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

ABSTRACT:

Background

The relationship between HIV infection, the functional organization of the brain, cognitive impairment, and aging remains poorly understood. Understanding disease progression over the life span is vital for the care of people living with HIV (PLWH).

Setting

Virologically suppressed PLWH (n = 297) on combination antiretroviral therapy and 1509 HIV-uninfected healthy controls were evaluated. PLWH were further classified as cognitively normal (CN) or cognitively impaired (CI) based on neuropsychological testing.

Methods

Feature selection identified resting-state networks (RSNs) that predicted HIV status and cognitive status within specific age bins (younger than 35 years, 35-55 years, and older than 55 years). Deep learning models generated voxelwise maps of R

SUBMITTER: Luckett PH 

PROVIDER: S-EPMC8556306 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature