Machine learning to distinguish HAND from Alzheimer's disease in HIV over age 60

This study will use a new approach that leverages computational machine learning with inputs from structural imaging, neuropsychological testing, motor examination, and affective/behavioral assessments to determine the factors that most accurately discriminate HIV-associated cognitive dsyfunction from the Mild Cognitive Impairment stage of AD (MCI-AD) in individuals over age 60. The long-term goal of this work is to inform clinical guidelines; thus, the modalities examined in this study are readily available in clinical care.