (DTI) and metrics of microstructural properties were extracted from regions of interest (ROIs). A support vector regression model was trained on two independent datasets of healthy adults across the adult life‐span (n = 765, Cam‐CAN = 588; UiO = 177) to predict participant age from DTI metrics, and applied to the HIV dataset. Predicted brain age gap (BAG) was computed as the difference between predicted age and chronological age, and statistically compared between HIV groups. Regressions assessed the relationship between BAG and HIV severity/medical comorbidities. Finally, correlation analyses tested for associations between BAG and cognitive performance. Results: BAG was significantly higher in the HIV+ group than the HIV? group F (1, 103) = 12.408, p = .001). HIV RNA viral load was significantly associated with BAG, particularly in older HIV+ individuals (R2 = 0.29, F(7, 70) = 2.66, p = .021). Further, BAG was negatively correlated with domain‐level cognitive function (learning: r = ?0.26, p = .008; memory: r = ?0.21, p = .034). Conclusions: HIV infection is associated with augmented white matter aging, and greater brain aging is associated with worse cognitive performance in multiple domains. |
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Keywords: | cognitive aging DTI HIV machine learning neuropsychology/behavior |
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