Background Persons with HIV (PWH) are characterized by altered brain structure and function. As they attain normal lifespans, it has become crucial to understand potential interactions between HIV and aging. However, it remains unclear how brain aging varies with viral load (VL). Methods In this study, we compare MRI biomarkers amongst PWH with undetectable VL (UVL; ≤50 genomic copies/ml; n=230), PWH with detectable VL (DVL; >50 copies/ml; n=93), and HIV uninfected (HIV-) controls (n=206). To quantify gray matter cerebral blood flow (CBF), we utilized arterial spin labeling. To measure structural aging, we used a publicly available deep learning algorithm to estimate brain age from T1-weighted MRI. Cognitive performance was measured using a neuropsychological battery covering five domains. Results Associations between age and CBF varied with VL. Older PWH with DVL had reduced CBF vs. PWH with UVL (p=0.02). Structurally predicted brain aging was accelerated in PWH vs. HIV- controls regardless of VL (p<0.001). Overall, PWH had impaired learning, executive function, psychomotor speed, and language compared to HIV- controls. Structural brain aging was associated with reduced psychomotor speed (p<0.001). Conclusions Brain aging in HIV is multifaceted. CBF depends on age and current VL, and is improved by medication adherence. By contrast, structural aging is an indicator of cognitive function and reflects serostatus rather than current VL.

Background: Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals. Setting: Machine learning analysis of highly dimensional risk features was performed on a clinical cohort of PLWH. Methods: Participants included 105 older (average age = 55.6) PLWH, with at least a 3-month history of combination antiretroviral therapy (median CD4 = 546). Predictors included demographics, HIV clinical markers, comorbid health conditions, cognition, and neuroimaging (ie, volumetrics, resting-state functional connectivity, and cerebral blood flow). Gradient-boosted multivariate regressions were implemented to establish linear and interactive classification models. Model performance was determined by sensitivity/specificity (F1 score) with 5-fold cross validation. Results: The linear gradient-boosted multivariate regression classifier included lower current CD4 count, lower psychomotor performance, and multiple neuroimaging indices (volumes, network connectivity, and blood flow) in visual and motor brain systems (F1 score = 71%; precision = 84%; and sensitivity = 66%). The interactive model identified novel synergies between neuroimaging features, female sex, symptoms of depression, and current CD4 count. Conclusions: Data-driven algorithms built from highly dimensional clinical and brain imaging features implicate disruption to the visuomotor system in older PLWH designated as frail individuals. Interactions between lower CD4 count, female sex, depressive symptoms, and neuroimaging features suggest potentiation of risk mechanisms. Longitudinal data-driven studies are needed to guide clinical strategies capable of preventing the development of frailty as PLWH reach advanced age.

Background: Deep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). Feature extraction techniques identified brain regions that were the strongest predictors. Setting: Virologically suppressed (<50 copies/mL) PLWH (n = 125) on combination antiretroviral therapy were enrolled. Participants averaged 51.4 (11.4) years of age and 13.7 (2.8) years of education. Participants were administered a neuropsychological battery, assessed for frailty, and completed structural neuroimaging. Methods: Deep neural network (DNN) models were trained to classify PLWH as cognitively unimpaired or impaired based on neuropsychological tests (Hopkins Verbal Learning Test-Revised and Brief Visuospatial Memory Test-Revised, Trail making, Letter-Number Sequencing, Verbal Fluency, and Color Word Interference), as well as frail, prefrail, or nonfrail based on the Fried phenotype criteria (at least 3 of the following 5: weight loss, physical inactivity, exhaustion, grip strength, walking time). Results: DNNs classified individuals with cognitive impairment in the learning, memory, and executive domains with 82%–86% accuracy (0.81–0.87 AUC). Our model classified nonfrail, prefrail, and frail PLWH with 75% accuracy. The strongest predictors of cognitive impairment were cortical (parietal, occipital, and temporal) and subcortical (amygdala, caudate, and hippocampus) regions, whereas the strongest predictors of frailty were subcortical (amygdala, caudate, hippocampus, thalamus, pallidum, and cerebellum). Conclusions: DNN models achieved high accuracy in classifying cognitive impairment and frailty status in PLWH. Feature selection algorithms identified predictive regions in each domain and identified overlapping regions between cognitive impairment and frailty. Our results suggest frailty in HIV is primarily subcortical, whereas cognitive impairment in HIV involves subcortical and cortical brain regions.

Here we computationally investigate how encumbering the hand could alter predictions made by the minimum torque change (MTC) and minimum endpoint variance hypotheses (MEPV) of movement planning. After minutes of training, people have made arm trajectories in a robot-generated viscous force field that were similar to previous baseline trajectories without the force field. We simulate the human arm interacting with this viscous load. We found that the viscous forces clearly differentiated MTC and MEPV predictions from both minimum-jerk predictions and from human behavior. We conclude that learned behavior in the viscous environment could arise from minimizing kinematic costs but could not arise from a minimization of either torque change or endpoint variance.