Modeling the Effects of HIV and Aging on Resting-State Networks using Machine Learning.

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Abstract

The relationship between HIV infection, the functional organization of the brain, cognitive impairment, and aging remains poorly understood. Understanding disease progression over the lifespan is vital for the care of people living with HIV (PLWH).Virologically suppressed PLWH (n=297) on combination antiretroviral therapy and 1509 controls were evaluated. PLWH were further classified as cognitively normal (CN) or impaired (CI) based on neuropsychological testing.Feature selection identified resting state networks (RSNs) that predicted HIV and cognitive status within specific age bins (< 35, 35-55, >55). Deep learning models generated voxelwise maps of RSNs to identify regional differences.Salience (SAL) and parietal memory networks (PMN) differentiated individuals by HIV status. When comparing controls to PLWH CN the PMN and SAL had the strongest predictive strength across all ages. When comparing controls to PLWH CI SAL, PMN, and frontal parietal (FPN) were the best predictors. When comparing PLWH CN to PLWH CI SAL, FPN, basal ganglia, and ventral attention were the strongest predictors. Only minor variability in predictive strength was observed with aging. Anatomically, differences in RSN topology occurred primarily in the dorsal and rostral lateral prefrontal cortex, cingulate, and caudate.Machine learning identified RSNs that classified individuals by HIV and cognitive status. PMN and SAL were sensitive for discriminating HIV status, with involvement of FPN occurring with cognitive impairment. Minor differences in RSN predictive strength were observed by age. These results suggest specific RSNs are affected by HIV, aging, and HIV associated cognitive impairment.Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.