Relationships Between Viral Load, Neuroimaging, and NP in Persons Living With HIV.

BACKGROUND: This study examined whether recommended viral load (VL) classifications by the Department of Health and Human Services map onto changes in brain integrity observed in people living with HIV (PLWH). METHODS: Three hundred forty-nine PLWH on combination antiretroviral therapy meeting criteria for virologic suppression (VS) (VL ≤ 20 copies/mL; n = 206), "low-level viremia" (20-200 copies/mL; n = 63), or virologic failure (VF) (>200 copies/mL; n = 80) and 195 demographically similar HIV-negative controls were compared for cognition and brain volumes from 10 regions of interest that are sensitive to HIV. Changes in cognition and brain volumes were examined in a subset of PLWH (n = 132) who completed a follow-up evaluation (mean interval = 28 months) and had no change in treatment regimen. RESULTS: Significant differences in cognition and brain volumes were observed between the HIV-negative control and VS groups compared with those in the VF groups, with few differences observed between the 3 PLWH subgroups. Longitudinally, PLWH who continued to have VF exhibited a greater decline in cognition and brain volumes compared with PLWH who remained with VS. Observed longitudinal changes in cognition correlated with brain volume changes. CONCLUSION: PLWH with continued VF (consecutive VL measurements of >200 copies/mL) represent a cause for clinical concern and may benefit from change in treatment in addition to consideration of other potential etiologies of VF to reduce loss of brain integrity.

Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

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.

Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV.

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.