Figure 1.
Study overview. Machine learning was used to generate the brain-age gap (BAG). Training data (left column) consisted of diffusion-weighted magnetic resonance imaging (MRI) scans from the Cambridge Center for Ageing and Neuroscience (CamCAN). Diffusion tensor metrics fractional anisotropy (FA) and mean diffusivity (MD) were calculated and used in training a Gaussian process regression machine learning model to estimate participant age from neuroimaging. Study participants (middle column) including persons with HIV (PWH) and HIV-negative controls underwent a comparable MRI protocol. Diffusion tensor metrics from these participants constituted testing data for the model. BAG, the difference between predicted and true age, was the output preserved for statistical analysis. Additionally, T1-weighted volumetric MRI (right column) from the same participants was used to generate an alternative BAG measure for multimodal comparison using DeepBrainNet, a pretrained publicly available neural network. This comparison was performed to test whether a novel metric of white matter microstructure is corroborated by more established morphological methods. Statistical analysis (bottom row) included 3 main parts. First, the primary hypothesis that brain aging is accelerated in PWH was tested by modeling the DTI-derived BAG as an age×viral load interaction in multiple linear regression. Second, the association between BAG and cognition was assessed. Finally, additional potential risk factors for accelerated brain aging including comorbidities were explored.